For each SNP, we estimated a per-allele log(OR) and standard error by logistic regression, including study and principal components as covariates. Genotype-specific ORs were also computed. Overall significance levels were obtained by combining the estimates from the combined GWAS and iCOGS using a fixed-effects meta-analysis to derive a 1-degree-of-freedom test. Inflation of the test statistics (λ) was estimated by dividing the 45th percentile of the test statistic by 0.357 (the 45th percentile for a χ2 distribution on 1 degree of freedom). For this purpose, we used a subset of 22,897 SNPs that were uncorrelated (r2 < 0.1), which were not selected by BCAC and were not within 1 of the 4 common fine-mapping regions. This subset was used to minimize the selection of SNPs associated with disease, on the assumption that such SNPs are likely to be representative of common SNPs in terms of population structure. The inflation statistic was converted to an equivalent inflation statistic for a study with 1,000 cases and 1,000 controls (λ1,000) by adjusting by effective study size, namely
