Article

Statistics in Biosciences

, Volume 1, Issue 2, pp 181-198

First online:

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

  • Michael P. EpsteinAffiliated withDepartment of Human Genetics, Emory University Email author 
  • , Jessica E. HunterAffiliated withDepartment of Human Genetics, Emory University
  • , Emily G. AllenAffiliated withDepartment of Human Genetics, Emory University
  • , Stephanie L. ShermanAffiliated withDepartment of Human Genetics, Emory University
  • , Xihong LinAffiliated withDepartment of Biostatistics, Harvard University
  • , Michael BoehnkeAffiliated withDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan

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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.

Keywords

Variance component model Linkage analysis Generalized linear mixed model