Abstract
BackgroundExisting 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.ResultsWe 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.ConclusionsQTLRel 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.
Highlights
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
QTLRel has been successfully used in an AIL to identify quantitative trait loci (QTL) for methamphetamine sensitivity [10], muscle weight [11], prepulse inhibition [12] and body weight [13]
We identified 11 QTL that exceeded the .05 genome-wide significance threshold estimated from 1800 gene dropping samples
Summary
QTLRel has been successfully used in an AIL to identify QTL for methamphetamine sensitivity [10], muscle weight [11], prepulse inhibition [12] and body weight [13]. We identified 11 QTL that exceeded the .05 genome-wide significance threshold estimated from 1800 gene dropping samples. These 11 QTL were confirmed as distinct signals using a forward step-wise multiple-QTL mapping function implemented in QTLRel. We investigated genome-wide epistatic effects but found none. The analysis was accomplished on an Linux machine equipped with a 2.40GHz Intel(R) Core(TM) Quad CPU. It took 295 seconds to estimate variance components and 70 seconds to perform the genome scan
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