Optimizing the power of genome-wide association studies by using publicly available reference samples to expand the control group.

Publication Type:

Journal Article


Genetic epidemiology, Volume 34, Issue 4, p.319-26 (2010)


2010, Alleles, Center-Authored Paper, Computer Simulation, Data Interpretation, Statistical, False Positive Reactions, Gene Frequency, Genetic Variation, Genome-Wide Association Study, Heterozygote, Human Biology Division, Humans, Models, Genetic, Models, Statistical, Odds Ratio, Reference Values, Research Design, RISK


Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as "genetically matched controls" for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify "axes" of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study.