P-value based analysis for shared controls design in genome-wide association studies

• Published in: Genetic epidemiology Vol. 34; no. 7; pp. 725 - 738
• Main Authors: Zaykin, Dmitri V., Kozbur, Damian O.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.11.2010
• Subjects: Case-Control Studies; Chi-Square Distribution; combining correlated P-values; Computer Simulation; Databases, Genetic; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; GWAS; Humans; meta-analysis; Models, Genetic; Molecular Epidemiology; Monte Carlo Method; multiple testing; Polymorphism, Single Nucleotide; shared controls
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.20536

An appealing genome‐wide association study design compares one large control group against several disease samples. A pioneering study by the Wellcome Trust Case Control Consortium that employed such a design has identified multiple susceptibility regions, many of which have been independently replicated. While reusing a control sample provides effective utilization of data, it also creates correlation between association statistics across diseases. An observation of a large association statistic for one of the diseases may greatly increase chances of observing a spuriously large association for a different disease. Accounting for the correlation is also particularly important when screening for SNPs that might be involved in a set of diseases with overlapping etiology. We describe methods that correct association statistics for dependency due to shared controls, and we describe ways to obtain a measure of overall evidence and to combine association signals across multiple diseases. The methods we describe require no access to individual subject data, instead, they efficiently utilize information contained in P‐values for association reported for individual diseases. P‐value based combined tests for association are flexible and essentially as powerful as the approach based on aggregating the individual subject data. Genet. Epidemiol. 34:725–738, 2010.© 2010 Wiley‐Liss, Inc.