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This page was generated on 2024-02-22 11:37:59 -0500 (Thu, 22 Feb 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.2 Patched (2023-11-13 r85521) -- "Eye Holes" 4691
palomino4Windows Server 2022 Datacenterx644.3.2 (2023-10-31 ucrt) -- "Eye Holes" 4444
lconwaymacOS 12.7.1 Montereyx86_644.3.2 Patched (2023-11-01 r85457) -- "Eye Holes" 4465
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

Package 1372/2266HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
MungeSumstats 1.10.1  (landing page)
Alan Murphy
Snapshot Date: 2024-02-21 14:05:05 -0500 (Wed, 21 Feb 2024)
git_url: https://git.bioconductor.org/packages/MungeSumstats
git_branch: RELEASE_3_18
git_last_commit: 83ed5fb
git_last_commit_date: 2023-10-26 06:36:40 -0500 (Thu, 26 Oct 2023)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino4Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.1 Ventura / arm64see weekly results here

CHECK results for MungeSumstats on lconway


To the developers/maintainers of the MungeSumstats package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/MungeSumstats.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: MungeSumstats
Version: 1.10.1
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.10.1.tar.gz
StartedAt: 2024-02-21 21:46:52 -0500 (Wed, 21 Feb 2024)
EndedAt: 2024-02-21 22:07:57 -0500 (Wed, 21 Feb 2024)
EllapsedTime: 1264.7 seconds
RetCode: 0
Status:   OK  
CheckDir: MungeSumstats.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.10.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/MungeSumstats.Rcheck’
* using R version 4.3.2 Patched (2023-11-01 r85457)
* using platform: x86_64-apple-darwin20 (64-bit)
* R was compiled by
    Apple clang version 14.0.3 (clang-1403.0.22.14.1)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘MungeSumstats/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MungeSumstats’ version ‘1.10.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MungeSumstats’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                    user system elapsed
get_genome_builds 76.270  5.112  82.380
format_sumstats   67.370  3.978  72.248
liftover           3.431  0.054   5.454
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: OK


Installation output

MungeSumstats.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL MungeSumstats
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library’
* installing *source* package ‘MungeSumstats’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (MungeSumstats)

Tests output

MungeSumstats.Rcheck/tests/testthat.Rout


R version 4.3.2 Patched (2023-11-01 r85457) -- "Eye Holes"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(MungeSumstats)
> 
> test_check("MungeSumstats")
Collecting metadata from Open GWAS.
Filtering metadata by substring criteria.
Filtering metadata by sample/case/control/SNP size criteria.
Excluding sample/case/control size with NAs.
Found 3 GWAS datasets matching search criteria across:
   - 3 trait(s)
   - 1 population(s)
   - 2 category(ies)
   - 2 subcategory(ies)
   - 2 publication(s)
   - 2 consortia(ium)
   - 1 genome build(s)
Collecting metadata from Open GWAS.
Filtering metadata by substring criteria.
Found 49 GWAS datasets matching search criteria across:
   - 44 trait(s)
   - 4 population(s)
   - 2 category(ies)
   - 2 subcategory(ies)
   - 9 publication(s)
   - 5 consortia(ium)
   - 1 genome build(s)
Downloading VCF ==> /tmp/RtmpJeZB3s/ieu-a-298.vcf.gz
Downloading with download.file.
trying URL 'https://gwas.mrcieu.ac.uk/files/ieu-a-298/ieu-a-298.vcf.gz'
Content type 'application/gzip' length 234480 bytes (228 KB)
==================================================
downloaded 228 KB

Downloading VCF index ==> https://gwas.mrcieu.ac.uk/files/ieu-a-298/ieu-a-298.vcf.gz.tbi
Downloading with download.file.
trying URL 'https://gwas.mrcieu.ac.uk/files/ieu-a-298/ieu-a-298.vcf.gz.tbi'
Content type 'application/gzip' length 37803 bytes (36 KB)
==================================================
downloaded 36 KB

Processing 1 datasets from Open GWAS.

========== Processing dataset : a-fake-id ==========

Downloading VCF ==> /tmp/RtmpJeZB3s/a-fake-id.vcf.gz
Downloading with download.file.
trying URL 'https://gwas.mrcieu.ac.uk/files/a-fake-id/a-fake-id.vcf.gz'
Processing 1 datasets from Open GWAS.

