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 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-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_1.14.6   BiocStyle_2.32.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1        dplyr_1.1.4             farver_2.1.2           
#>  [4] blob_1.2.4              filelock_1.0.3          Biostrings_2.72.1      
#>  [7] fastmap_1.2.0           BiocFileCache_2.12.0    digest_0.6.37          
#> [10] lifecycle_1.0.4         KEGGREST_1.44.1         RSQLite_2.3.7          
#> [13] magrittr_2.0.3          compiler_4.4.1          rlang_1.1.4            
#> [16] sass_0.4.9              progress_1.2.3          tools_4.4.1            
#> [19] utf8_1.2.4              yaml_2.3.10             data.table_1.16.0      
#> [22] knitr_1.48              prettyunits_1.2.0       bit_4.0.5              
#> [25] curl_5.2.2              plyr_1.8.9              xml2_1.3.6             
#> [28] withr_3.0.1             purrr_1.0.2             BiocGenerics_0.50.0    
#> [31] grid_4.4.1              stats4_4.4.1            fansi_1.0.6            
#> [34] colorspace_2.1-1        ggplot2_3.5.1           scales_1.3.0           
#> [37] gtools_3.9.5            tinytex_0.52            biomaRt_2.60.1         
#> [40] cli_3.6.3               rmarkdown_2.28          crayon_1.5.3           
#> [43] generics_0.1.3          rlist_0.4.6.2           httr_1.4.7             
#> [46] reshape2_1.4.4          DBI_1.2.3               cachem_1.1.0           
#> [49] stringr_1.5.1           zlibbioc_1.50.0         AnnotationDbi_1.66.0   
#> [52] BiocManager_1.30.25     XVector_0.44.0          vctrs_0.6.5            
#> [55] jsonlite_1.8.8          bookdown_0.40           IRanges_2.38.1         
#> [58] hms_1.1.3               S4Vectors_0.42.1        bit64_4.0.5            
#> [61] magick_2.8.4            jquerylib_0.1.4         tidyr_1.3.1            
#> [64] glue_1.7.0              stringi_1.8.4           gtable_0.3.5           
#> [67] GenomeInfoDb_1.40.1     GenomicRanges_1.56.1    UCSC.utils_1.0.0       
#> [70] munsell_0.5.1           tibble_3.2.1            pillar_1.9.0           
#> [73] rappdirs_0.3.3          htmltools_0.5.8.1       GenomeInfoDbData_1.2.12
#> [76] R6_2.5.1                dbplyr_2.5.0            httr2_1.0.3            
#> [79] evaluate_0.24.0         Biobase_2.64.0          highr_0.11             
#> [82] png_0.1-8               memoise_2.0.1           bslib_0.8.0            
#> [85] Rcpp_1.0.13             xfun_0.47               pkgconfig_2.0.3