Abstract
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.
Document Type
Conference Proceeding
Publication Date
11-29-2011
Digital Object Identifier (DOI)
http://dx.doi.org/10.1186/1753-6561-5-S9-S28
Repository Citation
Fardo, David W.; Druen, Anthony R.; Liu, Jinze; Mirea, Lucia; Infante-Rivard, Claire; and Breheny, Patrick, "Exploration and comparison of methods for combining population- and family-based genetic association using the Genetic Analysis Workshop 17 mini-exome" (2011). Biostatistics Faculty Publications. 3.
https://uknowledge.uky.edu/biostatistics_facpub/3
Notes/Citation Information
Published in BMC Proceedings, v. 5, suppl. 9, S28.
© 2011 Fardo et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.