Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD.

SummaryEducational attainment (EA) is strongly influenced by social and other environmental factors, but genetic factors are also estimated to account for at least 20% of the variation across individuals1. We report the results of a genome-wide association study (GWAS) for EA that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication in an independent sample of 111,349 individuals from the UK Biobank. We now identify 74 genome-wide significant loci associated with number of years of schooling completed. Single-nucleotide polymorphisms (SNPs) associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioral phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because EA is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric disease.

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.

A genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent SNPs are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈ 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

Motivation LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. Results In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and implementation The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

Highlights d Three groups of highly genetically-related disorders among 8 psychiatric disorders d Identified 109 pleiotropic loci affecting more than one disorder d Pleiotropic genes show heightened expression beginning in 2 nd prenatal trimester d Pleiotropic genes play prominent roles in neurodevelopmental processes Authors Cross-Disorder Group of the Psychiatric Genomics Consortium

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci ( P = 4.5×10 −8 –1.2×10 −43 ). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing ( p <3×10 −4 ). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D ( p = 4.3×10 −3 , n = 22,044), increased triglycerides ( p = 2.6×10 −14 , n = 93,440), increased waist-to-hip ratio ( p = 1.8×10 −5 , n = 77,167), increased glucose two hours post oral glucose tolerance testing ( p = 4.4×10 −3 , n = 15,234), increased fasting insulin ( p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations ( p = 4.5×10 −13 , n = 96,748) and decreased BMI ( p = 1.4×10 −4 , n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.

Refractive error is the most common eye disorder worldwide, and a prominent cause of blindness. Myopia affects over 30% of Western populations, and up to 80% of Asians. The CREAM consortium conducted genome-wide meta-analyses including 37,382 individuals from 27 studies of European ancestry, and 8,376 from 5 Asian cohorts. We identified 16 new loci for refractive error in subjects of European ancestry, of which 8 were shared with Asians. Combined analysis revealed 8 additional loci. The new loci include genes with functions in neurotransmission (GRIA4), ion channels (KCNQ5), retinoic acid metabolism (RDH5), extracellular matrix remodeling (LAMA2, BMP2), and eye development (SIX6, PRSS56). We also confirmed previously reported associations with GJD2 and RASGRF1. Risk score analysis using associated SNPs showed a tenfold increased risk of myopia for subjects with the highest genetic load. Our results, accumulated across independent multi-ethnic studies, considerably advance understanding of mechanisms involved in refractive error and myopia.