R code implementing QTL mapping of physiological, behavioral and gene expression phenotypes in Carworth Farms White (CFW) outbred mice.
R
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
R
data
LICENSE
README.md

README.md

cfw

This github repository contains code implementing QTL mapping of physiological, behavioral and gene expression phenotypes, and other data analyses to assess the viability of using Carworth Farms White (CFW) mice for mapping genes and genetic loci underlying complex traits relevant to the study of human disease and psychology. This code repository accompanies the following publication:

Parker, C.C., Gopalakrishnan, G., Carbonetto, P., Gonzales, N.M., Leung, E, Park, Y.J., Aryee, E., Davis, J., Blizard, D.A., Ackert-Bicknell, C.L., Lionikas, A., Pritchard, J.K., Palmer, A.A. Genome-wide association study of behavioral, physiological and gene expression traits in commercially available outbred CFW mice. Nature Genetics 48: 919–926.

If you use this code for your research, please cite our paper published in Nature Genetics.

We have released the phenotype, genotype and gene expression data through Data Dryad. Follow this link to access these data:

http://dx.doi.org/10.5061/dryad.2rs41

The R code in this repository has been tested with R version 3.2.3 on a MacBook Pro (2.53 GHz Intel Core 2 Duo, OS X 10.11).

License

The cfw code repository is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See file LICENSE for the full text of the license.

Contact info

For questions and feedback, please contact:

Abraham Palmer
Department of Psychiatry
University of California, San Diego
9500 Gilman Drive
La Jolla, California, USA
aapalmer@ucsd.edu

Contents

Here is a brief summary of the main source code files:

  • calc.pve.R: Estimate the proportion of variance in the phenotype, after removing linear effects of covariates, that is explained by the availablegenetic variants.

  • check.pheno.R: This is a small script to check the whether the observed quantiles for each phenotype, conditioned on different sets of covariates, match what we would expect under the normal distribution.

  • examine.binary.covariates.R: A script to show scatterplots of phenotype vs covariate, and calculate the proportion of variance in a phenotype of interest that is explained by variance candidate binary covariates (e.g., testing apparatus).

  • examine.bmd.R: A small script to compare the distribution of bone-mineral density (BMD) against BMD data from other studies.

  • examine.covariates.R: A script to show scatterplots of phenotype versus covariate, and calculate the proportion of variance in a phenotype of interest that is explained by various candidate covariates (e.g., body weight).

  • gen.megamuga.snp.density.plot.R: Script to plot distribution of MegaMUGA SNPs that are polymorphic in CFW mice across chromosomes 1-19.

  • misc.R: Defines several functions that don't fit anywhere else.

  • plotting.tools.R: Some functions for creating plots to summarize results.

  • polygenic.R: This file contains functions that implement the polygenic model for estimating the proportion of variance expained by available genetic markers. Here is an overview of the functions defined in this file:

  • qtl.analyses.R: Defines a data structure that provides information about each QTL analysis.

  • read.data.R: Defines several functions for reading the QTL experiment data from text files.

Contributors

The code in this repository was primarily developed by Peter Carbonetto and Shyam Gopalakrishnan. Other contributors include Clarissa C. Parker, Natalia M. Gonzales, Emily Leung, Yeonhee J. Park, Emmanuel Aryee, Joe Davis, David A. Blizard, Cheryl L. Ackert-Bicknell, Arimantas Lionikas, Jonathan K. Pritchard and Abraham A. Palmer.

Acknowledgments

This project was funded by NIH R01GM097737 (A.A.P.), NIH T32DA07255 (C.C.P), NIH T32GM07197 (N.M.G.), NIH R01AR056280 (D.A.B.), NIH R01AR060234 (C.A.B.), the Fellowship from the Human Frontiers Science Program (P.C.), and the Howard Hughes Medical Institute (J.K.P.). The authors wish to acknowledge technical assistance from Dana Godfrey, Sima Lionikaite, Vikte Lionikaite, Ausra S. Lionikiene, and John Zekos; as well as technical and intellectual input from Drs. Mark Abney, Justin Borevitz, Karl Broman, Na Cai, Riyan Cheng, Nancy Cox, Robert Davies, Jonathan Flint, Leo Goodstadt, Paul Grabowski, Bettina Harr, Ellen Leffler, Richard Mott, Jerome Nicod, John Novembre, Alkes Price, Matthew Stephens, Daniel Weeks, and Xiang Zhou.