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CHECK report for HIBAG on machv2

This page was generated on 2020-10-17 11:58:41 -0400 (Sat, 17 Oct 2020).

TO THE DEVELOPERS/MAINTAINERS OF THE HIBAG PACKAGE: Please make sure to use the following settings in order to reproduce any error or warning you see on this page.
Package 813/1905HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
HIBAG 1.24.0
Xiuwen Zheng
Snapshot Date: 2020-10-16 14:40:19 -0400 (Fri, 16 Oct 2020)
URL: https://git.bioconductor.org/packages/HIBAG
Branch: RELEASE_3_11
Last Commit: c8e5723
Last Changed Date: 2020-04-27 14:46:42 -0400 (Mon, 27 Apr 2020)
malbec2 Linux (Ubuntu 18.04.4 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay2 Windows Server 2012 R2 Standard / x64  OK  OK  OK  OK UNNEEDED, same version exists in internal repository
machv2 macOS 10.14.6 Mojave / x86_64  OK  OK [ OK ] OK UNNEEDED, same version exists in internal repository

Summary

Package: HIBAG
Version: 1.24.0
Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:HIBAG.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings HIBAG_1.24.0.tar.gz
StartedAt: 2020-10-17 01:59:30 -0400 (Sat, 17 Oct 2020)
EndedAt: 2020-10-17 02:01:15 -0400 (Sat, 17 Oct 2020)
EllapsedTime: 105.0 seconds
RetCode: 0
Status:  OK 
CheckDir: HIBAG.Rcheck
Warnings: 0

Command output

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:HIBAG.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings HIBAG_1.24.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.11-bioc/meat/HIBAG.Rcheck’
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-apple-darwin17.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HIBAG/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HIBAG’ version ‘1.24.0’
* 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 ‘HIBAG’ 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 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 line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.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: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.11-bioc/meat/HIBAG.Rcheck/00check.log’
for details.



Installation output

HIBAG.Rcheck/00install.out

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL HIBAG
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.0/Resources/library’
* installing *source* package ‘HIBAG’ ...
** using staged installation
** libs
clang++ -mmacosx-version-min=10.13 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c HIBAG.cpp -o HIBAG.o
clang++ -mmacosx-version-min=10.13 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c LibHLA.cpp -o LibHLA.o
clang++ -mmacosx-version-min=10.13 -std=gnu++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o HIBAG.so HIBAG.o LibHLA.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/4.0/Resources/library/00LOCK-HIBAG/00new/HIBAG/libs
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
   ‘HIBAG.Rmd’ 
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HIBAG)

Tests output

HIBAG.Rcheck/tests/runTests.Rout


R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin17.0 (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.

> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
> 
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.4
Supported by Streaming SIMD Extensions (SSE2) [64-bit]
> 
> 
> #############################################################
> 
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
> 
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7)
> 
> 
> for (hla.idx in seq_len(length(hla.list)))
+ {
+ 	hla.id <- hla.list[hla.idx]
+ 
+ 	# make a "hlaAlleleClass" object
+ 	hla <- hlaAllele(HLA_Type_Table$sample.id,
+ 		H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ 		H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ 		locus=hla.id, assembly="hg19")
+ 
+ 	# divide HLA types randomly
+ 	set.seed(100)
+ 	hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+ 
+ 	# SNP predictors within the flanking region on each side
+ 	region <- 500	# kb
+ 	snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ 		HapMap_CEU_Geno$snp.position,
+ 		hla.id, region*1000, assembly="hg19")
+ 
+ 	# training and validation genotypes
+ 	train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ 		samp.sel=match(hlatab$training$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 	test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		samp.sel=match(hlatab$validation$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 
+ 
+ 	# train a HIBAG model
+ 	set.seed(100)
+ 	model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ 	summary(model)
+ 
+ 	# validation
+ 	pred <- predict(model, test.geno)
+ 	summary(pred)
+ 
+ 	# compare
+ 	comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ 		call.threshold=0)
+ 	print(comp$overall)
+ 
+ 	# check
+ 	if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ 	{
+ 		stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ",
+ 			hla.acc[hla.idx], ".")
+ 	}
+ 
+ 	cat("\n\n")
+ }
Exclude 11 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:28
[1] 2020-10-17 02:00:28, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
[2] 2020-10-17 02:00:28, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
[3] 2020-10-17 02:00:28, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
[4] 2020-10-17 02:00:28, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
[5] 2020-10-17 02:00:28, OOB Acc: 79.41%, # of SNPs: 13, # of Haplo: 34
[6] 2020-10-17 02:00:28, OOB Acc: 100.00%, # of SNPs: 19, # of Haplo: 72
[7] 2020-10-17 02:00:29, OOB Acc: 100.00%, # of SNPs: 17, # of Haplo: 37
[8] 2020-10-17 02:00:29, OOB Acc: 84.62%, # of SNPs: 14, # of Haplo: 58
[9] 2020-10-17 02:00:29, OOB Acc: 89.29%, # of SNPs: 13, # of Haplo: 34
[10] 2020-10-17 02:00:29, OOB Acc: 80.77%, # of SNPs: 14, # of Haplo: 24
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 
        Max.         Mean           SD 
0.5364225707 0.0500771527 0.1262815588 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 86.1%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 93
    avg. # of SNPs in an individual classifier: 13.90
        (sd: 2.38, min: 11, max: 19, median: 13.00)
    avg. # of haplotypes in an individual classifier: 36.70
        (sd: 17.93, min: 14, max: 72, median: 34.00)
    avg. out-of-bag accuracy: 86.05%
        (sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 
        Max.         Mean           SD 
0.5364225707 0.0500771527 0.1262815588 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:29)	0%
Predicting (2020-10-17 02:00:29)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  3 (11.5%)  4 (15.4%) 18 (69.2%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.002746 0.006607 0.033131 0.023928 0.536423 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          25            51 0.9615385 0.9807692              0
  n.call call.rate
1     26         1


Exclude 1 monomorphic SNP
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340, # of samples: 28
# of unique HLA alleles: 22
[-] 2020-10-17 02:00:29
[1] 2020-10-17 02:00:29, OOB Acc: 58.33%, # of SNPs: 17, # of Haplo: 52
[2] 2020-10-17 02:00:29, OOB Acc: 63.64%, # of SNPs: 18, # of Haplo: 51
[3] 2020-10-17 02:00:29, OOB Acc: 50.00%, # of SNPs: 15, # of Haplo: 29
[4] 2020-10-17 02:00:30, OOB Acc: 59.09%, # of SNPs: 12, # of Haplo: 57
[5] 2020-10-17 02:00:30, OOB Acc: 63.64%, # of SNPs: 15, # of Haplo: 86
[6] 2020-10-17 02:00:30, OOB Acc: 79.17%, # of SNPs: 18, # of Haplo: 66
[7] 2020-10-17 02:00:30, OOB Acc: 70.83%, # of SNPs: 15, # of Haplo: 86
[8] 2020-10-17 02:00:31, OOB Acc: 77.78%, # of SNPs: 16, # of Haplo: 117
[9] 2020-10-17 02:00:31, OOB Acc: 77.78%, # of SNPs: 18, # of Haplo: 92
[10] 2020-10-17 02:00:31, OOB Acc: 61.11%, # of SNPs: 15, # of Haplo: 72
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 
        Max.         Mean           SD 
2.325640e-01 1.687210e-02 4.316862e-02 
Accuracy with training data: 100.0%
Out-of-bag accuracy: 66.1%
Gene: B
Training dataset: 28 samples X 340 SNPs
    # of HLA alleles: 22
    # of individual classifiers: 10
    total # of SNPs used: 118
    avg. # of SNPs in an individual classifier: 15.90
        (sd: 1.91, min: 12, max: 18, median: 15.50)
    avg. # of haplotypes in an individual classifier: 70.80
        (sd: 25.28, min: 29, max: 117, median: 69.00)
    avg. out-of-bag accuracy: 66.14%
        (sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 
        Max.         Mean           SD 
2.325640e-01 1.687210e-02 4.316862e-02 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 15
Predicting (2020-10-17 02:00:31)	0%
Predicting (2020-10-17 02:00:31)	100%
Gene: B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 12
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (20.0%)  5 (33.3%)  3 (20.0%)  4 (26.7%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
2.000e-08 4.068e-05 2.934e-03 3.316e-02 6.112e-03 2.412e-01 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            15          11            25 0.7333333 0.8333333              0
  n.call call.rate
1     15         1


