Prioritization of inbred mouse strains for resolving genetic regions

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.

Installation

if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MouseFM")

Loading package

library(MouseFM)

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/EiJ

Prioritize 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 combinations

View meta information

comment(df)
#> NULL

Extract 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 3

Create plots

plots = vis_reduction_factors(df$genotypes, df$reduction, 2)
plots[[1]]

plots[[2]]

Output of Session Info

The output of sessionInfo() on the system on which this document was compiled:

sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> 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       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] MouseFM_1.21.0   BiocStyle_2.39.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] KEGGREST_1.49.2      gtable_0.3.6         xfun_0.54           
#>  [4] bslib_0.9.0          ggplot2_4.0.0        httr2_1.2.1         
#>  [7] rlist_0.4.6.2        Biobase_2.71.0       vctrs_0.6.5         
#> [10] tools_4.5.1          generics_0.1.4       stats4_4.5.1        
#> [13] curl_7.0.0           tibble_3.3.0         AnnotationDbi_1.73.0
#> [16] RSQLite_2.4.3        blob_1.2.4           pkgconfig_2.0.3     
#> [19] data.table_1.17.8    RColorBrewer_1.1-3   dbplyr_2.5.1        
#> [22] S7_0.2.0             S4Vectors_0.49.0     lifecycle_1.0.4     
#> [25] farver_2.1.2         compiler_4.5.1       stringr_1.5.2       
#> [28] Biostrings_2.79.1    progress_1.2.3       Seqinfo_1.1.0       
#> [31] htmltools_0.5.8.1    sys_3.4.3            buildtools_1.0.0    
#> [34] sass_0.4.10          yaml_2.3.10          tidyr_1.3.1         
#> [37] pillar_1.11.1        crayon_1.5.3         jquerylib_0.1.4     
#> [40] cachem_1.1.0         gtools_3.9.5         tidyselect_1.2.1    
#> [43] digest_0.6.37        stringi_1.8.7        purrr_1.1.0         
#> [46] reshape2_1.4.4       dplyr_1.1.4          maketools_1.3.2     
#> [49] biomaRt_2.67.0       fastmap_1.2.0        grid_4.5.1          
#> [52] cli_3.6.5            magrittr_2.0.4       withr_3.0.2         
#> [55] scales_1.4.0         prettyunits_1.2.0    filelock_1.0.3      
#> [58] rappdirs_0.3.3       bit64_4.6.0-1        rmarkdown_2.30      
#> [61] XVector_0.51.0       httr_1.4.7           bit_4.6.0           
#> [64] png_0.1-8            hms_1.1.4            memoise_2.0.1       
#> [67] evaluate_1.0.5       knitr_1.50           GenomicRanges_1.63.0
#> [70] IRanges_2.45.0       BiocFileCache_3.1.0  rlang_1.1.6         
#> [73] Rcpp_1.1.0           glue_1.8.0           DBI_1.2.3           
#> [76] BiocManager_1.30.26  BiocGenerics_0.57.0  jsonlite_2.0.0      
#> [79] plyr_1.8.9           R6_2.6.1