Following QC, 370 cases were compared with 134 controls for association using logistic regression, adjusting for two ancestry-informative covariates, in PLINK [20]. Power analysis using 400 cases and 150 controls with the online calculator, the Center for Statistical Genetics (CaTS) power calculator program (http://csg.sph.umich.edu//abecasis/cats/), indicates that at a significance level of p = 0.0025, such a sample size has 60% power to detect common alleles with frequency of 0.5. (Figure A(i) in S1 File), and expectedly has reduced power to detect alleles of lower allele frequency (Figure A(ii) in S1 File). To account for multiple testing issues, the standard GWAS significance threshold of α = 5x10-8 [22] was used. Annotation of gene names for the SNPs was conducted using SeattleSeq http://snp.gs.washington.edu/SeattleSeqAnnotation137/) [23]. For those markers not annotated by the software, gene names were searched on the UCSC Genome Browser, build 37/hg19 (http://genome.ucsc.edu/). For the 100 most significantly associated SNPs from logistic regression, flanking regions of 50kb on either side were also searched. Finally, in order to identify independent association signals in our data, we applied the clumping procedure in PLINK2
