A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes

• Type of Material: Article
• Authors: Michael P. Epstein, Emory University; Jessica E. Hunter, Emory University; Emily Graves Allen, Emory University; Stephanie Sherman, Emory University; Xihong Lin, Harvard University; Michael Boehnke, University of Michigan
• Language: English
• Date: 2009-11
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © International Chinese Statistical Association 2009
• Final Published Version (URL): http://link.springer.com/article/10.1007%2Fs12561-009-9010-5
• Title of Journal or Parent Work: Statistics in Biosciences
• ISSN: 1867-1764
• Volume: 1
• Issue: 2
• Start Page: 181
• End Page: 198
• Grant/Funding Information: This research was supported by the University Research Committee of Emory University (to M.P.E.) and National Institutes of Health Grants R01 HG03618 (to M.P.E.), R01 HD29909 (to J.E.H., E.G.A. and S.L.S.), R29 CA76404 (to X.L.), and R01 HG00376 (to M.B.).
• Keywords: generalized linear mixed model; linkage analysis; Variance component model
• Research Categories: Biology, Genetics

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.