Prioritization of inbred mouse strains for resolving genetic regions
1 March 2022
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 4.1.2 (2021-11-01)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
#>
#> 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
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.4.2 BiocStyle_2.22.0
#>
#> loaded via a namespace (and not attached):
#> [1] Biobase_2.54.0 httr_1.4.2 tidyr_1.2.0
#> [4] sass_0.4.0 bit64_4.0.5 jsonlite_1.8.0
#> [7] gtools_3.9.2 bslib_0.3.1 assertthat_0.2.1
#> [10] highr_0.9 BiocManager_1.30.16 stats4_4.1.2
#> [13] BiocFileCache_2.2.1 blob_1.2.2 GenomeInfoDbData_1.2.7
#> [16] yaml_2.3.5 progress_1.2.2 pillar_1.7.0
#> [19] RSQLite_2.2.10 rlist_0.4.6.2 glue_1.6.2
#> [22] digest_0.6.29 GenomicRanges_1.46.1 XVector_0.34.0
#> [25] colorspace_2.0-3 plyr_1.8.6 htmltools_0.5.2
#> [28] XML_3.99-0.9 pkgconfig_2.0.3 biomaRt_2.50.3
#> [31] magick_2.7.3 bookdown_0.24 zlibbioc_1.40.0
#> [34] purrr_0.3.4 scales_1.1.1 tibble_3.1.6
#> [37] KEGGREST_1.34.0 farver_2.1.0 generics_0.1.2
#> [40] IRanges_2.28.0 ggplot2_3.3.5 ellipsis_0.3.2
#> [43] cachem_1.0.6 BiocGenerics_0.40.0 cli_3.2.0
#> [46] magrittr_2.0.2 crayon_1.5.0 memoise_2.0.1
#> [49] evaluate_0.15 fansi_1.0.2 xml2_1.3.3
#> [52] tools_4.1.2 data.table_1.14.2 prettyunits_1.1.1
#> [55] hms_1.1.1 lifecycle_1.0.1 stringr_1.4.0
#> [58] S4Vectors_0.32.3 munsell_0.5.0 AnnotationDbi_1.56.2
#> [61] Biostrings_2.62.0 compiler_4.1.2 jquerylib_0.1.4
#> [64] GenomeInfoDb_1.30.1 rlang_1.0.1 grid_4.1.2
#> [67] RCurl_1.98-1.6 rappdirs_0.3.3 bitops_1.0-7
#> [70] rmarkdown_2.11 gtable_0.3.0 DBI_1.1.2
#> [73] curl_4.3.2 reshape2_1.4.4 R6_2.5.1
#> [76] knitr_1.37 dplyr_1.0.8 fastmap_1.1.0
#> [79] bit_4.0.4 utf8_1.2.2 filelock_1.0.2
#> [82] stringi_1.7.6 Rcpp_1.0.8 vctrs_0.3.8
#> [85] png_0.1-7 dbplyr_2.1.1 tidyselect_1.1.2
#> [88] xfun_0.29