In Genomewide association (GWA) studies investigating thousands of SNPs, large sample sizes are needed to obtain a reasonable power after correction for multiple testing. To obtain the necessary sample sizes, data from different populations/cohorts are combined. The problem of pooling evidence across cohorts bears some resemblance with meta-analysis of clinical trials, and in fact classical meta-analytic methodologies from that field are typically used in GWAs. However, in genetics, it can be expected that the cohorts show some amount of heterogeneity in the association measures that are used for significance testing. In this paper, we demonstrate how it is possible to exploit this heterogeneity to improve our ability to detect influential genetic variants. We also discuss how pathway analysis based on summary data can help resolve heterogeneity. The current standard method for testing SNPs across cohorts in GWAs will miss heterogeneous but important genetic variants affecting complex diseases. Our new testing strategy has the potential to detect them while maintaining sensitivity to variants with homogeneous effects.