Prioritization of inbred mouse strains for resolving genetic regions
24 October 2023
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 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.18-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_1.12.0 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.0 dplyr_1.1.3 farver_2.1.1
#> [4] blob_1.2.4 filelock_1.0.2 Biostrings_2.70.0
#> [7] bitops_1.0-7 fastmap_1.1.1 RCurl_1.98-1.12
#> [10] BiocFileCache_2.10.0 XML_3.99-0.14 digest_0.6.33
#> [13] lifecycle_1.0.3 KEGGREST_1.42.0 RSQLite_2.3.1
#> [16] magrittr_2.0.3 compiler_4.3.1 rlang_1.1.1
#> [19] sass_0.4.7 progress_1.2.2 tools_4.3.1
#> [22] utf8_1.2.4 yaml_2.3.7 data.table_1.14.8
#> [25] knitr_1.44 prettyunits_1.2.0 bit_4.0.5
#> [28] curl_5.1.0 plyr_1.8.9 xml2_1.3.5
#> [31] withr_2.5.1 purrr_1.0.2 BiocGenerics_0.48.0
#> [34] grid_4.3.1 stats4_4.3.1 fansi_1.0.5
#> [37] colorspace_2.1-0 ggplot2_3.4.4 scales_1.2.1
#> [40] gtools_3.9.4 biomaRt_2.58.0 cli_3.6.1
#> [43] rmarkdown_2.25 crayon_1.5.2 generics_0.1.3
#> [46] rlist_0.4.6.2 httr_1.4.7 reshape2_1.4.4
#> [49] DBI_1.1.3 cachem_1.0.8 stringr_1.5.0
#> [52] zlibbioc_1.48.0 AnnotationDbi_1.64.0 BiocManager_1.30.22
#> [55] XVector_0.42.0 vctrs_0.6.4 jsonlite_1.8.7
#> [58] bookdown_0.36 IRanges_2.36.0 hms_1.1.3
#> [61] S4Vectors_0.40.0 bit64_4.0.5 magick_2.8.1
#> [64] jquerylib_0.1.4 tidyr_1.3.0 glue_1.6.2
#> [67] stringi_1.7.12 gtable_0.3.4 GenomeInfoDb_1.38.0
#> [70] GenomicRanges_1.54.0 munsell_0.5.0 tibble_3.2.1
#> [73] pillar_1.9.0 rappdirs_0.3.3 htmltools_0.5.6.1
#> [76] GenomeInfoDbData_1.2.11 R6_2.5.1 dbplyr_2.3.4
#> [79] evaluate_0.22 Biobase_2.62.0 png_0.1-8
#> [82] memoise_2.0.1 bslib_0.5.1 Rcpp_1.0.11
#> [85] xfun_0.40 pkgconfig_2.0.3