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/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]]

5 Output of Session Info

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

sessionInfo()
#> R version 4.4.0 alpha (2024-03-27 r86216)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] MouseFM_1.14.0   BiocStyle_2.32.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1        dplyr_1.1.4             farver_2.1.1           
#>  [4] blob_1.2.4              filelock_1.0.3          Biostrings_2.72.0      
#>  [7] fastmap_1.1.1           BiocFileCache_2.12.0    digest_0.6.35          
#> [10] lifecycle_1.0.4         KEGGREST_1.44.0         RSQLite_2.3.5          
#> [13] magrittr_2.0.3          compiler_4.4.0          rlang_1.1.3            
#> [16] sass_0.4.9              progress_1.2.3          tools_4.4.0            
#> [19] utf8_1.2.4              yaml_2.3.8              data.table_1.15.4      
#> [22] knitr_1.45              prettyunits_1.2.0       bit_4.0.5              
#> [25] curl_5.2.1              plyr_1.8.9              xml2_1.3.6             
#> [28] withr_3.0.0             purrr_1.0.2             BiocGenerics_0.50.0    
#> [31] grid_4.4.0              stats4_4.4.0            fansi_1.0.6            
#> [34] colorspace_2.1-0        ggplot2_3.5.0           scales_1.3.0           
#> [37] gtools_3.9.5            biomaRt_2.60.0          cli_3.6.2              
#> [40] rmarkdown_2.26          crayon_1.5.2            generics_0.1.3         
#> [43] rlist_0.4.6.2           httr_1.4.7              reshape2_1.4.4         
#> [46] DBI_1.2.2               cachem_1.0.8            stringr_1.5.1          
#> [49] zlibbioc_1.50.0         AnnotationDbi_1.66.0    BiocManager_1.30.22    
#> [52] XVector_0.44.0          vctrs_0.6.5             jsonlite_1.8.8         
#> [55] bookdown_0.38           IRanges_2.38.0          hms_1.1.3              
#> [58] S4Vectors_0.42.0        bit64_4.0.5             magick_2.8.3           
#> [61] jquerylib_0.1.4         tidyr_1.3.1             glue_1.7.0             
#> [64] stringi_1.8.3           gtable_0.3.4            GenomeInfoDb_1.40.0    
#> [67] GenomicRanges_1.56.0    UCSC.utils_1.0.0        munsell_0.5.0          
#> [70] tibble_3.2.1            pillar_1.9.0            rappdirs_0.3.3         
#> [73] htmltools_0.5.8         GenomeInfoDbData_1.2.12 R6_2.5.1               
#> [76] dbplyr_2.5.0            httr2_1.0.0             evaluate_0.23          
#> [79] Biobase_2.64.0          highr_0.10              png_0.1-8              
#> [82] memoise_2.0.1           bslib_0.6.2             Rcpp_1.0.12            
#> [85] xfun_0.43               pkgconfig_2.0.3