Diversity Outcross data from Recla et al (2014)

Source

https://phenome.jax.org/projects/Recla1

Founder genotypes from ftp://ftp.jax.org/MUGA/

This is the same data as in DO_Recla, but reduced to three chromosomes, one phenotype, and with reduced markers.

Files

• DOex.json - Control file in JSON format
• DOex_covar.csv - covariate data (individuals x covariates)
• DOex_pheno.csv - phenotype data (individuals x phenotypes)
• DOex_geno.csv - genotype data (markers x individuals)
• do_foundergeno.csv - founder genotype data (markers x founders)
• DOex_gmap.csv - Genetic map of markers (positions in cM)
• DOex_pmap.csv - Physical map of markers (positions in NCBI38/mm10 Mbp)

The data are also available as a zip file, DOex.zip.

Also included are some derived calculations:

• DOex_genoprobs.rds - Genotype probabilities calculated with qtl2::calc_genoprob()
• DOex_alleleprobs.rds - Allele probabilities calculated from DOex_genoprobs.rds and collapsed to alleles with qtl2::genoprob_to_alleleprob()

Further, there are tables of SNPs and genes for a 2 Mbp region on chromosome 2:

• c2_snpinfo.rds - A data frame with SNP genotypes for the 8 Collaborative Cross founder strains (in the interval 96.5-98.5 Mbp on chr 2)

• c2_genes.rds - A data frame with gene locations (in the interval 96.5-98.5 Mbp on chr 2)

File format

See the R/qtl2 input file format.

Citations

Recla JM, Robledo RF, Gatti DM, Bult CJ, Churchill GA, Chesler EJ (2014) Precise genetic mapping and integrative bioinformatics in Diversity Outbred mice reveals Hydin as a novel pain gene. Mamm Genome 25:211-222

Use with R/qtl2

Load these data into R directly from the web as follows:

library(qtl2)
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/DOex.zip")
DOex <- read_cross2(file)

You can load pre-calculated genotype probabilities (~19 MB) as follows:

tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/DOex_genoprobs.rds")
download.file(file, tmpfile)
pr <- readRDS(tmpfile)
unlink(tmpfile)

You can load pre-calculated allele probabilities (~5 MB) as follows:

tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/DOex_alleleprobs.rds")
download.file(file, tmpfile)
apr <- readRDS(tmpfile)
unlink(tmpfile)