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R
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.gitignore
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Makefile
Makefile
 
 
README.md
README.md
 
 
cc.json
cc.json
 
 
cc.zip
cc.zip
 
 
cc_covar.csv
cc_covar.csv
 
 
cc_crossinfo.csv
cc_crossinfo.csv
 
 
cc_geno1.csv
cc_geno1.csv
 
 
cc_geno10.csv
cc_geno10.csv
 
 
cc_geno11.csv
cc_geno11.csv
 
 
cc_geno12.csv
cc_geno12.csv
 
 
cc_geno13.csv
cc_geno13.csv
 
 
cc_geno14.csv
cc_geno14.csv
 
 
cc_geno15.csv
cc_geno15.csv
 
 
cc_geno16.csv
cc_geno16.csv
 
 
cc_geno17.csv
cc_geno17.csv
 
 
cc_geno18.csv
cc_geno18.csv
 
 
cc_geno19.csv
cc_geno19.csv
 
 
cc_geno2.csv
cc_geno2.csv
 
 
cc_geno3.csv
cc_geno3.csv
 
 
cc_geno4.csv
cc_geno4.csv
 
 
cc_geno5.csv
cc_geno5.csv
 
 
cc_geno6.csv
cc_geno6.csv
 
 
cc_geno7.csv
cc_geno7.csv
 
 
cc_geno8.csv
cc_geno8.csv
 
 
cc_geno9.csv
cc_geno9.csv
 
 
cc_genoX.csv
cc_genoX.csv
 
 

README.md

Collaborative Cross mice

These are genotypes and related information for 69 mouse Collaborative Cross lines, derived from the supplemental data for Srivastava et al. (2017) Genomes of the Mouse Collaborative Cross. Genetics 206:537-556, doi:10.1534/genetics.116.198838.

The supplementary data is at Zenodo, doi:10.5281/zenodo.377036.

(Also see the CCgeno repository which contains genotypes on a few more strains, though in a different form.)

We've used the following:

  • genotypes.zip

    • contains MRCAgenotypes.csv, SEQgenotypes.csv, and CC018genotypes.csv; the latter maybe should have included the GigaMUGA genotypes? We'll use just SEQgenotypes.csv, and will extract the markers in the GigaMUGA and/or MegaMUGA data.

  • Prob36.zip

    • contains a .csv.gz file for each strain which has marker, chromosome, build38 bp position, and then the 36 genotypes

  • SupplementalData.zip

    • contains CCStrains.csv with columns Strain, N_Founders, ChrY, Mitochondria

In addition, Table S2 at the journal website contains important information, including the funnel codes for 55 of the 69 strains. This also contains mtDNA and Y chromosome columns, which are not entirely consistent with the CCStrains.csv file at zenodo.

We'll use the information in Table S2 and will insert arbitrary cross direction information for the lines where the funnel codes are not available.

Note: Three strains (CC031, CC037, and CC056) have some problems where the founder that contributed their Y chromosome is also present on their X chromosome. This is not supposed to happen, and is inconsistent with the way R/qtl2 handles 8-way RIL data. So we've fudged the cross information for these strains so that the X chromosome genotype probabilities won't be messed up. Also, a number of strains are indicated to have been formed from only a subset of the eight founders. Our cross information will still include all eight founders, because the "risib8" cross type requires it.

The founder genotypes are from FigShare (doi:10.6084/m9.figshare.5404762.v2) and combine markers from the MegaMUGA and the GigaMUGA arrays. We use the array annotation information from https://github.com/kbroman/MUGAarrays.

The script R/convert_cc_data.R both downloads and converts the data into a form useful for R/qtl2.

File format

See the R/qtl2 input file format.

Citation

Srivastava et al. (2017) Genomes of the Mouse Collaborative Cross. Genetics 206:537-556, doi:10.1534/genetics.116.198838.

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/main/CC/cc.zip")
cc <- read_cross2(file)