Exclude 2 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354, # of samples: 36
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:31
[1] 2020-10-17 02:00:31, OOB Acc: 80.77%, # of SNPs: 19, # of Haplo: 40
[2] 2020-10-17 02:00:32, OOB Acc: 90.91%, # of SNPs: 32, # of Haplo: 32
[3] 2020-10-17 02:00:32, OOB Acc: 89.29%, # of SNPs: 19, # of Haplo: 43
[4] 2020-10-17 02:00:32, OOB Acc: 84.62%, # of SNPs: 19, # of Haplo: 72
[5] 2020-10-17 02:00:32, OOB Acc: 90.00%, # of SNPs: 19, # of Haplo: 66
[6] 2020-10-17 02:00:32, OOB Acc: 95.00%, # of SNPs: 21, # of Haplo: 59
[7] 2020-10-17 02:00:33, OOB Acc: 90.62%, # of SNPs: 18, # of Haplo: 25
[8] 2020-10-17 02:00:33, OOB Acc: 89.29%, # of SNPs: 23, # of Haplo: 57
[9] 2020-10-17 02:00:33, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 39
[10] 2020-10-17 02:00:33, OOB Acc: 89.29%, # of SNPs: 35, # of Haplo: 62
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 
        Max.         Mean           SD 
0.0723014524 0.0089787922 0.0135525777 
Accuracy with training data: 100.0%
Out-of-bag accuracy: 88.4%
Gene: C
Training dataset: 36 samples X 354 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 10
    total # of SNPs used: 135
    avg. # of SNPs in an individual classifier: 22.30
        (sd: 6.13, min: 18, max: 35, median: 19.00)
    avg. # of haplotypes in an individual classifier: 49.50
        (sd: 15.74, min: 25, max: 72, median: 50.00)
    avg. out-of-bag accuracy: 88.44%
        (sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 
        Max.         Mean           SD 
0.0723014524 0.0089787922 0.0135525777 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 24
Predicting (2020-10-17 02:00:33)	0%
Predicting (2020-10-17 02:00:33)	100%
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 19
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (8.3%)  3 (12.5%)  6 (25.0%) 13 (54.2%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000000 0.0002058 0.0060220 0.0035911 0.0497611 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            24          16            39 0.6666667    0.8125              0
  n.call call.rate
1     24         1


Exclude 4 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 345, # of samples: 31
# of unique HLA alleles: 7
[-] 2020-10-17 02:00:33
[1] 2020-10-17 02:00:33, OOB Acc: 95.45%, # of SNPs: 11, # of Haplo: 22
[2] 2020-10-17 02:00:33, OOB Acc: 100.00%, # of SNPs: 13, # of Haplo: 22
[3] 2020-10-17 02:00:33, OOB Acc: 83.33%, # of SNPs: 15, # of Haplo: 23
[4] 2020-10-17 02:00:33, OOB Acc: 82.14%, # of SNPs: 8, # of Haplo: 14
[5] 2020-10-17 02:00:34, OOB Acc: 88.46%, # of SNPs: 11, # of Haplo: 34
[6] 2020-10-17 02:00:34, OOB Acc: 90.00%, # of SNPs: 11, # of Haplo: 21
[7] 2020-10-17 02:00:34, OOB Acc: 92.31%, # of SNPs: 14, # of Haplo: 23
[8] 2020-10-17 02:00:34, OOB Acc: 96.15%, # of SNPs: 11, # of Haplo: 16
[9] 2020-10-17 02:00:34, OOB Acc: 89.29%, # of SNPs: 12, # of Haplo: 19
[10] 2020-10-17 02:00:34, OOB Acc: 86.36%, # of SNPs: 8, # of Haplo: 13
Calculating matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 
       Max.        Mean          SD 
0.557800372 0.029648068 0.098391249 
Accuracy with training data: 96.8%
Out-of-bag accuracy: 90.4%
Gene: DQA1
Training dataset: 31 samples X 345 SNPs
    # of HLA alleles: 7
    # of individual classifiers: 10
    total # of SNPs used: 80
    avg. # of SNPs in an individual classifier: 11.40
        (sd: 2.27, min: 8, max: 15, median: 11.00)
    avg. # of haplotypes in an individual classifier: 20.70
        (sd: 5.96, min: 13, max: 34, median: 21.50)
    avg. out-of-bag accuracy: 90.35%
        (sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%)
Matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 
       Max.        Mean          SD 
0.557800372 0.029648068 0.098391249 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 345 SNPs, 7 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 29
Predicting (2020-10-17 02:00:34)	0%
Predicting (2020-10-17 02:00:34)	100%
Gene: DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 6
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 5 (17.2%)  5 (17.2%)   2 (6.9%) 17 (58.6%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000001 0.0019253 0.0069908 0.0562248 0.0167536 0.5649087 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            29          21            49 0.7241379 0.8448276              0
  n.call call.rate
1     29         1


Exclude 6 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 350, # of samples: 34
# of unique HLA alleles: 12
[-] 2020-10-17 02:00:34
[1] 2020-10-17 02:00:34, OOB Acc: 86.36%, # of SNPs: 13, # of Haplo: 34
[2] 2020-10-17 02:00:34, OOB Acc: 76.92%, # of SNPs: 21, # of Haplo: 42
[3] 2020-10-17 02:00:34, OOB Acc: 80.77%, # of SNPs: 10, # of Haplo: 17
[4] 2020-10-17 02:00:34, OOB Acc: 92.31%, # of SNPs: 22, # of Haplo: 78
[5] 2020-10-17 02:00:34, OOB Acc: 92.31%, # of SNPs: 11, # of Haplo: 40
[6] 2020-10-17 02:00:34, OOB Acc: 71.43%, # of SNPs: 8, # of Haplo: 22
[7] 2020-10-17 02:00:35, OOB Acc: 71.43%, # of SNPs: 14, # of Haplo: 53
[8] 2020-10-17 02:00:35, OOB Acc: 86.36%, # of SNPs: 14, # of Haplo: 40
[9] 2020-10-17 02:00:35, OOB Acc: 100.00%, # of SNPs: 16, # of Haplo: 56
[10] 2020-10-17 02:00:35, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 
        Max.         Mean           SD 
0.2905810098 0.0221828682 0.0553213757 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 84.6%
Gene: DQB1
Training dataset: 34 samples X 350 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 10
    total # of SNPs used: 99
    avg. # of SNPs in an individual classifier: 14.30
        (sd: 4.45, min: 8, max: 22, median: 14.00)
    avg. # of haplotypes in an individual classifier: 41.60
        (sd: 17.55, min: 17, max: 78, median: 40.00)
    avg. out-of-bag accuracy: 84.64%
        (sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 
        Max.         Mean           SD 
0.2905810098 0.0221828682 0.0553213757 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 350 SNPs, 12 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:35)	0%
Predicting (2020-10-17 02:00:35)	100%
Gene: DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (11.5%)  7 (26.9%)  5 (19.2%) 11 (42.3%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0002253 0.0018486 0.0289671 0.0099906 0.3724278 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          21            46 0.8076923 0.8846154              0
  n.call call.rate
1     26         1