========== Processing dataset : ieu-a-298 ==========

Using previously downloaded VCF.
Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/ieu-a-298/ieu-a-298.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f850123701.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f84b01824f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f850123701.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8a9cdb45.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f84b01824f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8a9cdb45.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.041 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84f7cc1b5.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f816c6b4a1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 48 seconds.
1 SNPs are non-biallelic. These will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/snp_bi_allelic.tsv.gz
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84f7cc1b5.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.863 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87efa396f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f816c6b4a1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f87efa396f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.405 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f874f458a6.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Found 1 Indels. These will be removed from the sumstats. 
WARNING If you want to keep Indels, set the drop_indel param to FALSE & rerun MungeSumstats::format_sumstats()
Writing in tabular format ==> /tmp/RtmpJeZB3s/indel.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81713c8c.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85c156847
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 92 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
Effect/frq column(s) relate to A1 in the inputted sumstats
Found direction from matchine reference genome - NOTE this assumes non-effect allele will macth the reference genome
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A2	A1	FRQ	BETA	SE	P	
Ensuring parameters comply with LDSC format.
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Ensuring all SNPs are on the reference genome.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
Checking for correct direction of A1 (reference) and A2 (alternative allele).
There are 47 SNPs where A1 doesn't match the reference genome.
These will be flipped with their effect columns.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Computing Z-score from P using formula: `sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE)`
Assigning N=1001 for all SNPs.
26 SNPs (28%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f81713c8c.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.837 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.82090 -0.019 0.003 1.794e-08
2: rs11210860     1 43982527      G      A 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      G      A 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.76310  0.017 0.003 1.797e-08
   IMPUTATION_SNP flipped         Z IMPUTATION_z_score_p     N
           <lgcl>  <lgcl>     <num>               <lgcl> <int>
1:             NA    TRUE -5.630777                 TRUE  1001
2:             NA      NA  6.335939                 TRUE  1001
3:             NA      NA  7.568968                 TRUE  1001
4:             NA    TRUE  5.630488                 TRUE  1001
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85fdffb81.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8383dc72f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N_CON	N_CAS	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N_CON	N_CAS	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Computing effective sample size using the LDSC method:
 Neff = (N_CAS+N_CON) * (N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))[(N_CAS+N_CON)==max(N_CAS+N_CON)]))
Computing sample size using the sum method:
 N = N_CAS + N_CON
Computing effective sample size using the GIANT method:
 Neff = 2 / (1/N_CAS + 1/N_CON)
Computing effective sample size using the METAL method:
 Neff = 4 / (1/N_CAS + 1/N_CON)
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85fdffb81.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P N_CON
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08   100
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10   100
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14   100
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08   100
   N_CAS Neff_ldsc     N Neff_giant Neff_metal
   <int>     <int> <int>      <int>      <int>
1:   120       220   220        109        218
2:   120       220   220        109        218
3:   120       220   220        109        218
4:   120       220   220        109        218
Returning path to saved data.
Loading required namespace: GenomicFiles
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.1 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 0.7 secs
Renaming ID as SNP.
VCF file has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8619fec6c.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f845b4cf74
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8619fec6c.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.043 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N
    <num>      <num>  <int>
1: 0.0393 0.42730011 293723
2: 0.0353 0.74669974 293723
3: 0.0370 0.05464998 293723
4: 0.0830 0.77249913 293723
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f83ec71f66.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f83ec71f66.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.04 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ      ES       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N    BETA
    <num>      <num>  <int>   <num>
1: 0.0393 0.42730011 293723  0.0312
2: 0.0353 0.74669974 293723 -0.0114
3: 0.0370 0.05464998 293723  0.0711
4: 0.0830 0.77249913 293723 -0.0240
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84e87db71.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f845b4cf74
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
The sumstats SE column is not present...Deriving SE from Beta and P
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84e87db71.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.044 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
            P      N         SE IMPUTATION_SE
        <num>  <int>      <num>        <lgcl>
1: 0.42730011 293723 0.03930361          TRUE
2: 0.74669974 293723 0.03529477          TRUE
3: 0.05464998 293723 0.03699948          TRUE
4: 0.77249913 293723 0.08301411          TRUE
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f843088f9c.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f845b4cf74
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Summary statistics report:
   - 25 rows
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
The sumstats BETA column is not present...Deriving BETA from Z and SE
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
13 SNPs (52%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f843088f9c.tsv.gz
Summary statistics report:
   - 25 rows (100% of original 25 rows)
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.046 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR     BP     A1     A2       FRQ      Z          SE      P
        <char> <int>  <int> <char> <char>     <num>  <num>       <num>  <num>
1:  rs12184267     1 715265      C      T 0.9591931 -0.916 0.007518884 0.3598
2:  rs12184277     1 715367      A      G 0.9589313 -0.656 0.007491601 0.5116
3:  rs12184279     1 717485      C      A 0.9594241 -1.050 0.007534860 0.2938
4: rs116801199     1 720381      G      T 0.9578380 -0.300 0.007391344 0.7644
        N         BETA IMPUTATION_BETA
    <int>        <num>          <lgcl>
1: 225955 -0.006887298            TRUE
2: 226215 -0.004914490            TRUE
3: 226224 -0.007911603            TRUE
4: 226626 -0.002217403            TRUE
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Filtering SNPs based on INFO score.
46 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/info_filter.tsv.gz
INFO_filter==0. Skipping INFO score filtering step.
Filtering SNPs based on INFO score.
All rows have INFO>=0.9
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
3 p-values are >1 which LDSC/MAGMA may not be able to handle. These will be converted to 1.
5 p-values are <0 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
8 p-values are <=5e-324 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
VCF format detected.This will be converted to a standardised table format.
Importing tabular file: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library/MungeSumstats/extdata/eduAttainOkbay.txt
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Computing Z-score from P using formula: `sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE)`
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	Z	newZ	
Computing Z-score from BETA ans SE using formula: `BETA/SE`


