Accounting for heterogeneity of variances to improve the precision of QTL mapping in dairy cattle

Abstract

ABSTRACTThe principle of interval mapping for quantitative trait loci (QTL) was originally developed for the analysis of single backcross data but it has been increasingly applied to more complicated experimental designs and data structures. It is important to study whether accounting for the heterogeneity of variance would improve the precision of QTL mapping based on data of multiple populations or families. This study compared homogeneous and heterogeneous maximum likelihood approaches for QTL mapping. The data consisted of 433 sons from six sire families with 69 microsatellite markers distributed over 12 chromosomes. The results of this study indicate that the heterogeneous approach generally produced a smaller residual variance and thus provided a better fit to the data than the homogeneous approach, meaning that the heterogeneous approach offers better precision in estimating both positions and effects of QTL. The results further showed that accounting for the heterogeneity of residual variance led to different statistical inferences from ignoring the heterogeneity of variance in QTL mapping. The heterogeneous approach is useful for QTL mapping based on the joint data of diverse reference populations or heteroscedastic data obtained from crossing animals with different genetic backgrounds.