Prioritization of inbred mouse strains for resolving genetic regions
15 April 2025
Contents
1 Introduction
This R package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).
Method prio allows to select strain combinations which best refine a specified genetic region. E.g. if a crossing experiment with two inbred mouse strains ‘strain1’ and ‘strain2’ resulted in a QTL, the outputted strain combinations can be used to refine the respective region in further crossing experiments and to select candidate genes.
2 Installation
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MouseFM")3 Loading package
library(MouseFM)4 Example function calls
Available mouse strains
avail_strains()
#> id strain
#> 1 129P2_OlaHsd 129P2/OlaHsd
#> 2 129S1_SvImJ 129S1/SvImJ
#> 3 129S5SvEvBrd 129S5/SvEvBrd
#> 4 A_J A/J
#> 5 AKR_J AKR/J
#> 6 BALB_cJ BALB/cJ
#> 7 BTBR BTBR
#> 8 BUB_BnJ BUB/BnJ
#> 9 C3H_HeH C3H/HeH
#> 10 C3H_HeJ C3H/HeJ
#> 11 C57BL_10J C57BL/10J
#> 12 C57BL_6J C57BL/6J
#> 13 C57BL_6NJ C57BL/6NJ
#> 14 C57BR_cdJ C57BR/cdJ
#> 15 C57L_J C57L/J
#> 16 C58_J C58/J
#> 17 CAST_EiJ CAST/EiJ
#> 18 CBA_J CBA/J
#> 19 DBA_1J DBA/1J
#> 20 DBA_2J DBA/2J
#> 21 FVB_NJ FVB/NJ
#> 22 I_LnJ I/LnJ
#> 23 KK_HiJ KK/HiJ
#> 24 LEWES_EiJ LEWES/EiJ
#> 25 LP_J LP/J
#> 26 MOLF_EiJ MOLF/EiJ
#> 27 NOD_ShiLtJ NOD/ShiLtJ
#> 28 NZB_B1NJ NZB/B1NJ
#> 29 NZO_HlLtJ NZO/HlLtJ
#> 30 NZW_LacJ NZW/LacJ
#> 31 PWK_PhJ PWK/PhJ
#> 32 RF_J RF/J
#> 33 SEA_GnJ SEA/GnJ
#> 34 SPRET_EiJ SPRET/EiJ
#> 35 ST_bJ ST/bJ
#> 36 WSB_EiJ WSB/EiJ
#> 37 ZALENDE_EiJ ZALENDE/EiJPrioritize additional mouse strains for a given region which was identified in a crossing experiment with strain1 C57BL_6J and strain2 AKR_J.
df = prio("chr1", start=5000000, end=6000000, strain1="C57BL_6J", strain2="AKR_J")
#> Query chr1:5,000,000-6,000,000
#> Calculate reduction factors...
#> Set size 1: 35 combinations
#> Set size 1: continue with 20 of 35 strains
#> Set size 2: 190 combinations
#> Set size 3: 1,140 combinationsView meta information
comment(df)
#> NULLExtract the combinations with the best refinement
get_top(df$reduction, n_top=3)
#> strain1 strain2 combination mean min max n
#> 8 C57BL_6J AKR_J C3H_HeH,DBA_1J,SPRET_EiJ 0.8068057 0.7467057 0.9926794 3
#> 7 C57BL_6J AKR_J C3H_HeH,DBA_2J,SPRET_EiJ 0.8068057 0.7467057 0.9926794 3
#> 6 C57BL_6J AKR_J C3H_HeJ,DBA_1J,SPRET_EiJ 0.8068057 0.7467057 0.9926794 3Create plots
plots = vis_reduction_factors(df$genotypes, df$reduction, 2)
plots[[1]]
plots[[2]]
5 Output of Session Info
The output of sessionInfo() on the system
on which this document was compiled:
sessionInfo()
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> 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_1.18.0 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 farver_2.1.2
#> [25] compiler_4.5.0 stringr_1.5.1 Biostrings_2.76.0
#> [28] progress_1.2.3 tinytex_0.57 munsell_0.5.1
#> [31] GenomeInfoDb_1.44.0 htmltools_0.5.8.1 sass_0.4.10
#> [34] yaml_2.3.10 tidyr_1.3.1 pillar_1.10.2
#> [37] crayon_1.5.3 jquerylib_0.1.4 cachem_1.1.0
#> [40] magick_2.8.6 gtools_3.9.5 tidyselect_1.2.1
#> [43] digest_0.6.37 stringi_1.8.7 purrr_1.0.4
#> [46] reshape2_1.4.4 dplyr_1.1.4 bookdown_0.43
#> [49] grid_4.5.0 biomaRt_2.64.0 fastmap_1.2.0
#> [52] colorspace_2.1-1 cli_3.6.4 magrittr_2.0.3
#> [55] withr_3.0.2 scales_1.3.0 prettyunits_1.2.0
#> [58] filelock_1.0.3 UCSC.utils_1.4.0 rappdirs_0.3.3
#> [61] bit64_4.6.0-1 rmarkdown_2.29 XVector_0.48.0
#> [64] httr_1.4.7 bit_4.6.0 png_0.1-8
#> [67] hms_1.1.3 memoise_2.0.1 evaluate_1.0.3
#> [70] knitr_1.50 GenomicRanges_1.60.0 IRanges_2.42.0
#> [73] BiocFileCache_2.16.0 rlang_1.1.6 Rcpp_1.0.14
#> [76] glue_1.8.0 DBI_1.2.3 xml2_1.3.8
#> [79] BiocManager_1.30.25 BiocGenerics_0.54.0 jsonlite_2.0.0
#> [82] plyr_1.8.9 R6_2.6.1