A powerful regression-based method of quantitative-trait linkage analysis for general pedigrees

Abstract

We present a new method of quantitative trait linkage analysis that combines the advantages of simplicity of regression-based methods and the generality and greater power of variance components models. The new method is based on a regression of estimated identityby-descent (IBD) sharing between relative pairs on the squared-sums and squared-differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure, and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method, by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method, and have performed simulation studies to assess estimation accuracy, Type 1 error rate, and power under a range of conditions. Under most scenarios and in large samples, the method is found to give the correct Type 1 error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus. In large sibships the new method is more powerful than variance components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.