Exclude 5 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 322, # of samples: 35
# of unique HLA alleles: 20
[-] 2020-10-17 02:00:35
[1] 2020-10-17 02:00:35, OOB Acc: 70.00%, # of SNPs: 17, # of Haplo: 77
[2] 2020-10-17 02:00:36, OOB Acc: 68.75%, # of SNPs: 22, # of Haplo: 119
[3] 2020-10-17 02:00:36, OOB Acc: 73.33%, # of SNPs: 19, # of Haplo: 33
[4] 2020-10-17 02:00:36, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 67
[5] 2020-10-17 02:00:37, OOB Acc: 86.36%, # of SNPs: 24, # of Haplo: 127
[6] 2020-10-17 02:00:38, OOB Acc: 66.67%, # of SNPs: 18, # of Haplo: 102
[7] 2020-10-17 02:00:38, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 71
[8] 2020-10-17 02:00:38, OOB Acc: 70.00%, # of SNPs: 15, # of Haplo: 32
[9] 2020-10-17 02:00:39, OOB Acc: 91.67%, # of SNPs: 20, # of Haplo: 93
[10] 2020-10-17 02:00:39, OOB Acc: 66.67%, # of SNPs: 15, # of Haplo: 57
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 
        Max.         Mean           SD 
5.019208e-01 4.576117e-02 1.379081e-01 
Accuracy with training data: 94.3%
Out-of-bag accuracy: 75.3%
Gene: DRB1
Training dataset: 35 samples X 322 SNPs
    # of HLA alleles: 20
    # of individual classifiers: 10
    total # of SNPs used: 129
    avg. # of SNPs in an individual classifier: 18.30
        (sd: 3.06, min: 15, max: 24, median: 18.00)
    avg. # of haplotypes in an individual classifier: 77.80
        (sd: 32.72, min: 32, max: 127, median: 74.00)
    avg. out-of-bag accuracy: 75.31%
        (sd: 9.00%, min: 66.67%, max: 91.67%, median: 71.67%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 
        Max.         Mean           SD 
5.019208e-01 4.576117e-02 1.379081e-01 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 322 SNPs, 20 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 25
Predicting (2020-10-17 02:00:39)	0%
Predicting (2020-10-17 02:00:39)	100%
Gene: DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 4 (16.0%)  5 (20.0%)  9 (36.0%)  7 (28.0%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0001451 0.0007388 0.0111563 0.0028980 0.2302289 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            25          16            40    0.64       0.8              0
  n.call call.rate
1     25         1


