P-value based analysis for shared controls design in genome-wide association studies.
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.
Zaykin
Dmitri V
DV
Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina, USA. zaykind@niehs.nih.gov
Kozbur
Damian O
DO
D016022:Case-Control Studies; D016009:Chi-Square Distribution; D003198:Computer Simulation; D030541:Databases, Genetic; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D017720:Molecular Epidemiology; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide