Multivariate segregation analysis using the mixed model

Most major genes involved in the etiology of complex diseases are likely to have pleiotropic effects on a number of intervening quantitative traits. Methods of segregation analysis that incorporate the additional information from such multiple traits will exhibit greater power for detecting the effects of major genes and allow explicit tests of major locus pleiotropy hypotheses. In this study, we present a new method for multivariate segregation analysis that utilizes a multivariate generalization of Hasstedt's [1982] technique for calculating approximate mixed model likelihoods on pedigrees. The method is based on a simplification of the multivariate conditional likelihood via a transformation that simultaneously orthogonalizes the residual additive genetic and environmental covariance matrices. This transformation allows the multivariate conditional likelihood to be factored into the product of independent univariate conditional likelihoods. Resulting computations are relatively fast, making it feasible to analyze multiple traits in extended pedigrees. We demonstrate our method with a bivariate analysis of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein AI (apo AI) serum levels in 585 pedigreed baboons.