Models for complex and quantitative traits that involve multiple, possibly interacting, genes are described. Methods of linkage analysis are developed that utilize special features of these models, and their power is compared with that of simple genome scans that ignore these special features. Our calculations show that for family-based nonparametric linkage analysis in human genetics, in contrast to experimental genetics, there are limits to the increase in power that can be achieved by correctly modeling gene-gene interactions. In particular, the noncentrality parameter of likelihood-based statistics to detect single gene effects involves both single gene and interaction components of variance, so even when the interaction components of variance are relatively large, the incremental power from a statistic designed to detect both single gene and interaction effects is often quite modest. We carry out our analysis with the assistance of a parameterization that allows us to compute score statistics, noncentrality parameters, and Fisher information matrices reasonably explicitly.
Copyright 2002 Wiley-Liss, Inc.