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

Abstract

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼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 ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼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 × 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 ∼24.6% of the variance of height and ∼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 ∼0.44 and ∼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.

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

“…We evaluated complete GWAS results in the form of summary statistics ( p values and odds ratios) for clinically diagnosed AD dementia [24] and eight CV-associated RFs, including BMI [47], T2D [28], CAD [31], WHR [18], and plasma lipid levels (TC, TG, LDL, and HDL [44]). We obtained publicly available AD GWAS summary statistic data from the International Genomics of Alzheimer’s Disease Project (IGAP Stages 1 and 2; for additional details, see Supplemental Information and [24]; Table 1).…”

Genetic risk, body mass index, and weight control behaviors: Unlocking the triad

“…For this study, we included all nongenetically related participants in the Add Health cohort who were of European ancestry (given that the BMI PGS was from a GWAS of European-ancestry individuals [Yengo et al, 2018]) and who had genetic data collected in Wave IV. Approximately 77% of participants in the Wave IV sample consented to providing saliva samples and long-term archiving and genome-wide genotyping.…”

“…Genetic sex was inferred based on the X chromosome and checked based on estimates of autosomal heterozygosity (F) as implemented in PLINK. The Add Health cohort was not included in the discovery sample as reported in their supplemental files (Yengo et al, 2018). Individuals with higher than expected autosomal heterozygosity (based off of PLINK defaults) were also dropped.…”

“…The use of 10 PCs was based on our reading of the literature and current best practices (Conley et al, 2016;Price et al, 2006;Price, Zaitlen, Reich, & Patterson, 2010), the meta-analysis which also adjusted for 10 PCs (Yengo et al, 2018), as well as recent work using Add Health genetic data (Belsky et al, 2018). PCs were calculated for nonrelated individuals of European ancestry using the -pca command in PLINK and based on a set of pruned SNPs where linkage disequilibrium pruning was conducted using a window size of 50 kb, a step of five variants, and an R-square cutoff of 0.2.…”

“…Genome-wide association studies (GWAS) have discovered genetic loci associated with BMI (Locke et al, 2015;Speliotes et al, 2010;Yengo et al, 2018). Genome-wide association studies (GWAS) have discovered genetic loci associated with BMI (Locke et al, 2015;Speliotes et al, 2010;Yengo et al, 2018).…”