Copyright © 2005 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 76, Issue 4, 592-608, 1 April 2005

doi:10.1086/429225

Genetic Association Analysis Using Data from Triads and Unrelated Subjects

Michael P. Epstein1, Go To Corresponding Author,  , Colin D. Veal3, Richard C. Trembath3, Jonathan N.W.N. Barker4, Chun Li5 and Glen A. Satten2

1 Department of Human Genetics, Emory University, Atlanta
2 Centers for Disease Control and Prevention, Atlanta
3 Division of Medical Genetics, University of Leicester, Leicester, United Kingdom
4 St. John’s Institute of Dermatology, King’s College London, London
5 Center for Human Genetics Research, Vanderbilt University, Nashville

Address for correspondence and reprints: Dr. Michael P. Epstein, Department of Human Genetics, Emory University School of Medicine, 615 Michael Street, Suite 301, Atlanta, GA 30322


Abstract

The selection of an appropriate control sample for use in association mapping requires serious deliberation. Unrelated controls are generally easy to collect, but the resulting analyses are susceptible to spurious association arising from population stratification. Parental controls are popular, since triads comprising a case and two parents can be used in analyses that are robust to this stratification. However, parental controls are often expensive and difficult to collect. In some situations, studies may have both parental and unrelated controls available for analysis. For example, a candidate-gene study may analyze triads but may have an additional sample of unrelated controls for examination of background linkage disequilibrium in genomic regions. Also, studies may collect a sample of triads to confirm results initially found using a traditional case-control study. Initial association studies also may collect each type of control, to provide insurance against the weaknesses of the other type. In these situations, resulting samples will consist of some triads, some unrelated controls, and, possibly, some unrelated cases. Rather than analyze the triads and unrelated subjects separately, we present a likelihood-based approach for combining their information in a single combined association analysis. Our approach allows for joint analysis of data from both triad and case-control study designs. Simulations indicate that our proposed approach is more powerful than association tests that are based on each separate sample. Our approach also allows for flexible modeling and estimation of allele effects, as well as for missing parental data. We illustrate the usefulness of our approach using SNP data from a candidate-gene study of psoriasis.


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