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8f2c228a.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85e433867
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8f2c228a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.087 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f831ce6ab6.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85e433867
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f831ce6ab6.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.047 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86c04b5ba.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f89e44f18
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86c04b5ba.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.093 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f82e7d6a2f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f89e44f18
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f82e7d6a2f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.047 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85dfd677d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f86dc2097e
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Warning: Multiple columns in the sumstats file seem to relate to alleles A1>A2.
The column ALLELES will be kept whereas the column(s) ALLELE will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column ALLELES has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85dfd677d.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.046 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f855139d1b.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f86dc2097e
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f855139d1b.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.049 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8190bc271.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f86f46eb72
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8190bc271.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.09 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81b2960de.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f86f46eb72
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f81b2960de.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f836fdd295.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f876a8999f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Warning: Multiple columns in the sumstats file seem to relate to Chromosome:Base Pair position.
The column CHR_BP_2 will be kept whereas the column(s) CHR_BP will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column CHR_BP_2 has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f836fdd295.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.083 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84e779cdf.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f876a8999f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84e779cdf.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.044 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84595ba7c.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f866a89423
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84595ba7c.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.044 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f876467fa1.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f853782ade
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f876467fa1.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.045 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Setting sorted=FALSE (required when formatted=FALSE).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8196e31c8.tsv.gz
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Assigning N=1000 for all SNPs.
N already exists within sumstats_dt.
[1] "Testing: compute_n='ldsc'"
Computing effective sample size using the LDSC method:
 Neff = (N_CAS+N_CON) * (N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))[(N_CAS+N_CON)==max(N_CAS+N_CON)]))
[1] "Testing: compute_n='giant'"
Computing effective sample size using the GIANT method:
 Neff = 2 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='metal'"
Computing effective sample size using the METAL method:
 Neff = 4 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='sum'"
Computing sample size using the sum method:
 N = N_CAS + N_CON


