A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes |
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Authors: | Michael P Epstein Jessica E Hunter Emily G Allen Stephanie L Sherman Xihong Lin Michael Boehnke |
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Institution: | (1) Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah Health Sciences Center, Salt Lake City, UT, USA;(2) Present address: Diabetes and Obesity Research Unit, Genetic Basis of Human Disease, Translational Genomics Research Institute, 445 N Fifth Street, Phoenix, AZ 85004, USA |
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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. |
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