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')
#>
#> Support me: http://matthiasmunz.de/support_me
#>
#> 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
#>
#> Github Repo: https://github.com/matmu/MouseFM
#> MouseFM Backend: https://github.com/matmu/MouseFM-Backend
#> ---------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 4.5.0 RC (2025-04-04 r88126 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.18.0 BiocStyle_2.36.0
#>
#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.48.0 gtable_0.3.6 ggplot2_3.5.2
#> [4] xfun_0.52 bslib_0.9.0 httr2_1.1.2
#> [7] rlist_0.4.6.2 Biobase_2.68.0 vctrs_0.6.5
#> [10] tools_4.5.0 generics_0.1.3 stats4_4.5.0
#> [13] curl_6.2.2 tibble_3.2.1 AnnotationDbi_1.70.0
#> [16] RSQLite_2.3.9 blob_1.2.4 pkgconfig_2.0.3
#> [19] data.table_1.17.0 dbplyr_2.5.0 S4Vectors_0.46.0
#> [22] lifecycle_1.0.4 GenomeInfoDbData_1.2.14 compiler_4.5.0
#> [25] stringr_1.5.1 Biostrings_2.76.0 progress_1.2.3
#> [28] munsell_0.5.1 GenomeInfoDb_1.44.0 htmltools_0.5.8.1
#> [31] sass_0.4.10 yaml_2.3.10 tidyr_1.3.1
#> [34] pillar_1.10.2 crayon_1.5.3 jquerylib_0.1.4
#> [37] cachem_1.1.0 gtools_3.9.5 tidyselect_1.2.1
#> [40] digest_0.6.37 stringi_1.8.7 purrr_1.0.4
#> [43] reshape2_1.4.4 dplyr_1.1.4 bookdown_0.43
#> [46] grid_4.5.0 biomaRt_2.64.0 fastmap_1.2.0
#> [49] colorspace_2.1-1 cli_3.6.4 magrittr_2.0.3
#> [52] scales_1.3.0 prettyunits_1.2.0 filelock_1.0.3
#> [55] UCSC.utils_1.4.0 rappdirs_0.3.3 bit64_4.6.0-1
#> [58] rmarkdown_2.29 XVector_0.48.0 httr_1.4.7
#> [61] bit_4.6.0 png_0.1-8 hms_1.1.3
#> [64] memoise_2.0.1 evaluate_1.0.3 knitr_1.50
#> [67] GenomicRanges_1.60.0 IRanges_2.42.0 BiocFileCache_2.16.0
#> [70] rlang_1.1.6 Rcpp_1.0.14 glue_1.8.0
#> [73] DBI_1.2.3 xml2_1.3.8 BiocManager_1.30.25
#> [76] BiocGenerics_0.54.0 jsonlite_2.0.0 plyr_1.8.9
#> [79] R6_2.6.1