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85964ec66.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8324afded
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85964ec66.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.038 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f851c133aa.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Saving output messages to:
/tmp/RtmpJeZB3s/file144f851c133aa_log_msg.txt
Any runtime errors will be saved to:
/tmp/RtmpJeZB3s/file144f851c133aa_log_output.txt
Messages will not be printed to terminal.
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85b5a0162.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8795556f9
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85b5a0162.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.041 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f845248f6d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f829d49b5f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 186 rows
   - 93 unique variants
   - 140 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
93 sumstat rows are duplicated. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f845248f6d.tsv.gz
Summary statistics report:
   - 93 rows (50% of original 186 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.041 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84784729a.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f829d49b5f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84784729a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.038 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87cc26c51.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f829d49b5f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 94 rows
   - 94 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
1 base-pair positions are duplicated in the sumstats file. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f87cc26c51.tsv.gz
Summary statistics report:
   - 93 rows (98.9% of original 94 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.425 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f838edbb5.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8fac765d
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Filtering effect columns, ensuring none equal 0.
5 SNPs have effect values = 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f838edbb5.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.047 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85e8d4a4d.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f84348c53d
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85e8d4a4d.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.042 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
        FRQ
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f854f02637.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f84348c53d
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=FALSE, the FRQ column will be renamed MAJOR_ALLELE_FRQ to differentiate the values from 
minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f854f02637.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.043 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
   MAJOR_ALLELE_FRQ
              <num>
1:         1.863269
2:         1.169733
3:         1.401423
4:         1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86ad68748.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f844946684
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86ad68748.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.044 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86dcc3c7e.tsv
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.
Reading header.
Reading entire file.
Sorting coordinates with 'GenomicRanges'.
Converting summary statistics to GenomicRanges.
Sorting coordinates with 'data.table'.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f82dce863f.tsv.gz
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	non_effect_allele	effect_allele	FRQ	BETA1	SE	P	
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	non_effect_allele	effect_allele	FRQ	BETA1	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f82dce863f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.045 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f818872277.tsv.gz
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A2	A1	FRQ	BETA1	SE	P	
Allele columns are ambiguous, attempting to infer direction
Found direction from effect/frq column naming
Effect/frq column(s) relate to A1 in the inputted sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA1	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f818872277.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.046 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f877ba598e.tsv.gz
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A2	A1	A1FRQ	BETA	SE	P	
Allele columns are ambiguous, attempting to infer direction
Found direction from effect/frq column naming
Effect/frq column(s) relate to A1 in the inputted sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	A1FRQ	BETA	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f877ba598e.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.043 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f875640fe7.tsv.gz
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A2	A1	FRQ	BETA	SE	P	
Allele columns are ambiguous, attempting to infer direction
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A2	A1	FRQ	BETA	SE	P	
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 17 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
Effect/frq column(s) relate to A1 in the inputted sumstats
Found direction from matchine reference genome - NOTE this assumes non-effect allele will macth the reference genome
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Summary statistics report:
   - 17 rows
   - 17 unique variants
   - 15 genome-wide significant variants (P<5e-8)
   - 2 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Ensuring all SNPs are on the reference genome.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 17 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 22 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
6 SNPs (35.3%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f875640fe7.tsv.gz
Summary statistics report:
   - 17 rows (100% of original 17 rows)
   - 17 unique variants
   - 15 genome-wide significant variants (P<5e-8)
   - 2 chromosomes
Done munging in 0.839 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85eb9a063.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85e0dd76a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
Filtering SNPs based on INFO score.
38 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/info_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
28 SNPs (50.9%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85eb9a063.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.046 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
       INFO
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8251ea4c1.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f86b78458e
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8251ea4c1.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.037 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84fae9319.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84fae9319.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.039 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
[1] "/tmp/RtmpJeZB3s/data/file1/file144f821ae26de.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file2/file144f83079ca18.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file3/file144f8bc30685.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file4/file144f830e10bcb.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file5/file144f86cd557f.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file6/file144f812a80856.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file7/file144f855bb4fab.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file8/file144f87c6b8f85.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file9/file144f8799ba493.tsv.gz"
[1] "/tmp/RtmpJeZB3s/data/file10/file144f85951ed44.tsv.gz"
10 file(s) found.
Parsing info from 10 log file(s).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f814660c02.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8373952b9
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
WARNING: 1 rows in sumstats file are missing data and will be removed.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f814660c02.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.04 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f83f695424.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8373952b9
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f83f695424.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.056 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84b4f0c36.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 4 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84b4f0c36.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.147 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f83b680f51.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8481e458c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Standardising column headers.
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 1 chromosomes
Checking for multi-GWAS.
WARNING: Multiple traits found in sumstats file only one of which can be analysed: 
SMK, NONSMK
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	MARKERNAME	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	EFFECT	STDERR	P_VALUE	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column MARKERNAME has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	BETA	SE	P	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f83b680f51.tsv.gz
Summary statistics report:
   - 4 rows (80% of original 5 rows)
   - 4 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.155 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
         SNP    CHR        BP     A1     A2 POSITION_HG18 EAF_HAPMAPCEU     N
      <char> <char>     <int> <char> <char>         <int>         <num> <int>
1: rs1000050   chr1 161003087      C      T     161003087        0.9000 36257
2: rs1000073   chr1 155522020      G      A     155522020        0.3136 36335
3: rs1000075   chr1  94939420      C      T      94939420        0.3583 38959
4: rs1000085   chr1  66630503      G      C      66630503        0.1667 38761
      BETA     SE      P N_NONSMK EFFECT_NONSMK STDERR_NONSMK P_VALUE_NONSMK
     <num>  <num>  <num>    <int>         <num>         <num>          <num>
1:  0.0001 0.0109 0.9931   127514        0.0058        0.0059         0.3307
2:  0.0046 0.0083 0.5812   126780        0.0038        0.0045         0.3979
3: -0.0013 0.0082 0.8687   147567       -0.0043        0.0044         0.3259
