Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

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.

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.

Here we reiterate the fastGWA model ! = # $%& ' $%& + ) * + * + , + -[S1]where ! is an . × 1 vector of mean centred phenotypes with . being the sample size; # $%& is a vector of mean-centred genotype variables of a variant of interest with its effect ' $%& ; ) * is the incidence matrix of fixed covariates with their corresponding coefficients + * ; , is a vector of the total genetic effects captured by pedigree relatedness with ,~2(0, 67 8 9 ); 6 is the family relatedness matrix based on pedigree structure; -is a vector of residuals with -~2(0, <7 = 9 ). The variance-covariance matrix of ! is > = 67 8 9 + ?7 = 9 and the generalized least squares estimate of. Therefore, to test whether ' $%& = 0, we first need to estimate the variance components 7 8 9 and 7 = 9 . As in most existing MLM-based association tools 1-7 , to avoid running the variance estimation analysis repeatedly for each target variant, we estimate 7 8 9 and 7 = 9 under the null modelassuming the effect of a single variant on 7 N 8 9 is negligible. The REML log-likelihood (L) function of model [2] can be written asConventional REML algorithms such as the average information (AI) 8 involve the computations of > WX , Y and Y6, which is computationally intensive when n is large even if 6 is sparse. Here we describe an algorithm (termed as fastGWA-REML) that uses grid search to estimate 7 8 9 without the need to compute > WX , Y and Y6. For ease of computation, we first adjust the phenotype for covariates by linear regression (let ! Z[\ denote a vector of phenotypes after adjustment). We can rewrite L as −with 1 being an . × 1 vector of 1's. All the elements in L including |>|, > WX X and > WX ! Z[\ can be computed efficiently by the Cholesky decomposition of V (without the need of computing > WX ) in sparse matrix setting. Because the computation of L is extremely fast, we can use a grid search to obtain an estimate of 7 8 9 (note that 7 N = 9 can be computed as 7 N ] 9 − 7 N 8 9 with 7 N ] 9 being the empirical variance of phenotype after adjustment).The rationale underlying this grid-search method is similar to that in Runcie et al. 9 . We compute the log-likelihood scores given a grid of possible values of 7 N 8 9 (e.g., 7 N 8 9 Î[0, 1.67 N ] 9 ] with 100 steps, i.e., a step size of 0.0167 N ] 9 ). Note that we define an upper limit to be large than 7 N ] 9 to accommodate rare scenarios where the estimate of 7 N 8 9 from the fastGWA model can be larger than 7 N ] 9 if the true heritability is large in the presence of substantial common environmental effects. Next, we refine the search in a window around the 7 N 8 9 value that produces the highest log-likelihood score (denoted by 7 N 8(bZG) 9) with a window size of 0.27 N 8(bZG) 9 and 16 steps. For example, if 7 N 8(bZG) 9 = 0.167 N ] 9 , we will refine the search in 7 N 8 9 Î[0.1447 N ] 9 , 0.1767 N ] 9 ] with 16 steps (i.e., a step size of 0.0027 N ] 9 ). We repeat this process iteratively until the difference in 7 N 8 9 with the highest log-likelihood score between two adjacent iterations is smalle...

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.