> 
> 
> 
> #############################################################
> 
> {
+ 	function.list <- readRDS(
+ 		system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+ 
+ 	sapply(function.list, FUN = function(func.name)
+ 		{
+ 			args <- list(
+ 				topic   = func.name,
+ 				package = "HIBAG",
+ 				echo = FALSE,
+ 				verbose = FALSE,
+ 				ask = FALSE
+ 			)
+ 			suppressWarnings(do.call(example, args))
+ 			NULL
+ 		})
+ 	invisible()
+ }
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:39
     1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13
     2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13
     3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13
     4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13
     5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13
     6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13
     7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16
     8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18
     9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19
    10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21
    11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23
    12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23
    13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23
[1] 2020-10-17 02:00:39, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
     1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17
     2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23
     3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31
     4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31
     5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32
     6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34
     7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38
     8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36
     9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42
    10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42
    11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44
    12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48
[2] 2020-10-17 02:00:39, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
     1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11
     2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12
     3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12
     4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12
     5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12
     6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12
     7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12
     8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14
     9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14
    10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14
    11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14
[3] 2020-10-17 02:00:39, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
     1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13
     3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16
     4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18
     5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18
     6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20
     7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20
     8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21
     9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21
    10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21
    11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22
    12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23
    13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23
[4] 2020-10-17 02:00:39, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 81.6%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 38
    avg. # of SNPs in an individual classifier: 12.25
        (sd: 0.96, min: 11, max: 13, median: 12.50)
    avg. # of haplotypes in an individual classifier: 27.00
        (sd: 14.63, min: 14, max: 48, median: 23.00)
    avg. out-of-bag accuracy: 81.61%
        (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:39)	0%
Predicting (2020-10-17 02:00:39)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  4 (15.4%) 17 (65.4%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:39)	0%
Predicting (2020-10-17 02:00:39)	100%
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.fam".
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 90
Predicting (2020-10-17 02:00:39)	0%
Predicting (2020-10-17 02:00:39)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
dominant model:
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*
-----                                                              
01:01    36        24    50.0        50.0   0.0000  1.000    1.000 
02:01    25        35    52.0        48.6   0.0000  1.000    1.000 
02:06    59         1    50.8         0.0   0.0000  1.000    1.000 
03:01    51         9    49.0        55.6   0.0000  1.000    1.000 
11:01    55         5    50.9        40.0   0.0000  1.000    1.000 
23:01    58         2    50.0        50.0   0.0000  1.000    1.000 
24:03    59         1    50.8         0.0   0.0000  1.000    1.000 
25:01    55         5    52.7        20.0   0.8727  0.350    0.353 
26:01    57         3    52.6         0.0   1.4035  0.236    0.237 
29:02    56         4    51.8        25.0   0.2679  0.605    0.612 
31:01    57         3    49.1        66.7   0.0000  1.000    1.000 
32:01    56         4    46.4       100.0   2.4107  0.121    0.112 
68:01    57         3    52.6         0.0   1.4035  0.236    0.237 
additive model:
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01  95  25  50.5  48.0   0.0000  1.000    1.000 
02:01  77  43  48.1  53.5   0.1450  0.703    0.704 
02:06 119   1  50.4   0.0   0.0000  1.000    1.000 
03:01 111   9  49.5  55.6   0.0000  1.000    1.000 
11:01 115   5  50.4  40.0   0.0000  1.000    1.000 
23:01 117   3  50.4  33.3   0.0000  1.000    1.000 
24:02 109  11  46.8  81.8   3.6030  0.058    0.053 
24:03 119   1  50.4   0.0   0.0000  1.000    1.000 
25:01 115   5  51.3  20.0   0.8348  0.361    0.364 
26:01 117   3  51.3   0.0   1.3675  0.242    0.244 
29:02 116   4  50.9  25.0   0.2586  0.611    0.619 
31:01 117   3  49.6  66.7   0.0000  1.000    1.000 
32:01 116   4  48.3 100.0   2.3276  0.127    0.119 
68:01 117   3  51.3   0.0   1.3675  0.242    0.244 
recessive model:
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01        59     1        50.8       0    0.000  1.000    1.000 
02:01        52     8        46.2      75    1.298  0.255    0.254 
02:06        60     0        50.0       .        .       .        .
03:01        60     0        50.0       .        .       .        .
11:01        60     0        50.0       .        .       .        .
23:01        59     1        50.8       0    0.000  1.000    1.000 
24:02        60     0        50.0       .        .       .        .
24:03        60     0        50.0       .        .       .        .
25:01        60     0        50.0       .        .       .        .
26:01        60     0        50.0       .        .       .        .
29:02        60     0        50.0       .        .       .        .
31:01        60     0        50.0       .        .       .        .
32:01        60     0        50.0       .        .       .        .
68:01        60     0        50.0       .        .       .        .
genotype model:
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
dominant model:
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01    36        24  -0.14684     -0.117427  0.909 
02:01    25        35  -0.32331     -0.000618  0.190 
02:06    59         1  -0.14024      0.170057       .
03:01    51         9  -0.05600     -0.583178  0.147 
11:01    55         5  -0.19188      0.489815  0.287 
23:01    58         2  -0.15400      0.413687  0.281 
24:02    49        11  -0.10486     -0.269664  0.537 
24:03    59         1  -0.11409     -1.373118       .
25:01    55         5  -0.12237     -0.274749  0.742 
26:01    57         3  -0.12473     -0.331558  0.690 
29:02    56         4  -0.13044     -0.199941  0.789 
31:01    57         3  -0.10097     -0.783003  0.607 
32:01    56         4  -0.07702     -0.947791  0.092 
68:01    57         3  -0.16915      0.512457  0.196 
genotype model:
      [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01    36    23     1  -0.14684  -0.08833  -0.78655  0.784 
02:01    25    27     8  -0.32331  -0.02341   0.07631  0.446 
02:06    59     1     0  -0.14024   0.17006         .  0.756 
03:01    51     9     0  -0.05600  -0.58318         .  0.138 
11:01    55     5     0  -0.19188   0.48981         .  0.137 
23:01    58     1     1  -0.15400   0.10762   0.71975  0.663 
24:02    49    11     0  -0.10486  -0.26966         .  0.618 
24:03    59     1     0  -0.11409  -1.37312         .  0.205 
25:01    55     5     0  -0.12237  -0.27475         .  0.742 
26:01    57     3     0  -0.12473  -0.33156         .  0.725 
29:02    56     4     0  -0.13044  -0.19994         .  0.892 
31:01    57     3     0  -0.10097  -0.78300         .  0.243 
32:01    56     4     0  -0.07702  -0.94779         .  0.086 
68:01    57     3     0  -0.16915   0.51246         .  0.243 
Logistic regression (dominant model) with 60 individuals:
  glm(case ~ h, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.792e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  -8.777e-16
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.372e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.624e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.418e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000   2.874e-15
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.495e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.170e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.282e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.771e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
          h.2.5%   h.97.5% h.pval
24:02     0.1585    3.4251 0.032*
-----                            
01:01    -1.0330    1.0330 1.000 
02:01    -1.1643    0.8899 0.793 
02:06 -2868.1268 2836.9268 0.991 
03:01    -1.1624    1.6872 0.718 
11:01    -2.3074    1.4237 0.643 
23:01    -2.8192    2.8192 1.000 
24:03 -2868.1268 2836.9268 0.991 
25:01    -3.7498    0.7588 0.194 
26:01 -2731.9621 2698.6192 0.990 
29:02    -3.4931    1.1530 0.324 
31:01    -1.7277    3.1842 0.561 
32:01 -3859.2763 3894.6947 0.993 
68:01 -2731.9621 2698.6192 0.990 
Logistic regression (dominant model) with 60 individuals:
  glm(case ~ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.793e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  -2.268e-04
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.370e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.562e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.686e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.451e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  -3.062e-03
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.501e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.189e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.289e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.781e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.673e+01
          h.2.5%   h.97.5% h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02     0.1587    3.4264 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01    -1.0334    1.0330 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01    -1.1652    0.8913 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06 -2868.1460 2836.9076 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01    -1.1813    1.7185 0.717   0.011958  -0.5044    0.5283   0.964 
11:01    -2.3225    1.4322 0.642   0.008025  -0.5026    0.5186   0.975 
23:01    -2.8348    2.8287 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03 -2868.1286 2836.9250 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01    -3.7579    0.7568 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01 -2731.8901 2698.5450 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02    -3.5309    1.1526 0.320   0.033234  -0.4796    0.5461   0.899 
31:01    -1.7274    3.1851 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01 -3845.6317 3881.2510 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01 -2721.2124 2687.7497 0.990  -0.086589  -0.6512    0.4781   0.764 
Logistic regression (dominant model) with 60 individuals:
  glm(case ~ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p  h.est_OR
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042* 6.005e+00
-----                                                                        
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  9.998e-01
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  8.720e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  1.647e-07
03:01    51         9    49.0        55.6   0.0000  1.000    1.000  1.308e+00
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  6.407e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  9.969e-01
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  1.676e-07
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  2.230e-01
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.744e-08
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  3.045e-01
31:01    57         3    49.1        66.7   0.0000  1.000    1.000  2.073e+00
32:01    56         4    46.4       100.0   2.4107  0.121    0.112  5.428e+07
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.416e-08
      h.2.5%_OR h.97.5%_OR h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02   1.17200     30.766 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01   0.35579      2.809 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01   0.31185      2.438 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06   0.00000        Inf 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01   0.30687      5.576 0.717   0.011958  -0.5044    0.5283   0.964 
11:01   0.09803      4.188 0.642   0.008025  -0.5026    0.5186   0.975 
23:01   0.05873     16.923 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03   0.00000        Inf 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01   0.02333      2.131 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01   0.00000        Inf 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02   0.02928      3.167 0.320   0.033234  -0.4796    0.5461   0.899 
31:01   0.17774     24.171 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01   0.00000        Inf 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01   0.00000        Inf 0.990  -0.086589  -0.6512    0.4781   0.764 
Linear regression (dominant model) with 60 individuals:
  glm(y ~ h, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5% h.97.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.02941 -0.4805  0.5393
02:01    25        35  -0.32331     -0.000618  0.190   0.32269 -0.1772  0.8226
02:06    59         1  -0.14024      0.170057       .  0.31030 -1.6397  2.2603
03:01    51         9  -0.05600     -0.583178  0.147  -0.52718 -1.2136  0.1592
11:01    55         5  -0.19188      0.489815  0.287   0.68170 -0.2051  1.5685
23:01    58         2  -0.15400      0.413687  0.281   0.56768 -0.8165  1.9518
24:02    49        11  -0.10486     -0.269664  0.537  -0.16481 -0.8091  0.4795
24:03    59         1  -0.11409     -1.373118       . -1.25903 -3.1835  0.6655
25:01    55         5  -0.12237     -0.274749  0.742  -0.15237 -1.0555  0.7507
26:01    57         3  -0.12473     -0.331558  0.690  -0.20683 -1.3519  0.9383
29:02    56         4  -0.13044     -0.199941  0.789  -0.06950 -1.0709  0.9319
31:01    57         3  -0.10097     -0.783003  0.607  -0.68203 -1.8149  0.4508
32:01    56         4  -0.07702     -0.947791  0.092  -0.87077 -1.8470  0.1054
68:01    57         3  -0.16915      0.512457  0.196   0.68161 -0.4512  1.8145
      h.pval
01:01 0.910 
02:01 0.211 
02:06 0.756 
03:01 0.138 
11:01 0.137 
23:01 0.425 
24:02 0.618 
24:03 0.205 
25:01 0.742 
26:01 0.725 
29:02 0.892 
31:01 0.243 
32:01 0.086 
68:01 0.243 
Linear regression (dominant model) with 60 individuals:
