in the replication analysis. This was likely due to the inclusion of one study that had imputed to the combined HapMap + 1000G panel, whereas all other studies with imputed data had imputed to HapMap. To increase order 345627-80-7 homogeneity, we performed several sensitivity analyses. First, we reran the discovery meta-analysis excluding the MAAS study. This analysis did not materially change our findings, with one additional SNP reaching the subthreshold level of significance and five SNPs losing significance. None of these five SNPs had replicated in the primary analysis. Second, we reran the replication and joint meta-analysis including only those cohorts that imputed to 1000G. Results of this analysis were very similar to the primary analysis, with two additional replicated SNPs, rs17309930 near BDNF and rs13107325 in SLC39A8. Both of these are known loci for adult BMI. Third, we reran the replication including only the HapMap-imputed and unimputed studies. The results were very similar to those using all studies, with rs4870949 and rs2590942 PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19821824 now passing the significance threshold and rs8092503 and rs3829849 now just above it. rs1573972 was not replicated in any of the analyses. As results of the third and fourth sensitivity analyses were very similar to those including all replication cohorts, we used the latter as our main analysis for reasons of power. Statistical approach Cohort-specific genome-wide association analyses were first run in the discovery cohorts, using high-density Illumina or Affymetrix SNP arrays, followed by imputation to the HapMap CEU release 22 imputation panel. The MAAS study imputed to the combined 1000 Genomes Pilot + HapMap 3 panel. Before imputation, studies applied studyspecific quality filters on samples and SNP call rate, minor allele frequency and HardyWeinberg disequilibrium. Leipzig, NFBC1986, STRIP and PANIC contributed unimputed data from the Metabochip. Linear regression models assuming an additive genetic model were run in each study, to assess the association of each SNP with SDSBMI, adjusting for principal components if this was deemed needed in the individual studies. As SDSBMI is age and sex specific, no further adjustments were made. Before the meta-analysis, we applied quality filters to each study, filtering out SNPs with a minor allele frequency below 1% and SNPs with poor imputation quality. For studies contributing unimputed metabochip data to the discovery analysis, we excluded SNPs with a SNP call rate of <0.95 Genetic risk score and percentage of variance explained A weighted risk score was computed as the sum of the number of SDSBMI-increasing alleles weighted by the effect sizes from the discovery meta-analysis. Then, the score was rescaled to range from zero to the maximum number of SDS BMI-increasing alleles and rounded to the nearest integer. The association of the risk score with 398 | Human Molecular Genetics, 2016, Vol. 25, No. 2 SDSBMI was assessed in one of the largest replication cohorts by running a linear regression model. The variance in SDSBMI explained by the risk score was estimated by the unadjusted R 2 of this model. The percentage of variance in adult BMI explained by the 15 SNPs was calculated using the published data from the recently published large meta-analysis of GWAs studies on adult BMI. For each SNP, the variance explained was calculated as: 2 MAF , and these variances were then summed to give the total percentage of variance in adult BMI explained by the 15 SNPs. LD sc