Multivariate genetic analyses in heterogeneous populations

Martin and Eaves (Heredity 38(1):79-95, 1977) proposed a multivariate model for twin and family data in order to investigate potential differences in the genetic and environmental architecture of multivariate phenotypes. The general form of the model is the independent pathway model, which differentiates between genetic and environmental influences at the item level, and therefore permits the decomposition to differ across items. A restricted version is the common pathway model, where the decomposition takes place at the factor level. The paper has spurred numerous studies, and evidence for differences in genetic and environmental architecture has been established for personality and several other psychiatric phenotypes by showing a better fit of the independent pathway model compared to the common pathway model. We show that genome-wide association studies (GWAS) that use an aggregate score computed from multiple questionnaire items as a univariate phenotype implicitly assume a similar structure as the common pathway model. It has been shown that in case of a differential genetic and environmental architecture, multivariate GWAS methods can outperform the univariate GWAS approach. However, current multivariate methods rely on the assumptions of phenotypic and genetic homogeneity, that is, item responses are assumed to have the same means and covariances, and genetic effects are assumed to be the same for all subjects. We describe a distance-based regression technique that is designed to account for subgroups in the population, and that therefore can account for differential genetic effects. A first evaluation with simulated data shows a substantial increase of power compared to univariate GWAS.