  glm(y ~ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Linear regression (dominant model) with 60 individuals:
  glm(y ~ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Logistic regression (additive model) with 60 individuals:
  glm(case ~ h, family = binomial, data = data)
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p    h.est     h.2.5%
24:02 109  11  46.8  81.8   3.6030  0.058    0.053    1.7918     0.1585
-----                                                                  
01:01  95  25  50.5  48.0   0.0000  1.000    1.000   -0.1207    -1.0843
02:01  77  43  48.1  53.5   0.1450  0.703    0.704    0.2137    -0.5289
02:06 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
03:01 111   9  49.5  55.6   0.0000  1.000    1.000    0.2624    -1.1624
11:01 115   5  50.4  40.0   0.0000  1.000    1.000   -0.4418    -2.3074
23:01 117   3  50.4  33.3   0.0000  1.000    1.000   -0.4323    -2.3435
24:03 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
25:01 115   5  51.3  20.0   0.8348  0.361    0.364   -1.4955    -3.7498
26:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
29:02 116   4  50.9  25.0   0.2586  0.611    0.619   -1.1701    -3.4931
31:01 117   3  49.6  66.7   0.0000  1.000    1.000    0.7282    -1.7277
32:01 116   4  48.3 100.0   2.3276  0.127    0.119   17.7092 -3859.2763
68:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
        h.97.5% h.pval
24:02    3.4251 0.032*
-----                 
01:01    0.8430 0.806 
02:01    0.9563 0.573 
02:06 2836.9268 0.991 
03:01    1.6872 0.718 
11:01    1.4237 0.643 
23:01    1.4789 0.658 
24:03 2836.9268 0.991 
25:01    0.7588 0.194 
26:01 2698.6192 0.990 
29:02    1.1530 0.324 
31:01    3.1842 0.561 
32:01 3894.6947 0.993 
68:01 2698.6192 0.990 
Logistic regression (recessive model) with 60 individuals:
  glm(case ~ h, family = binomial, data = data)
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p   h.est
01:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
02:01        52     8        46.2      75    1.298  0.255    0.254    1.253
02:06        60     0        50.0       .        .       .        .       .
03:01        60     0        50.0       .        .       .        .       .
11:01        60     0        50.0       .        .       .        .       .
23:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
24:02        60     0        50.0       .        .       .        .       .
24:03        60     0        50.0       .        .       .        .       .
25:01        60     0        50.0       .        .       .        .       .
26:01        60     0        50.0       .        .       .        .       .
29:02        60     0        50.0       .        .       .        .       .
31:01        60     0        50.0       .        .       .        .       .
32:01        60     0        50.0       .        .       .        .       .
68:01        60     0        50.0       .        .       .        .       .
          h.2.5%  h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991 
02:01    -0.4379    2.943 0.146 
02:06          .        .      .
03:01          .        .      .
11:01          .        .      .
23:01 -2868.1268 2836.927 0.991 
24:02          .        .      .
24:03          .        .      .
25:01          .        .      .
26:01          .        .      .
29:02          .        .      .
31:01          .        .      .
32:01          .        .      .
68:01          .        .      .
Logistic regression (genotype model) with 60 individuals:
  glm(case ~ h, family = binomial, data = data)
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
         h1.est    h1.2.5%  h1.97.5% h1.pval  h2.est    h2.2.5% h2.97.5%
24:02   1.79176     0.1585    3.4251  0.032*       .          .        .
-----                                                                   
01:01   0.08701    -0.9600    1.1340  0.871  -15.566 -2868.0929 2836.961
02:01  -0.45474    -1.5524    0.6430  0.417    1.019    -0.7637    2.801
02:06 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
03:01   0.26236    -1.1624    1.6872  0.718        .          .        .
11:01  -0.44183    -2.3074    1.4237  0.643        .          .        .
23:01  16.56607 -4686.4552 4719.5873  0.994  -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
25:01  -1.49549    -3.7498    0.7588  0.194        .          .        .
26:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
29:02  -1.17007    -3.4931    1.1530  0.324        .          .        .
31:01   0.72824    -1.7277    3.1842  0.561        .          .        .
32:01  17.70917 -3859.2763 3894.6947  0.993        .          .        .
68:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
      h2.pval
24:02       .
-----        
01:01  0.991 
02:01  0.263 
02:06       .
03:01       .
11:01       .
23:01  0.994 
24:03       .
25:01       .
26:01       .
29:02       .
31:01       .
32:01       .
68:01       .
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:41
     1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13
     2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13
     3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13
     4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13
     5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13
     6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13
     7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16
     8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18
     9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19
    10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21
    11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23
    12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23
    13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23
[1] 2020-10-17 02:00:41, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
     1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17
     2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23
     3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31
     4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31
     5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32
     6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34
     7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38
     8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36
     9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42
    10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42
    11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44
    12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48
[2] 2020-10-17 02:00:41, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
     1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11
     2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12
     3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12
     4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12
     5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12
     6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12
     7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12
     8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14
     9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14
    10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14
    11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14
[3] 2020-10-17 02:00:41, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
     1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13
     3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16
     4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18
     5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18
     6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20
     7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20
     8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21
     9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21
    10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21
    11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22
    12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23
    13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23
[4] 2020-10-17 02:00:41, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 81.6%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 38
    avg. # of SNPs in an individual classifier: 12.25
        (sd: 0.96, min: 11, max: 13, median: 12.50)
    avg. # of haplotypes in an individual classifier: 27.00
        (sd: 14.63, min: 14, max: 48, median: 23.00)
    avg. out-of-bag accuracy: 81.61%
        (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:41)	0%
Predicting (2020-10-17 02:00:41)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  4 (15.4%) 17 (65.4%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:41)	0%
Predicting (2020-10-17 02:00:41)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  4 (15.4%) 17 (65.4%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.fam".
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.fam".
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bim".
Import 5316 SNPs from chromosome 6.
SNP genotypes: 
    90 samples X 5316 SNPs
    SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
    min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
2102 2046  480  471  134   83 
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2020-10-17 02:00:42
[1] 2020-10-17 02:00:42, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2020-10-17 02:00:42, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
The HIBAG model:
	There are 77 SNP predictors in total.
	There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0 
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:42
[1] 2020-10-17 02:00:42, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02 
        Max.         Mean           SD 
6.267394e-01 6.664806e-02 1.405453e-01 
Accuracy with training data: 94.2%
Out-of-bag accuracy: 87.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:42
[1] 2020-10-17 02:00:42, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02 
        Max.         Mean           SD 
2.755151e-01 2.949891e-02 6.162169e-02 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 87.5%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 24
    avg. # of SNPs in an individual classifier: 13.50
        (sd: 2.12, min: 12, max: 15, median: 13.50)
    avg. # of haplotypes in an individual classifier: 36.00
        (sd: 5.66, min: 32, max: 40, median: 36.00)
    avg. out-of-bag accuracy: 87.23%
        (sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02 
        Max.         Mean           SD 
4.511273e-01 4.807348e-02 1.006148e-01 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:42
     1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13
     2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13
     3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13
     4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13
     5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13
     6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13
     7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16
     8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18
     9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19
    10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21
    11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23
    12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23
    13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23
[1] 2020-10-17 02:00:42, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
     1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17
     2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23
     3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31
     4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31
     5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32
     6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34
     7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38
     8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36
     9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42
    10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42
    11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44
    12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48
[2] 2020-10-17 02:00:42, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
     1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11
     2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12
     3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12
     4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12
     5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12
     6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12
     7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12
     8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14
     9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14
    10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14
    11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14
[3] 2020-10-17 02:00:42, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
     1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13
     3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16
     4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18
     5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18
     6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20
     7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20
     8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21
     9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21
    10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21
    11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22
    12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23
    13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23
[4] 2020-10-17 02:00:43, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 81.6%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 38
    avg. # of SNPs in an individual classifier: 12.25
        (sd: 0.96, min: 11, max: 13, median: 12.50)
    avg. # of haplotypes in an individual classifier: 27.00
        (sd: 14.63, min: 14, max: 48, median: 23.00)
    avg. out-of-bag accuracy: 81.61%
        (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 
        Max.         Mean           SD 
0.5261716555 0.0476729895 0.1180875414 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:43)	0%
Predicting (2020-10-17 02:00:43)	100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
-23 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
238 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
239 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
240 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
241 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
242 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
243 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
244 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
245 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
246 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
247 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
248 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
249 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
250 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
251 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
252 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
253 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
254 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
255 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
256 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
257 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
258 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
259 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
260 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
261 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
262 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
263 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
