Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

TitleMeta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry
Publication TypeJournal Article
Year of Publication2018
AuthorsYengo, L, Sidorenko, J, Kemper, KE, Zheng, Z, Wood, AR, Weedon, MN, Frayling, TM, Hirschhron, JN, Yang, J, Visscher, PM
Corporate Authorsthe GIANT Consortium
JournalHuman Molecular Genetics
ISSN Number0964-6906
KeywordsBMI, Genetics, GWAS, Height

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in similar to 250000 European participants have led to the discovery of similar to 700 and similar to 100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in similar to 450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N similar to 700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 x 10(-8)), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain similar to 24.6% of the variance of height and similar to 6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were similar to 0.44 and similar to 0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

Citation Key9988