Statistics in Biosciences

, Volume 1, Issue 2, pp 181–198

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


    • Department of Human GeneticsEmory University
  • Jessica E. Hunter
    • Department of Human GeneticsEmory University
  • Emily G. Allen
    • Department of Human GeneticsEmory University
  • Stephanie L. Sherman
    • Department of Human GeneticsEmory University
  • Xihong Lin
    • Department of BiostatisticsHarvard University
  • Michael Boehnke
    • Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan

DOI: 10.1007/s12561-009-9010-5

Cite this article as:
Epstein, M.P., Hunter, J.E., Allen, E.G. et al. Stat Biosci (2009) 1: 181. doi:10.1007/s12561-009-9010-5


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.


Variance component modelLinkage analysisGeneralized linear mixed model

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© International Chinese Statistical Association 2009