264 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
265 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
266 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
267 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
268 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
269 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
270 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
271 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
272 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
273 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
274 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
275 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
276 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
277 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
278 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
279 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
280 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
281 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
282 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
283 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
284 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
285 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
286 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
287 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
288 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
289 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
290 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
291 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
292 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
293 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
294 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
295 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
296 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
297 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
298 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
299 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
300 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
301 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
302 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
303 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
304 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
305 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
306 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
307 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
308 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
309 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
310 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
311 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
312 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
313 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
314 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
315 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
316 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
317 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
318 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
319 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
320 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
321 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
322 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
323 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
324 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
325 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
326 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
327 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
328 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
329 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
330 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
331 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
332 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
333 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
334 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
335 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
336 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
337 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
338 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
339 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
340 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
341 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
  5 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
  6 120  20  92   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
-31 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
-30 120 112   .   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-29 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-28 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-27 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-26 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-25 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-24 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-23 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  6 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 96 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 97 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 98 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 99 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
100 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
101 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
102 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
103 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
104 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
106 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
107 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
108 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
109 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
110 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
111 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
112 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
113 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
114 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
115 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
116 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
117 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
118 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
119 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
120 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
121 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
122 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
123 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
124 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
125 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
126 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
127 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
128 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
129 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
130 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
131 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
132 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
133 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
134 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
135 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
136 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
137 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
138 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
139 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
140 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
141 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
142 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
143 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
144 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
145 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
146 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
147 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
148 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
149 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
150 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
152 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
153 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
154 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
155 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
156 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
157 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
158 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
159 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
160 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
162 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
164 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
165 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
166 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
168 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
169 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
170 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
171 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
172 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
173 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
174 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
175 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
176 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
177 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
178 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
179 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
180 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
181 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
182 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
183 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:47
[1] 2020-10-17 02:00:47, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2020-10-17 02:00:48, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2020-10-17 02:00:48, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2020-10-17 02:00:48, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
[5] 2020-10-17 02:00:48, OOB Acc: 78.95%, # of SNPs: 14, # of Haplo: 21
[6] 2020-10-17 02:00:48, OOB Acc: 93.75%, # of SNPs: 16, # of Haplo: 22
[7] 2020-10-17 02:00:48, OOB Acc: 93.75%, # of SNPs: 24, # of Haplo: 81
[8] 2020-10-17 02:00:49, OOB Acc: 92.86%, # of SNPs: 20, # of Haplo: 45
[9] 2020-10-17 02:00:49, OOB Acc: 94.74%, # of SNPs: 16, # of Haplo: 45
[10] 2020-10-17 02:00:49, OOB Acc: 97.37%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 
        Max.         Mean           SD 
0.3770420992 0.0416903408 0.0825371577 
Accuracy with training data: 98.3%
Out-of-bag accuracy: 91.9%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 95
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 3.50, min: 12, max: 24, median: 15.00)
    avg. # of haplotypes in an individual classifier: 37.20
        (sd: 18.22, min: 21, max: 81, median: 36.00)
    avg. out-of-bag accuracy: 91.92%
        (sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 
        Max.         Mean           SD 
0.3770420992 0.0416903408 0.0825371577 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Import 2348 SNPs from chromosome 6.
3 SNPs with invalid alleles have been removed.
SNP genotypes: 
    165 samples X 2345 SNPs
    SNPs range from 24652946bp to 33524089bp on hg18
Missing rate per SNP:
    min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919
Missing rate per sample:
    min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 
556 467 410 400 116 109 103  81  30  29  28  16 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.fam".
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
No allelic strand orders are switched.
SNP genotypes: 
    150 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1197 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
    min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
    min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T 
511 476 105 105 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.fam".
Open "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
No allelic strand orders are switched.
SNP genotypes: 
    60 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:52
[1] 2020-10-17 02:00:52, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 
        Max.         Mean           SD 
9.105734e-02 2.054649e-02 2.598603e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 92.0%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:52
[1] 2020-10-17 02:00:52, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 
        Max.         Mean           SD 
9.784316e-02 1.490504e-02 1.947399e-02 
Accuracy with training data: 97.5%
Out-of-bag accuracy: 97.5%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:52
[1] 2020-10-17 02:00:52, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 
        Max.         Mean           SD 
1.808372e-01 2.422083e-02 3.699146e-02 
Accuracy with training data: 95.8%
Out-of-bag accuracy: 88.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 3
    total # of SNPs used: 40
    avg. # of SNPs in an individual classifier: 18.67
        (sd: 5.03, min: 14, max: 24, median: 18.00)
    avg. # of haplotypes in an individual classifier: 33.67
        (sd: 4.51, min: 29, max: 38, median: 34.00)
    avg. out-of-bag accuracy: 92.80%
        (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 
        Max.         Mean           SD 
1.210500e-01 1.989079e-02 2.507466e-02 
Genome assembly: hg19
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2020-10-17 02:00:52
[1] 2020-10-17 02:00:52, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2020-10-17 02:00:52, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:52
[1] 2020-10-17 02:00:52, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2020-10-17 02:00:53, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2020-10-17 02:00:53, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2020-10-17 02:00:53, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Accuracy with training data: 99.2%
Out-of-bag accuracy: 92.0%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 42
    avg. # of SNPs in an individual classifier: 13.75
        (sd: 1.26, min: 12, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 29.50
        (sd: 8.35, min: 21, max: 40, median: 28.50)
    avg. out-of-bag accuracy: 91.96%
        (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Genome assembly: hg19
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 42
    avg. # of SNPs in an individual classifier: 13.75
        (sd: 1.26, min: 12, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 29.50
        (sd: 8.35, min: 21, max: 40, median: 28.50)
    avg. out-of-bag accuracy: 91.96%
        (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Genome assembly: hg19
Sat Oct 17 02:00:53 2020, passing the 1/4 classifiers.
Sat Oct 17 02:00:53 2020, passing the 2/4 classifiers.
Sat Oct 17 02:00:53 2020, passing the 3/4 classifiers.
Sat Oct 17 02:00:53 2020, passing the 4/4 classifiers.
Allele	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 92.0%, Call rate: 100.0%
01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 --
02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100)
24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 --
24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 92.0%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.7</td> <td>100.0</td> <td>95.1</td> <td>92.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.7</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.7</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.3</td> <td>100.0</td> <td>97.1</td> <td>76.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>100.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>24:02 (75)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>25:01 (83)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:01 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>

