The statistical analysis of genome-wide association studies (GWASs) with multiple diseases and shared controls (SCs) is discussed. The usual method for analyzing data from these studies is to compare each individual disease with either the SCs or the pooled controls which include other diseases. We observed that applying individual association tests can be problematic because these tests may suffer from power loss in detecting significant associations between diseases and single-nucleotide polymorphism or copy number variant. We propose here a two-stage procedure wherein we first apply an overall chi-square test for multiple diseases with SCs; if the overall test is rejected, then individual tests using the chi-square partition method will be applied to each disease against SCs. A real GWAS data set with SCs and a Monte Carlo simulation study are used to demonstrate that the proposed method is more effective and preferable than other existing methods for analyzing data from GWASs with multiple diseases and SCs.
Keywords: Cochran–Armitage trend test; chi-square partition; robust test; single-nucleotide polymorphism.
© The Author(s) 2013.