This R package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).
For one ore more chromosomal regions (GRCm38), method finemap extracts homozygous SNVs for which the allele differs between two sets of strains (e.g. case vs controls) and outputs respective causal SNV/gene candidates.
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/EiJCall finemap to finemap a specific region
res = finemap(chr="chr7",
strain1=c("C57BL_6J","C57L_J","CBA_J","NZB_B1NJ"),
strain2=c("C3H_HEJ","MOLF_EiJ","NZW_LacJ","WSB_EiJ","SPRET_EiJ"),
impact=c("HIGH", "MODERATE", "LOW"))
#> Query chr7
res[1:10,]
#> chr pos rsid ref alt
#> 1 7 45666192 rs51324364 A G
#> 2 7 45853238 rs47469186 T C
#> 3 7 45858570 rs47348864 A C
#> 4 7 45977282 rs32753716 A G
#> 5 7 45996764 rs32757904 T C
#> 6 7 45996772 rs51886013 A G
#> 7 7 45998716 rs32753986 A G
#> 8 7 46029114 rs46389823 A G
#> 9 7 46068710 rs32761224 A C
#> 10 7 46081400 rs108318096 T C
#> consequences
#> 1 non_coding_transcript_exon_variant,non_coding_transcript_variant,intron_variant,splice_region_variant
#> 2 synonymous_variant
#> 3 intron_variant,splice_region_variant
#> 4 upstream_gene_variant,non_coding_transcript_exon_variant,downstream_gene_variant,synonymous_variant
#> 5 non_coding_transcript_exon_variant,missense_variant
#> 6 non_coding_transcript_exon_variant,synonymous_variant
#> 7 non_coding_transcript_exon_variant,synonymous_variant
#> 8 upstream_gene_variant,synonymous_variant
#> 9 missense_variant
#> 10 upstream_gene_variant,synonymous_variant
#> C57BL_6J C3H_HeJ C57L_J CBA_J MOLF_EiJ NZB_B1NJ NZW_LacJ SPRET_EiJ WSB_EiJ
#> 1 0 1 0 0 1 0 1 1 1
#> 2 0 1 0 0 1 0 1 1 1
#> 3 0 1 0 0 1 0 1 1 1
#> 4 0 1 0 0 1 0 1 1 1
#> 5 0 1 0 0 1 0 1 1 1
#> 6 0 1 0 0 1 0 1 1 1
#> 7 0 1 0 0 1 0 1 1 1
#> 8 0 1 0 0 1 0 1 1 1
#> 9 0 1 0 0 1 0 1 1 1
#> 10 0 1 0 0 1 0 1 1 1View meta information
comment(res)
#> [1] "#Alleles of strain C57BL_6J represent the reference (ref) alleles"
#> [2] "#reference_version=GRCm38"Annotate with consequences (Not recommended for large queries!)
cons = annotate_consequences(res, "mouse")Annotate with genes
genes = annotate_mouse_genes(res, flanking = 50000)The output of sessionInfo() on the system
on which this document was compiled:
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-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_1.8.0 BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
#> [1] Biobase_2.58.0 httr_1.4.4 tidyr_1.2.1
#> [4] sass_0.4.2 bit64_4.0.5 jsonlite_1.8.3
#> [7] gtools_3.9.3 bslib_0.4.0 assertthat_0.2.1
#> [10] BiocManager_1.30.19 stats4_4.2.1 BiocFileCache_2.6.0
#> [13] blob_1.2.3 GenomeInfoDbData_1.2.9 yaml_2.3.6
#> [16] progress_1.2.2 pillar_1.8.1 RSQLite_2.2.18
#> [19] rlist_0.4.6.2 glue_1.6.2 digest_0.6.30
#> [22] GenomicRanges_1.50.0 XVector_0.38.0 colorspace_2.0-3
#> [25] plyr_1.8.7 htmltools_0.5.3 XML_3.99-0.12
#> [28] pkgconfig_2.0.3 biomaRt_2.54.0 bookdown_0.29
#> [31] zlibbioc_1.44.0 purrr_0.3.5 scales_1.2.1
#> [34] tibble_3.1.8 KEGGREST_1.38.0 generics_0.1.3
#> [37] IRanges_2.32.0 ggplot2_3.3.6 ellipsis_0.3.2
#> [40] cachem_1.0.6 BiocGenerics_0.44.0 cli_3.4.1
#> [43] magrittr_2.0.3 crayon_1.5.2 memoise_2.0.1
#> [46] evaluate_0.17 fansi_1.0.3 xml2_1.3.3
#> [49] tools_4.2.1 data.table_1.14.4 prettyunits_1.1.1
#> [52] hms_1.1.2 lifecycle_1.0.3 stringr_1.4.1
#> [55] S4Vectors_0.36.0 munsell_0.5.0 AnnotationDbi_1.60.0
#> [58] Biostrings_2.66.0 compiler_4.2.1 jquerylib_0.1.4
#> [61] GenomeInfoDb_1.34.0 rlang_1.0.6 grid_4.2.1
#> [64] RCurl_1.98-1.9 rappdirs_0.3.3 bitops_1.0-7
#> [67] rmarkdown_2.17 gtable_0.3.1 DBI_1.1.3
#> [70] curl_4.3.3 reshape2_1.4.4 R6_2.5.1
#> [73] knitr_1.40 dplyr_1.0.10 fastmap_1.1.0
#> [76] bit_4.0.4 utf8_1.2.2 filelock_1.0.2
#> [79] stringi_1.7.8 Rcpp_1.0.9 vctrs_0.5.0
#> [82] png_0.1-7 dbplyr_2.2.1 tidyselect_1.2.0
#> [85] xfun_0.34