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.
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MouseFM")
library(MouseFM)
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]]
The output of sessionInfo()
on the system
on which this document was compiled:
sessionInfo()
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
#>
#> 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
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.0.0 BiocStyle_2.18.0
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 tidyr_1.1.2 gtools_3.8.2
#> [4] prettyunits_1.1.1 assertthat_0.2.1 digest_0.6.27
#> [7] BiocFileCache_1.14.0 plyr_1.8.6 R6_2.4.1
#> [10] GenomeInfoDb_1.26.0 stats4_4.0.3 RSQLite_2.2.1
#> [13] evaluate_0.14 httr_1.4.2 ggplot2_3.3.2
#> [16] pillar_1.4.6 zlibbioc_1.36.0 rlang_0.4.8
#> [19] progress_1.2.2 curl_4.3 data.table_1.13.2
#> [22] blob_1.2.1 magick_2.5.0 S4Vectors_0.28.0
#> [25] rmarkdown_2.5 stringr_1.4.0 RCurl_1.98-1.2
#> [28] bit_4.0.4 biomaRt_2.46.0 munsell_0.5.0
#> [31] compiler_4.0.3 xfun_0.18 pkgconfig_2.0.3
#> [34] askpass_1.1 BiocGenerics_0.36.0 htmltools_0.5.0
#> [37] openssl_1.4.3 tidyselect_1.1.0 tibble_3.0.4
#> [40] GenomeInfoDbData_1.2.4 bookdown_0.21 IRanges_2.24.0
#> [43] XML_3.99-0.5 crayon_1.3.4 dplyr_1.0.2
#> [46] dbplyr_1.4.4 bitops_1.0-6 rappdirs_0.3.1
#> [49] grid_4.0.3 jsonlite_1.7.1 gtable_0.3.0
#> [52] lifecycle_0.2.0 DBI_1.1.0 magrittr_1.5
#> [55] scales_1.1.1 rlist_0.4.6.1 stringi_1.5.3
#> [58] farver_2.0.3 reshape2_1.4.4 XVector_0.30.0
#> [61] xml2_1.3.2 ellipsis_0.3.1 vctrs_0.3.4
#> [64] generics_0.0.2 tools_4.0.3 bit64_4.0.5
#> [67] Biobase_2.50.0 glue_1.4.2 purrr_0.3.4
#> [70] hms_0.5.3 parallel_4.0.3 yaml_2.2.1
#> [73] AnnotationDbi_1.52.0 colorspace_1.4-1 BiocManager_1.30.10
#> [76] GenomicRanges_1.42.0 memoise_1.1.0 knitr_1.30