4:  0.0053 0.0095 0.5746   147259       -0.0034        0.0052         0.5157
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f82cf4bced.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8f49a8fa
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f82cf4bced.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.055 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     5
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     1
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     1
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     7
   N_FIXED
     <int>
1:       5
2:       1
3:       1
4:       7
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8773769da.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f834945865
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpJeZB3s/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8773769da.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.055 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f866d97464.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f834945865
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpJeZB3s/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f866d97464.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f88d4551a.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f834945865
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpJeZB3s/n_large.tsv.gz
Removing rows where is.na(N)
0 SNPs have N values that are NA and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/n_null.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f88d4551a.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f845508eba.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8237c8a21
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	BP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 45 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f845508eba.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.875 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86b707b3c.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f83c1495b4
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 21 seconds.
Writing in tabular format ==> /tmp/RtmpJeZB3s/chr_bp_not_found_from_snp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86b707b3c.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.495 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f815070c05.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f812a132b1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f815070c05.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.049 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8168301b9.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f812a132b1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8168301b9.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.061 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8413454d6.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f849803498
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 92 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 57 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8413454d6.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.073 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87c99880a.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f87ffd0cda
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f87c99880a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.118 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8350eefdc.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f83e56fddb
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81e4a517f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f849803498
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f81e4a517f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.092 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81ae05a25.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85f3ee7bd
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no SNP column found within the data. It must be inferred from other column information.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f81ae05a25.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.158 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84f077068.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f863cba61f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 23 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
1 SNPs have been removed as their BP column is not in the range of 1 to the length of the chromosome
Writing in tabular format ==> /tmp/RtmpJeZB3s/bad_bp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs are on chromosomes X, Y, MT and will be removed.
Writing in tabular format ==> /tmp/RtmpJeZB3s/chr_excl.tsv.gz
45 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84f077068.tsv.gz
Summary statistics report:
   - 90 rows (96.8% of original 93 rows)
   - 90 unique variants
   - 67 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f812e323d1.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f863cba61f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f812e323d1.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.096 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Reading header.
Reading entire file.
Reading header.
Reading header.
Reading header.
Reading header.
Reading header.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8248d77ae
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8668df31c
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f83b9b984b.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f83b9b984b.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.9 secs
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	ALT	SNP	FRQ	BETA	SE	P	
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.3 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 0.9 secs
Renaming ID as SNP.
VCF file has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85806fb04.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f85806fb04.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 101 rows x 13 columns.
Time difference of 0.6 secs
VCF file has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	SNP	END	FILTER	FRQ	BETA	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8341665fb.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
5 SNP IDs contain other information in the same column. These will be separated.
Checking for merged allele column.
Column SNP_INFO has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8341665fb.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.072 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f812dcd979.tsv.gz
Log data to be saved to ==>  /tmp/RtmpJeZB3s
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f812dcd979.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.045 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81c655a04.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f836d029c1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86723566b.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f82d945bfd
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86723566b.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.049 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f85f6fa54f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f82d945bfd
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f85f6fa54f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.081 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86bfa0ab3.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f82e6318ef
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86bfa0ab3.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.097 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f869e31d8f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f8397bb9cc
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f869e31d8f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.065 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87cc693b0.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f82b384b0a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
5 SNPs have SE values <= 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f87cc693b0.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.049 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support	
Returning unmapped column names without making them uppercase.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support	
Returning unmapped column names without making them uppercase.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84c463f9d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f83bc04648
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 85 rows
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for strand ambiguous SNPs.
43 SNPs (50.6%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84c463f9d.tsv.gz
Summary statistics report:
   - 85 rows (100% of original 85 rows)
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Done munging in 0.1 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86d268b39.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f83bc04648
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for strand ambiguous SNPs.
8 SNPs are strand-ambiguous alleles including 4 A/T and 4 C/G ambiguous SNPs. These will be removed
43 SNPs (50.6%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86d268b39.tsv.gz
Summary statistics report:
   - 85 rows (91.4% of original 93 rows)
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f844e76a2.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86f466e49.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f83be62bef
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f86f466e49.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.093 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.
Converting summary statistics to GenomicRanges.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f813f6e53.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f86b640bc8.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87541ae8c.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f82b2aa578.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f87cd57f6b.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f824a089d4.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f827b8d015.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f856652712.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8ae83b0e.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8147d19ba.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f821240cd8.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f81b5b2204.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpJeZB3s/file144f85651e812
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
.tsv
=== write tests ===
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f830c41200.tsv

