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)
#>
#> ---------
#>
#> For example usage please run: vignette('MouseFM')
#>
#> Github Repo: https://github.com/matmu/MouseFM
#> MouseFM Backend: https://github.com/matmu/MouseFM-Backend
#>
#> Citation appreciated:
#> Munz M, Khodaygani M, Aherrahrou Z, Busch H, Wohlers I (2021) In silico candidate variant and gene identification using inbred mouse strains. PeerJ. doi:10.7717/peerj.11017
#>
#> ---------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 BUB_BnJ C3H_HeH
#> 1 0 0 0 0 0 0 0 0 1
#> 2 0 0 0 0 0 0 0 0 1
#> 3 0 0 0 0 0 0 0 0 1
#> 4 0 0 0 0 0 0 0 0 1
#> 5 0 0 0 0 0 0 0 0 1
#> 6 0 0 0 0 0 0 0 0 1
#> 7 0 0 0 0 0 0 0 0 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 1
#> 10 0 0 0 0 0 0 0 0 0
#> C3H_HeJ C57BL_10J C57BL_6NJ C57BR_cdJ C57L_J C58_J CAST_EiJ CBA_J DBA_1J
#> 1 1 0 0 0 0 0 1 1 1
#> 2 1 0 0 0 0 0 0 1 1
#> 3 1 0 0 0 0 0 0 1 1
#> 4 1 0 0 0 0 0 0 1 1
#> 5 1 0 0 0 0 0 0 1 1
#> 6 1 0 0 0 0 0 0 1 1
#> 7 1 0 0 0 0 0 0 1 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 1 0 0 0 0 0 0 1 0
#> 10 0 0 0 0 0 0 0 0 0
#> DBA_2J FVB_NJ I_LnJ KK_HiJ LEWES_EiJ LP_J MOLF_EiJ NOD_ShiLtJ NZB_B1NJ
#> 1 1 0 0 0 1 0 0 0 1
#> 2 1 0 0 0 1 0 0 0 0
#> 3 1 0 0 0 1 0 0 0 0
#> 4 1 0 0 0 1 0 0 0 0
#> 5 1 0 0 0 1 0 0 0 0
#> 6 1 0 0 0 1 0 0 0 0
#> 7 1 0 0 0 1 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 1
#> 9 0 0 0 0 1 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 1
#> NZO_HlLtJ NZW_LacJ PWK_PhJ RF_J SEA_GnJ SPRET_EiJ ST_bJ WSB_EiJ ZALENDE_EiJ
#> 1 0 0 1 1 0 1 0 1 1
#> 2 0 0 1 1 0 1 0 1 1
#> 3 0 0 1 1 0 1 0 1 1
#> 4 0 0 1 1 0 1 0 1 1
#> 5 0 0 1 1 0 0 0 1 1
#> 6 0 0 1 1 0 1 0 1 1
#> 7 0 0 1 1 0 1 0 1 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 1 0 0 0 1 1
#> 10 0 0 0 0 0 0 0 0 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 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.12.0 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.42.0 gtable_0.3.4 ggplot2_3.4.4
#> [4] xfun_0.40 bslib_0.5.1 rlist_0.4.6.2
#> [7] Biobase_2.62.0 vctrs_0.6.4 tools_4.3.1
#> [10] bitops_1.0-7 generics_0.1.3 curl_5.1.0
#> [13] stats4_4.3.1 tibble_3.2.1 fansi_1.0.5
#> [16] AnnotationDbi_1.64.0 RSQLite_2.3.1 blob_1.2.4
#> [19] pkgconfig_2.0.3 data.table_1.14.8 dbplyr_2.3.4
#> [22] S4Vectors_0.40.0 lifecycle_1.0.3 GenomeInfoDbData_1.2.11
#> [25] compiler_4.3.1 stringr_1.5.0 Biostrings_2.70.0
#> [28] progress_1.2.2 munsell_0.5.0 GenomeInfoDb_1.38.0
#> [31] htmltools_0.5.6.1 sass_0.4.7 RCurl_1.98-1.12
#> [34] yaml_2.3.7 tidyr_1.3.0 pillar_1.9.0
#> [37] crayon_1.5.2 jquerylib_0.1.4 cachem_1.0.8
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#> [49] biomaRt_2.58.0 fastmap_1.1.1 colorspace_2.1-0
#> [52] cli_3.6.1 magrittr_2.0.3 XML_3.99-0.14
#> [55] utf8_1.2.4 scales_1.2.1 rappdirs_0.3.3
#> [58] filelock_1.0.2 prettyunits_1.2.0 bit64_4.0.5
#> [61] rmarkdown_2.25 XVector_0.42.0 httr_1.4.7
#> [64] bit_4.0.5 png_0.1-8 hms_1.1.3
#> [67] memoise_2.0.1 evaluate_0.22 knitr_1.44
#> [70] GenomicRanges_1.54.0 IRanges_2.36.0 BiocFileCache_2.10.0
#> [73] rlang_1.1.1 Rcpp_1.0.11 glue_1.6.2
#> [76] DBI_1.1.3 xml2_1.3.5 BiocManager_1.30.22
#> [79] BiocGenerics_0.48.0 jsonlite_1.8.7 plyr_1.8.9
#> [82] R6_2.5.1 zlibbioc_1.48.0