Detection of pleiotropic effects of quantitative trait loci in outbred populations using regression analysis

Abstract

In this paper a method is presented to determine pleiotropic quantitative trait loci (QTL) or closely linked QTL in an outbred population. The method is based on results from single-trait analyses for different traits and is derived for a granddaughter design. The covariance between estimated contrasts of grandsires obtained in single-trait regression analysis is computed. When there is no pleiotropic QTL, the covariance between contrasts depends on the heritabilities of the traits involved, the polygenic and environmental correlation between the traits, the phenotypic standard deviations, the number of sires per grandsire, and the number of daughters per sire. A pleiotropic QTL results in a covariance that deviates from this expected covariance. The deviation depends on the size of the effects on both traits and on the fraction of grandsires heterozygous for the QTL. When analyzing experimental data, the expected covariance and the confidence interval for the expected covariance can be determined by permutation of the data. A covariance outside the confidence interval suggests the presence of a pleiotropic QTL or a closely linked QTL. The method is verified by simulation and illustrated by analyzing an experimental data set on chromosome 6 in dairy cattle.