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

Authors

  • Dmitri V. Zaykin,

    Corresponding author
    1. Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina
    • Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina
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  • Damian O. Kozbur

    1. Student Internship Program, National Institute of Environmental Health Sciences, National Institutes of Health, North Carolina
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Abstract

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.

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