  Combining data from several case-control genome-wide association (GWA)
studies can yield greater efficiency for detecting associations of disease with
single nucleotide polymorphisms (SNPs) than separate analyses of the component
studies. We compared several procedures to combine GWA study data both in terms
of the power to detect a disease-associated SNP while controlling the
genome-wide significance level, and in terms of the detection probability
($\mathit{DP}$). The $\mathit{DP}$ is the probability that a particular
disease-associated SNP will be among the $T$ most promising SNPs selected on
the basis of low $p$-values. We studied both fixed effects and random effects
models in which associations varied across studies. In settings of practical
relevance, meta-analytic approaches that focus on a single degree of freedom
had higher power and $\mathit{DP}$ than global tests such as summing chi-square
test-statistics across studies, Fisher's combination of $p$-values, and forming
a combined list of the best SNPs from within each study.
