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DOex.json
DOex.zip
DOex_alleleprobs.rds
DOex_covar.csv
DOex_foundergeno.csv
DOex_geno.csv
DOex_genoprobs.rds
DOex_gmap.csv
DOex_pheno.csv
DOex_pmap.csv
Makefile
ReadMe.md
c2_genes.rds
c2_snpinfo.rds

ReadMe.md

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

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

Also included are some derived calculations:

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)
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