Departments of Psychiatry, Biostatistics, Genetics and Computer Science
University of North Carolina at Chapel Hill
Center for Biomarker Research and Precision Medicine
Virginia Commonwealth University
CorrMeta is a computationally efficient alternative to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) or expression quantitative trait loci (eQTL) analysis. Instead of analyzing all twin samples together with LMM, CorrMeta first randomly splits twin samples into two independent groups on which multiple linear regression analysis is performed separately, followed by an appropriate meta-analysis to combine the two non-independent test results. Similar idea is also extended to combine GWAS results from multiple correlated phenotypes through CorrMeta. Our approaches provide a huge leap in terms of computing performance for GWAS data with related subjects and correlated phenotypes.
Download and document
CorrMeta R package.
To install, download the package file CorrMeta_1.0.tar.gz and run (in R):
install.packages("CorrMeta_1.0.tar.gz", repos = NULL, type="source")
The package includes reference manual and:
Sample data set (file)
Xia, K, Shabalin, AA et al (2015). CorrMeta: Fast Association Analysis for eQTL and GWAS Data with Related Samples and Correlated Phenotypes. (Submitted)
Kai Xia: firstname.lastname@example.org
Andrey A Shabalin: email@example.com
Fei Zou: firstname.lastname@example.org