</body>
</html>
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
2020-10-17 02:00:54,   1, job  2, # of SNPs: 12, # of haplo: 53, acc: 90.9%
  --  avg out-of-bag acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91%
2020-10-17 02:00:54,   2, job  1, # of SNPs: 14, # of haplo: 70, acc: 90.9%
  --  avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
2020-10-17 02:00:54,   3, job  2, # of SNPs: 14, # of haplo: 26, acc: 90.9%
  --  avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
2020-10-17 02:00:54,   4, job  1, # of SNPs: 14, # of haplo: 21, acc: 84.6%
  --  avg out-of-bag acc: 89.34%, sd: 3.15%, min: 84.62%, max: 90.91%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003244030 0.0003397173 0.0004775462 0.0024572317 0.0132103293 0.0416021277 
        Max.         Mean           SD 
0.4789692818 0.0532492385 0.1193261417 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 89.3%
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
The model is autosaved in 'tmp_model.RData'.
2020-10-17 02:00:54,   1, job  1, # of SNPs: 11, # of haplo: 25, acc: 87.5%
  --  avg out-of-bag acc: 87.50%, sd: NA%, min: 87.50%, max: 87.50%
2020-10-17 02:00:54,   2, job  2, # of SNPs: 15, # of haplo: 38, acc: 83.3%
  --  avg out-of-bag acc: 85.42%, sd: 2.95%, min: 83.33%, max: 87.50%
2020-10-17 02:00:54,   3, job  2, # of SNPs: 12, # of haplo: 54, acc: 79.2%
Stop "job 2".
  --  avg out-of-bag acc: 83.33%, sd: 4.17%, min: 79.17%, max: 87.50%
2020-10-17 02:00:55,   4, job  1, # of SNPs: 19, # of haplo: 57, acc: 93.8%
Stop "job 1".
  --  avg out-of-bag acc: 85.94%, sd: 6.22%, min: 79.17%, max: 93.75%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 
        Max.         Mean           SD 
0.4576709956 0.0416266235 0.1076618487 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 85.9%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 47
    avg. # of SNPs in an individual classifier: 14.25
        (sd: 3.59, min: 11, max: 19, median: 13.50)
    avg. # of haplotypes in an individual classifier: 43.50
        (sd: 14.89, min: 25, max: 57, median: 46.00)
    avg. out-of-bag accuracy: 85.94%
        (sd: 6.22%, min: 79.17%, max: 93.75%, median: 85.42%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 
        Max.         Mean           SD 
0.4576709956 0.0416266235 0.1076618487 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:55)	0%
Predicting (2020-10-17 02:00:55)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (7.7%)  3 (11.5%)  7 (26.9%) 14 (53.8%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Run in parallel with 2 compute nodes.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (7.7%)  3 (11.5%)  7 (26.9%) 14 (53.8%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Exclude 11 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:55
     1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14
     2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14
     3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14
     4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15
     5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15
     6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15
     7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15
     8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17
     9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19
    10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20
    11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21
    12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27
    13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30
    14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31
[1] 2020-10-17 02:00:55, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31
     1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13
     2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14
     3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14
     4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16
     5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18
     6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18
     7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20
     8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20
     9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27
    10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27
    11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28
    12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37
    13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38
[2] 2020-10-17 02:00:55, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.000000e+00 4.210645e-09 4.210645e-08 2.324904e-03 8.109951e-03 1.989621e-02 
        Max.         Mean           SD 
5.990492e-02 1.420983e-02 1.652472e-02 
Accuracy with training data: 100.0%
Out-of-bag accuracy: 94.9%
Exclude 11 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:55
     1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13
     2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15
     3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15
     4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15
     5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15
     6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15
     7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19
     8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28
     9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29
    10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29
    11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30
[1] 2020-10-17 02:00:55, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30
     1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16
     2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22
     3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23
     4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34
     5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51
     6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53
     7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53
     8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55
     9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57
    10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89
    11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90
    12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90
    13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90
    14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90
    15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90
[2] 2020-10-17 02:00:56, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002703521 0.0002971139 0.0005379705 0.0035684045 0.0131584084 0.0413920204 
        Max.         Mean           SD 
0.5762364122 0.0439769026 0.0999476134 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 90.8%
HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:56)	0%
Predicting (2020-10-17 02:00:56)	100%
HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:56)	0%
Predicting (2020-10-17 02:00:56)	100%
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158, # of samples: 60
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:56
     1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14
     2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15
     3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19
     4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23
     5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23
     6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27
     7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35
     8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51
     9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55
    10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55
    11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56
    12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59
    13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58
    14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60
[1] 2020-10-17 02:00:56, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60
     1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15
     2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18
     3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23
     4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29
     5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40
     6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40
     7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41
     8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41
     9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43
    10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43
    11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43
    12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44
    13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45
    14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45
    15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45
    16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47
    17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47
    18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50
[2] 2020-10-17 02:00:56, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 93.2%
Gene: A
Training dataset: 60 samples X 158 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: A
Training dataset: 60 samples X 28 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Platform: Illumina 1M Duo 
Information: Training set -- HapMap Phase II 
HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2020-10-17 02:00:57)	0%
Predicting (2020-10-17 02:00:57)	100%
HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2020-10-17 02:00:57)	0%
Predicting (2020-10-17 02:00:57)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:57
     1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14
     2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14
     3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14
     4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15
     5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15
     6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15
     7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15
     8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17
     9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19
    10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20
    11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21
    12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27
    13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30
    14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31
[1] 2020-10-17 02:00:57, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31
     1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13
     2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14
     3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14
     4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16
     5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18
     6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18
     7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20
     8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20
     9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27
    10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27
    11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28
    12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37
    13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38
[2] 2020-10-17 02:00:57, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38
     1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13
     2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15
     3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15
     4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15
     5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15
     6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15
     7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19
     8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28
     9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29
    10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29
    11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30
[3] 2020-10-17 02:00:57, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30
     1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16
     2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22
     3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23
     4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34
     5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51
     6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53
     7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53
     8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55
     9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57
    10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89
    11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90
    12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90
    13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90
    14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90
    15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90
[4] 2020-10-17 02:00:57, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 92.9%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 49
    avg. # of SNPs in an individual classifier: 13.25
        (sd: 1.71, min: 11, max: 15, median: 13.50)
    avg. # of haplotypes in an individual classifier: 47.25
        (sd: 28.72, min: 30, max: 90, median: 34.50)
    avg. out-of-bag accuracy: 92.87%
        (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:57)	0%
Predicting (2020-10-17 02:00:57)	100%
Allele	Num.	Freq.	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Train	Train	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 88.5%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 --
02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100)
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50)
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\
02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 88.5%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>96.2</td> <td>100.0</td> <td>95.0</td> <td>85.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>25</td> <td>0.3676</td> <td>18</td> <td>0.3462</td> <td>100.0</td> <td>98.1</td> <td>94.4</td> <td>100.0</td> <td>100.0</td> <td>97.1</td> <td>01:01 (100)</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>2</td> <td>0.0294</td> <td>3</td> <td>0.0577</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>1</td> <td>0.0147</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>96.2</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>96.2</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>92.3</td> <td>60.0</td> <td>95.7</td> <td>60.0</td> <td>95.7</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>4</td> <td>0.0588</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>03:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>2</td> <td>0.0294</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>

