![]() ![]() However, the required sample size to explain all SNP heritability by identified individual GWS loci is not known because it depends on the joint distribution of allele frequency and effect size at causal variants. ∼50% and ∼30%, respectively ( 9–11), this indicates an enormous potential for discoveries expected simply from increasing sample sizes. Compared to estimates of height and BMI, i.e. To date, the largest published GWAS of height ( 5) and body mass index (BMI) ( 6) in ∼250000 participants on average have uncovered 697 and 97 near-independent SNPs associated with these traits and explaining ∼15% and ∼3% of trait variance, respectively. Overall, clarifying the differences between, and has been a major advance in the field and has helped providing theoretical guarantees that increasing GWAS sample sizes would continue to yield more discoveries, as long as the difference between and persists. Therefore, their effects might remain statistically undetectable, despite still contributing to the difference between and. It is worth noting that untagged variants are often rare or even unique to single nuclear families. ( 9–11) and others ( 12) have helped clarifying the distinction between what can potentially be explained by all SNPs (aka SNP heritability, ) and what remains out of the reach of SNP array-based GWAS, for instance causal variants that are not tagged by genotyped or imputed SNPs. The reasons explaining the gap between and, also termed as missing heritability, are now better understood. One of the early challenges faced by GWAS has been to bridge the gap between the amount of trait variance explained by genome-wide significant (GWS) loci ( ) compared to estimates of heritabilities from pedigree-based studies ( ). They have also been used to generate experimentally testable hypotheses and predict traits and disease risk ( 7, 8). GWAS have led to the discovery of tens of thousands of polymorphisms associated with interindividual differences in quantitative traits or disease susceptibility. Over the past 15 years, genome-wide association studies (GWAS) have been increasingly successful in unveiling many aspects of the genetic architectures of complex traits and diseases ( 1–6). All summary statistics are made available for follow-up studies. 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. 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. Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. 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). 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. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. ![]() 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. ![]()
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