Segregation analysis of continuous phenotypes by using higher sample moments.

The present article discusses the use of computational methods based on generalized estimating equations (GEE), as a potential alternative to full maximum-likelihood methods, for performing segregation analysis of continuous phenotypes by using randomly selected family data. The method that we propose can estimate effect and degree of dominance of a major gene in the presence of additional nongenetic or polygenetic familial associations, by relating sample moments to their expectations calculated under the genetic model. It is known that all parameters in basic major-gene models cannot be identified, for estimation purposes, solely in terms of the first two sample moments of data from randomly selected families. Thus, we propose the use of higher (third order) sample moments to resolve this identifiability problem, in a pseudo-profile likelihood estimation scheme. In principle, our methods may be applied to fitting genetic models by using complex pedigrees and for estimation in the presence of missing phenotype data for family members. In order to assess its statistical efficiency we compare several variants of the method with each other and with maximum-likelihood estimates provided by the SAGE computer package in a simulation study.