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QTLRel: an R Package for Genome-wide Association Studies in which Relatedness is a Concern

Riyan Cheng1, Mark Abney1, Abraham A Palmer12* and Andrew D Skol3

Author Affiliations

1 Department of Human Genetics, The University of Chicago, IL 60637 USA

2 Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, IL 60637 USA

3 Department of Medicine, The University of Chicago, IL 60637 USA

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BMC Genetics 2011, 12:66  doi:10.1186/1471-2156-12-66

Published: 27 July 2011

Abstract

Background

Existing software for quantitative trait mapping is either not able to model polygenic variation or does not allow incorporation of more than one genetic variance component. Improperly modeling the genetic relatedness among subjects can result in excessive false positives. We have developed an R package, QTLRel, to enable more flexible modeling of genetic relatedness as well as covariates and non-genetic variance components.

Results

We have successfully used the package to analyze many datasets, including F34 body weight data that contains 688 individuals genotyped at 3105 SNP markers and identified 11 QTL. It took 295 seconds to estimate variance components and 70 seconds to perform the genome scan on an Linux machine equipped with a 2.40GHz Intel(R) Core(TM)2 Quad CPU.

Conclusions

QTLRel provides a toolkit for genome-wide association studies that is capable of calculating genetic incidence matrices from pedigrees, estimating variance components, performing genome scans, incorporating interactive covariates and genetic and non-genetic variance components, as well as other functionalities such as multiple-QTL mapping and genome-wide epistasis.