title: A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes.
abstract: Variance-component methods are popular and flexible analytic tools for elucidating the genetic mechanisms of complex quantitative traits from pedigree data. However, variance-component methods typically assume that the trait of interest follows a multivariate normal distribution within a pedigree. Studies have shown that violation of this normality assumption can lead to biased parameter estimates and inflations in type-I error. This limits the application of variance-component methods to more general trait outcomes, whether continuous or categorical in nature. In this paper, we develop and apply a general variance-component framework for pedigree analysis of continuous and categorical outcomes. We develop appropriate models using generalized-linear mixed model theory and fit such models using approximate maximum-likelihood procedures. Using our proposed method, we demonstrate that one can perform variance-component pedigree analysis on outcomes that follow any exponential-family distribution. Additionally, we also show how one can modify the method to perform pedigree analysis of ordinal outcomes. We also discuss extensions of our variance-component framework to accommodate pedigrees ascertained based on trait outcome. We demonstrate the feasibility of our method using both simulated data and data from a genetic study of ovarian insufficiency.
authors.affiliation: Department of Human Genetics, Emory University, Atlanta, GA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Epstein
authors.forename: Jessica E
authors.initials: JE
authors.lastname: Hunter
authors.forename: Emily G
authors.initials: EG
authors.lastname: Allen
authors.forename: Stephanie L
authors.initials: SL
authors.lastname: Sherman
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke