We assessed evidence for association in several ways (see Methods for details), drawing on both classical and bayesian statistical approaches. For polymorphic SNPs on the Affymetrix chip, we performed trend tests (1 degree of freedom16) and general genotype tests (2 degrees of freedom16, referred to as genotypic) between each case collection and the pooled controls, and calculated analogous Bayes factors. There are examples from animal models where genetic effects act differently in males and females17, and to assess this in our data we applied a Box 1Significance levels in genome-wide studiesThere has been much debate concerning interpretation of significance levels in genome-wide association studies and whether, and how, these should be corrected for multiple testing. Classical multiple testing theory in statistics is concerned with the problem of ‘multiple tests’ of a single ‘global’ null hypothesis. This, we would argue, is a problem far removed from that which faces us in genome-wide association studies, where we face the problem of testing ‘multiple hypotheses’ (for a particular disease, one hypothesis for each SNP, or region of correlated SNPs, in the genome) and
