This R package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).
Method fetch allows to download homozygous genotypes of 37 inbred mouse strains for a given genetic region.
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MouseFM")library(MouseFM)Fetch genotypes for region chr1:5000000-6000000.
df = fetch("chr1", start=5000000, end=6000000)
#> Query chr1:5,000,000-6,000,000
df[1:10,]
#> chr pos rsid ref alt most_severe_consequence
#> 1 1 5000016 rs47088541 A T intron_variant
#> 2 1 5000029 rs48827827 G A intron_variant
#> 3 1 5000057 rs48099867 C T intron_variant
#> 4 1 5000062 rs246021564 G C intron_variant
#> 5 1 5000067 rs265132353 C T intron_variant
#> 6 1 5000068 rs51419610 A G intron_variant
#> 7 1 5000101 rs253320650 C G intron_variant
#> 8 1 5000156 <NA> C T intron_variant
#> 9 1 5000157 rs216747169 G A intron_variant
#> 10 1 5000240 <NA> T G intron_variant
#> consequences C57BL_6J
#> 1 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 2 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 3 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 4 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 5 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 6 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 7 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 8 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 9 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 10 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 129P2_OlaHsd 129S1_SvImJ 129S5SvEvBrd AKR_J A_J BALB_cJ BTBR_Tplus_Itpr3tf_J
#> 1 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0
#> BUB_BnJ C3H_HeH C3H_HeJ C57BL_10J C57BL_6NJ C57BR_cdJ C57L_J C58_J CAST_EiJ
#> 1 0 1 1 0 0 0 0 0 1
#> 2 0 1 1 0 0 0 0 0 0
#> 3 0 1 1 0 0 0 0 0 0
#> 4 0 1 1 0 0 0 0 0 0
#> 5 0 1 1 0 0 0 0 0 0
#> 6 0 1 1 0 0 0 0 0 0
#> 7 0 1 1 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0
#> 9 0 1 1 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0
#> CBA_J DBA_1J DBA_2J FVB_NJ I_LnJ KK_HiJ LEWES_EiJ LP_J MOLF_EiJ NOD_ShiLtJ
#> 1 1 1 1 0 0 0 1 0 0 0
#> 2 1 1 1 0 0 0 1 0 0 0
#> 3 1 1 1 0 0 0 1 0 0 0
#> 4 1 1 1 0 0 0 1 0 0 0
#> 5 1 1 1 0 0 0 1 0 0 0
#> 6 1 1 1 0 0 0 1 0 0 0
#> 7 1 1 1 0 0 0 1 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0
#> 9 1 0 0 0 0 0 1 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0
#> NZB_B1NJ NZO_HlLtJ NZW_LacJ PWK_PhJ RF_J SEA_GnJ SPRET_EiJ ST_bJ WSB_EiJ
#> 1 1 0 0 1 1 0 1 0 1
#> 2 0 0 0 1 1 0 1 0 1
#> 3 0 0 0 1 1 0 1 0 1
#> 4 0 0 0 1 1 0 1 0 1
#> 5 0 0 0 1 1 0 0 0 1
#> 6 0 0 0 1 1 0 1 0 1
#> 7 0 0 0 1 1 0 1 0 1
#> 8 1 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 1 0 0 0 1
#> 10 1 0 0 0 0 0 0 0 0
#> ZALENDE_EiJ
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 0
#> 9 1
#> 10 0View meta information
comment(df)
#> [1] "#Alleles of strain C57BL_6J represent the reference (ref) alleles"
#> [2] "#reference_version=GRCm38"The output of sessionInfo() on the system
on which this document was compiled:
sessionInfo()
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.0.0 BiocStyle_2.18.0
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 tidyr_1.1.2 gtools_3.8.2
#> [4] prettyunits_1.1.1 assertthat_0.2.1 digest_0.6.27
#> [7] BiocFileCache_1.14.0 plyr_1.8.6 R6_2.4.1
#> [10] GenomeInfoDb_1.26.0 stats4_4.0.3 RSQLite_2.2.1
#> [13] evaluate_0.14 httr_1.4.2 ggplot2_3.3.2
#> [16] pillar_1.4.6 zlibbioc_1.36.0 rlang_0.4.8
#> [19] progress_1.2.2 curl_4.3 data.table_1.13.2
#> [22] blob_1.2.1 S4Vectors_0.28.0 rmarkdown_2.5
#> [25] stringr_1.4.0 RCurl_1.98-1.2 bit_4.0.4
#> [28] biomaRt_2.46.0 munsell_0.5.0 compiler_4.0.3
#> [31] xfun_0.18 pkgconfig_2.0.3 askpass_1.1
#> [34] BiocGenerics_0.36.0 htmltools_0.5.0 openssl_1.4.3
#> [37] tidyselect_1.1.0 tibble_3.0.4 GenomeInfoDbData_1.2.4
#> [40] bookdown_0.21 IRanges_2.24.0 XML_3.99-0.5
#> [43] crayon_1.3.4 dplyr_1.0.2 dbplyr_1.4.4
#> [46] bitops_1.0-6 rappdirs_0.3.1 grid_4.0.3
#> [49] jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
#> [52] DBI_1.1.0 magrittr_1.5 scales_1.1.1
#> [55] rlist_0.4.6.1 stringi_1.5.3 reshape2_1.4.4
#> [58] XVector_0.30.0 xml2_1.3.2 ellipsis_0.3.1
#> [61] vctrs_0.3.4 generics_0.0.2 tools_4.0.3
#> [64] bit64_4.0.5 Biobase_2.50.0 glue_1.4.2
#> [67] purrr_0.3.4 hms_0.5.3 parallel_4.0.3
#> [70] yaml_2.2.1 AnnotationDbi_1.52.0 colorspace_1.4-1
#> [73] BiocManager_1.30.10 GenomicRanges_1.42.0 memoise_1.1.0
#> [76] knitr_1.30