</body>
</html>
**Overall accuracy: 88.5%, Call rate: 100.0%**

| Allele  | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- |
| 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) |
| 02:06 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 |  4 | 0.0588 |  5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 11:01 |  2 | 0.0294 |  3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 |  1 | 0.0147 |  2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) |
| 24:02 |  6 | 0.0882 |  5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) |
| 24:03 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 |  4 | 0.0588 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 26:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 29:02 |  3 | 0.0441 |  1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) |
| 31:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 |  2 | 0.0294 |  2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:00:58
     1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14
     2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14
     3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14
     4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15
     5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15
     6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15
     7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15
     8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17
     9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19
    10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20
    11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21
    12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27
    13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30
    14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31
[1] 2020-10-17 02:00:58, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31
     1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13
     2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14
     3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14
     4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16
     5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18
     6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18
     7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20
     8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20
     9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27
    10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27
    11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28
    12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37
    13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38
[2] 2020-10-17 02:00:58, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38
     1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13
     2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15
     3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15
     4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15
     5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15
     6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15
     7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19
     8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28
     9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29
    10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29
    11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30
[3] 2020-10-17 02:00:58, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30
     1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16
     2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22
     3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23
     4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34
     5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51
     6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53
     7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53
     8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55
     9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57
    10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89
    11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90
    12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90
    13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90
    14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90
    15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90
[4] 2020-10-17 02:00:58, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 92.9%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 49
    avg. # of SNPs in an individual classifier: 13.25
        (sd: 1.71, min: 11, max: 15, median: 13.50)
    avg. # of haplotypes in an individual classifier: 47.25
        (sd: 28.72, min: 30, max: 90, median: 34.50)
    avg. out-of-bag accuracy: 92.87%
        (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:00:58)	0%
Predicting (2020-10-17 02:00:58)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:59
     1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18
     3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19
     4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20
     5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20
     6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23
     7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25
     8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29
     9, SNP: 42, Loss: 47.0092, OOB Acc: 85.42%, # of Haplo: 37
    10, SNP: 33, Loss: 41.5486, OOB Acc: 85.42%, # of Haplo: 41
    11, SNP: 5, Loss: 39.769, OOB Acc: 85.42%, # of Haplo: 51
    12, SNP: 10, Loss: 34.0977, OOB Acc: 85.42%, # of Haplo: 51
    13, SNP: 37, Loss: 32.3969, OOB Acc: 85.42%, # of Haplo: 52
    14, SNP: 7, Loss: 28.1492, OOB Acc: 85.42%, # of Haplo: 52
    15, SNP: 15, Loss: 27.2163, OOB Acc: 85.42%, # of Haplo: 55
[1] 2020-10-17 02:00:59, OOB Acc: 85.42%, # of SNPs: 15, # of Haplo: 55
     1, SNP: 18, Loss: 453.852, OOB Acc: 44.12%, # of Haplo: 17
     2, SNP: 4, Loss: 358.517, OOB Acc: 50.00%, # of Haplo: 18
     3, SNP: 49, Loss: 258.495, OOB Acc: 52.94%, # of Haplo: 18
     4, SNP: 5, Loss: 172.555, OOB Acc: 67.65%, # of Haplo: 21
     5, SNP: 42, Loss: 144.905, OOB Acc: 76.47%, # of Haplo: 21
     6, SNP: 38, Loss: 98.7462, OOB Acc: 79.41%, # of Haplo: 21
     7, SNP: 36, Loss: 83.4743, OOB Acc: 82.35%, # of Haplo: 24
     8, SNP: 19, Loss: 60.2385, OOB Acc: 88.24%, # of Haplo: 24
     9, SNP: 46, Loss: 49.1775, OOB Acc: 88.24%, # of Haplo: 24
    10, SNP: 20, Loss: 42.3205, OOB Acc: 88.24%, # of Haplo: 24
    11, SNP: 12, Loss: 41.1299, OOB Acc: 91.18%, # of Haplo: 25
    12, SNP: 1, Loss: 33.8332, OOB Acc: 91.18%, # of Haplo: 25
    13, SNP: 37, Loss: 32.8313, OOB Acc: 91.18%, # of Haplo: 26
    14, SNP: 7, Loss: 38.8398, OOB Acc: 94.12%, # of Haplo: 27
    15, SNP: 15, Loss: 35.0817, OOB Acc: 94.12%, # of Haplo: 32
    16, SNP: 39, Loss: 33.7063, OOB Acc: 94.12%, # of Haplo: 30
[2] 2020-10-17 02:00:59, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 30
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Accuracy with training data: 95.8%
Out-of-bag accuracy: 89.8%
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 23
    avg. # of SNPs in an individual classifier: 15.50
        (sd: 0.71, min: 15, max: 16, median: 15.50)
    avg. # of haplotypes in an individual classifier: 42.50
        (sd: 17.68, min: 30, max: 55, median: 42.50)
    avg. out-of-bag accuracy: 89.77%
        (sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Genome assembly: hg19
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 1
    total # of SNPs used: 15
    avg. # of SNPs in an individual classifier: 15.00
        (sd: NA, min: 15, max: 15, median: 15.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: NA, min: 55, max: 55, median: 55.00)
    avg. out-of-bag accuracy: 85.42%
        (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:00:59
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2020-10-17 02:00:59, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
     5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
     6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
     7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
     8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
    10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
    11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
    13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
    14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
    15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
    16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
    17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
    18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2020-10-17 02:00:59, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264, # of samples: 34
# of unique HLA alleles: 14
[-] 2020-10-17 02:01:00
     1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14
     2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14
     3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14
     4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15
     5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15
     6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15
     7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15
     8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17
     9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19
    10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20
    11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21
    12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27
    13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30
    14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31
[1] 2020-10-17 02:01:00, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31
     1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13
     2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14
     3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14
     4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16
     5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18
     6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18
     7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20
     8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20
     9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27
    10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27
    11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28
    12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37
    13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38
[2] 2020-10-17 02:01:00, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38
     1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13
     2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15
     3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15
     4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15
     5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15
     6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15
     7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19
     8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28
     9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29
    10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29
    11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30
[3] 2020-10-17 02:01:00, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30
     1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16
     2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22
     3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23
     4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34
     5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51
     6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53
     7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53
     8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55
     9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57
    10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89
    11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90
    12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90
    13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90
    14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90
    15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90
[4] 2020-10-17 02:01:00, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 92.9%
Gene: A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 49
    avg. # of SNPs in an individual classifier: 13.25
        (sd: 1.71, min: 11, max: 15, median: 13.50)
    avg. # of haplotypes in an individual classifier: 47.25
        (sd: 28.72, min: 30, max: 90, median: 34.50)
    avg. out-of-bag accuracy: 92.87%
        (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 
        Max.         Mean           SD 
0.2881182061 0.0290933670 0.0516709455 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2020-10-17 02:01:00)	0%
Predicting (2020-10-17 02:01:00)	100%
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2020-10-17 02:01:00
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2020-10-17 02:01:00, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
     5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
     6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
     7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
     8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
    10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
    11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
    13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
    14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
    15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
    16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
    17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
    18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2020-10-17 02:01:01, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 31
    avg. # of SNPs in an individual classifier: 18.50
        (sd: 0.71, min: 18, max: 19, median: 18.50)
    avg. # of haplotypes in an individual classifier: 47.50
        (sd: 6.36, min: 43, max: 52, median: 47.50)
    avg. out-of-bag accuracy: 91.85%
        (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
> 
> proc.time()
   user  system elapsed 
 30.007   1.012  33.079 

Example timings

HIBAG.Rcheck/HIBAG-Ex.timings

nameusersystemelapsed
HIBAG-package0.5050.0380.569
hlaAllele0.0180.0030.021
hlaAlleleDigit0.0200.0030.023
hlaAlleleSubset0.0200.0020.022
hlaAssocTest1.2010.0381.241
hlaAttrBagging0.4330.0260.459
hlaBED2Geno0.1110.0110.121
hlaCheckAllele000
hlaCheckSNPs0.0810.0020.084
hlaCombineAllele0.0240.0030.026
hlaCombineModelObj0.3600.0060.368
hlaCompareAllele0.3260.0180.347
hlaConvSequence4.1500.4254.583
hlaDistance1.9280.0061.935
hlaErrMsg0.0010.0000.000
hlaFlankingSNP0.0130.0020.015
hlaGDS2Geno0.1090.0080.119
hlaGeno2PED0.0440.0030.047
hlaGenoAFreq0.0070.0010.007
hlaGenoCombine0.0650.0050.071
hlaGenoLD0.9580.0120.971
hlaGenoMFreq0.0040.0000.005
hlaGenoMRate0.0060.0000.006
hlaGenoMRate_Samp0.0060.0010.007
hlaGenoSubset0.0090.0010.010
hlaGenoSwitchStrand0.0490.0040.052
hlaLDMatrix1.7710.1351.909
hlaLociInfo0.0060.0050.011
hlaMakeSNPGeno0.0400.0030.044
hlaModelFiles0.2540.0090.264
hlaModelFromObj0.0820.0030.084
hlaOutOfBag0.6160.0190.635
hlaParallelAttrBagging0.2560.0342.286
hlaPredMerge0.3200.0080.328
hlaPublish0.6310.0140.646
hlaReport0.3180.0130.331
hlaReportPlot1.8740.0201.898
hlaSNPID0.0060.0000.007
hlaSampleAllele0.0090.0010.011
hlaSplitAllele0.0630.0010.064
hlaSubModelObj0.1110.0040.116
hlaUniqueAllele0.0090.0010.010
plot.hlaAttrBagObj0.3620.0050.368
predict.hlaAttrBagClass0.3320.0150.348
print.hlaAttrBagClass0.1700.0040.174
summary.hlaSNPGenoClass0.0040.0010.005