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Table of Contents
Vol. 74, No. 1, 2012
Issue release date: November 2012
Section title: Original Paper
Free Access
Hum Hered 2012;74:17–26

The Robustness of Generalized Estimating Equations for Association Tests in Extended Family Data

Suktitipat B.a,c,d · Mathias R.A.b · Vaidya D.b · Yanek L.R.b · Young J.H.b · Becker L.C.b · Becker D.M.b · Wilson A.F.a · Fallin M.D.b, c
aGenometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, bDepartment of Medicine, Johns Hopkins Medical Institutions, and cDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md., USA; dDepartment of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
email Corresponding Author

M. Daniele Fallin, PhD

615 N. Wolfe Street

Room 6509

Baltimore, MD 21205 (USA)

Tel. +1 410 955 3643, E-Mail dfallin@jhsph.edu

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Variance components analysis (VCA), the traditional method for handling correlations within families in genetic association studies, is computationally intensive for genome-wide analyses, and the computational burden of VCA increases with family size and the number of genetic markers. Alternative approaches that do not require the computation of familial correlations are preferable, provided that they do not inflate type I error or decrease power. We performed a simulation study to evaluate practical alternatives to VCA that use regression with generalized estimating equations (GEE) in extended family data. We compared the properties of linear regression with GEE applied to an entire extended family structure (GEE-EXT) and GEE applied to nuclear family structures split from these extended families (GEE-SPL) to variance components likelihood-based methods (FastAssoc). GEE-EXT was evaluated with and without robust variance estimators to estimate the standard errors. We observed similar average type I error rates from GEE-EXT and FastAssoc compared to GEE-SPL. Type I error rates for the GEE-EXT method with a robust variance estimator were marginally higher than the nominal rate when the minor allele frequency (MAF) was <0.1, but were close to the nominal rate when the MAF was ≥0.2. All methods gave consistent effect estimates and had similar power. In summary, the GEE framework with the robust variance estimator, the computationally fastest and least data management-intensive approach, appears to work well in extended families and thus provides a reasonable alternative to full variance components approaches for extended pedigrees in a genome-wide association study setting.

© 2012 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: October 25, 2011
Accepted: July 04, 2012
Published online: October 03, 2012
Issue release date: November 2012

Number of Print Pages: 10
Number of Figures: 4
Number of Tables: 5

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

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