Abstract
Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.
Highlights
Quantitative trait locus (QTL) mapping [1] and genome-wide association studies (GWAS) [2] are the main tools for identifying genomic regions harboring quantitative trait loci
One of the goals of QTL mapping and GWAS is to quantify the size of a QTL, which is measured by the QTL variance or the proportion of trait variance explained by the QTL
The estimated QTL variance in conventional QTL mapping studies takes the square of the estimated QTL effect
Summary
Quantitative trait locus (QTL) mapping [1] and genome-wide association studies (GWAS) [2] are the main tools for identifying genomic regions harboring quantitative trait loci. These QTL regions are the targets for molecular geneticists to further expand the experiments, to clone the actual genes for agronomic traits and to help breeders develop optimal marker assisted selection (MAS) programs [3]. We believe that estimating the variances of QTL is important as locating the QTL because only QTL with large effects are useful for application while small effect but statistically significant QTL are not economically meaningful. Whether a QTL is large or small is determined relative to the residual or phenotypic variance
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