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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Exploration and comparison of methods for combining population- and family-based genetic association using the Genetic Analysis Workshop 17 mini-exome

David W Fardo123*, Anthony R Druen4, Jinze Liu34, Lucia Mirea56, Claire Infante-Rivard7 and Patrick Breheny1

Author Affiliations

1 Department of Biostatistics, University of Kentucky College of Public Health, 121 Washington Avenue, Lexington, KY 40536, USA

2 Division of Biomedical Informatics, University of Kentucky College of Public Health, 121 Washington Avenue, Lexington, KY 40536, USA

3 Center for Clinical and Translational Science, University of Kentucky, 800 Rose Street, Room C-300 , Lexington, KY 40536, USA

4 Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA

5 Dalla Lana School of Public Health, University of Toronto, 155 College Street, Health Science Building, 6th floor, Toronto, ON M5T 3M7, Canada

6 Samuel Lunenfeld Research Institute Mount Sinai Hospital Joseph and Wolf Lebovic Health Complex, 600 University Avenue, Toronto, ON M5G 1X5, Canada

7 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada Purvis Hall, 1020 Pine Avenue West, Montreal, QC H3A 1A3, Canada

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BMC Proceedings 2011, 5(Suppl 9):S28  doi:10.1186/1753-6561-5-S9-S28

Published: 29 November 2011


We examine the performance of various methods for combining family- and population-based genetic association data. Several approaches have been proposed for situations in which information is collected from both a subset of unrelated subjects and a subset of family members. Analyzing these samples separately is known to be inefficient, and it is important to determine the scenarios for which differing methods perform well. Others have investigated this question; however, no extensive simulations have been conducted, nor have these methods been applied to mini-exome-style data such as that provided by Genetic Analysis Workshop 17. We quantify the empirical power and false-positive rates for three existing methods applied to the Genetic Analysis Workshop 17 mini-exome data and compare relative performance. We use knowledge of the underlying data simulation model to make these assessments.