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f830c41200.tsv
Checking for empty columns.
.tsv.gz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8786a3e88.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f8786a3e88.tsv.gz
Checking for empty columns.
.tsv.bgz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8622d7dc4.tsv.bgz

=== read tests ===
Importing tabular bgz file: /tmp/RtmpJeZB3s/file144f8622d7dc4.tsv.bgz
Checking for empty columns.
.tsv.gz
=== write tests ===
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f868ba5e93.tsv
Writing uncompressed instead of gzipped to enable tabix indexing.
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.

=== read tests ===
Importing tabular bgz file: /tmp/RtmpJeZB3s/file144f868ba5e93.tsv.bgz
Checking for empty columns.
.tsv.bgz
=== write tests ===
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8d819e9e.tsv
Writing uncompressed instead of gzipped to enable tabix indexing.
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.

=== read tests ===
Importing tabular bgz file: /tmp/RtmpJeZB3s/file144f8d819e9e.tsv.bgz
Checking for empty columns.
.csv
=== write tests ===
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8191222e2.csv

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f8191222e2.csv
Checking for empty columns.
.csv.gz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f874c2a42e.csv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f874c2a42e.csv.gz
Checking for empty columns.
.vcf
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

save_path suggests VCF output but write_vcf=FALSE. Switching output to tabular format (.tsv.gz).
Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8661004f9.tsv.gz
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f8661004f9.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f8661004f9.tsv.gz
Checking for empty columns.
.vcf.gz
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

save_path suggests VCF output but write_vcf=FALSE. Switching output to tabular format (.tsv.gz).
Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f84acaaf9b.tsv.gz
Writing in tabular format ==> /tmp/RtmpJeZB3s/file144f84acaaf9b.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpJeZB3s/file144f84acaaf9b.tsv.gz
Checking for empty columns.
.vcf
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f83e38b0f4.vcf

=== read tests ===
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.5 secs
No INFO (SI) column detected.
.vcf.gz
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f86f2290d1.vcf.gz

=== read tests ===
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.6 secs
No INFO (SI) column detected.
.vcf
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f82016edf6.vcf
.vcf
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpJeZB3s/file144f8e6a3c1c.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f8e6a3c1c.vcf.bgz

=== read tests ===
Using local VCF.
File already tabix-indexed.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.5 secs
No INFO (SI) column detected.
.vcf.bgz
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpJeZB3s/file144f83bf4937c.vcf.bgz

=== read tests ===
Using local VCF.
File already tabix-indexed.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.5 secs
No INFO (SI) column detected.
[ FAIL 0 | WARN 4 | SKIP 0 | PASS 184 ]

[ FAIL 0 | WARN 4 | SKIP 0 | PASS 184 ]
> 
> proc.time()
   user  system elapsed 
561.106  34.919 754.116 

Example timings

MungeSumstats.Rcheck/MungeSumstats-Ex.timings

nameusersystemelapsed
compute_nsize3.5510.1363.704
download_vcf000
find_sumstats0.0010.0000.001
format_sumstats67.370 3.97872.248
formatted_example2.3430.2152.568
get_genome_builds76.270 5.11282.380
import_sumstats0.0010.0000.002
index_tabular2.6910.0912.799
index_vcf2.3590.0212.393
infer_effect_column2.5260.0262.574
liftover3.4310.0545.454
list_sumstats0.0020.0010.004
load_snp_loc_data0.0000.0000.001
parse_logs0.0100.0010.013
read_header0.0030.0020.005
read_sumstats0.0060.0010.007
read_vcf1.9310.0241.963
standardise_header2.4590.0142.480
vcf2df0.7560.0990.863
write_sumstats0.0060.0020.009