Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

ince the first genome-wide association study on macular degeneration in 2005 (ref. 1), over 3,000 GWASs have been published, for over 1,000 traits, reporting on tens of thousands of genetic risk variants 2. These results have increased our understanding of the genetic architecture of traits. Occasionally, GWAS results have led to further insight into disease mechanisms 3,4 , such as autophagy for Crohn's disease 5 , immunodeficiency for rheumatoid arthritis 6 and transcriptome regulation through FOXA2 in the pancreatic islet and liver for type 2 diabetes 7. After a decade of GWASs, we have learned that the majority of studied traits are highly polygenic and influenced by many genetic variants, each of small effect 4,8 , with disparate genetic architectures across traits 9. Fundamental questions (such as whether all genetic variants or genes in the human genome are associated with at least one, many or even all traits, and whether the polygenic effects for specific traits are functionally clustered or randomly spread across the genome) are, however, still unanswered 4,10,11. Such knowledge would greatly enhance our understanding of how genetic variation leads to trait variation and trait correlations. Whereas GWAS primarily aims to discover genetic variants associated with specific traits, the current availability of vast amounts of GWAS results allow investigation of these general questions. To this end, we compiled a catalog of 4,155 GWAS results across 2,965 unique traits from 295 studies (https://atlas.ctglab.nl), including publicly available GWASs and new results for 600 traits from the UK Biobank 12. These GWAS results were used in the current study to (1) chart the extent of pleiotropy at trait-associated locus, gene, SNP and gene-set levels, (2) characterize the nature of trait-associated variants (that is, the distribution of effect size, minor allele frequency (MAF) and biological functionality of trait-associated or credible SNPs) and (3) investigate genetic architecture across a variety of traits and domains in terms of SNP heritability and trait polygenicity (see Supplementary Fig. 1). Results Catalog of 4,155 GWAS summary statistics. We collected publicly available, full GWAS summary statistics (last update 23 October 2018; see Methods) resulting in 3,555 sets of GWAS summary statistics from 294 studies. We additionally performed GWAS on 600 traits available from the UK Biobank release 2 cohort (UKB2; release May 2017) 12 , by selecting nonbinary traits with >50,000 European individuals with nonmissing phenotypes, and binary traits for which the number of available cases and controls were both >10,000 and total sample size was >50,000 (see Methods, Supplementary Note and Supplementary Tables 1 and 2). In total, we collected 4,155 GWASs from 295 unique studies covering 2,965 unique traits (Supplementary Table 3). Traits were classified into 27 domains 13,14. The average sample size across curated GWASs was 56,250 subjects, with a maximum of 898,130 for type 2 diabetes 15. The 4,155 GWAS results are ma...

Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics, and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads, and prioritize genes for functional follow-up studies.

2After a decade of genome-wide association studies (GWASs), fundamental questions in 3 human genetics are still unanswered, such as the extent of pleiotropy across the genome, the 4 nature of trait-associated genetic variants and the disparate genetic architecture across human 5 traits. The current availability of hundreds of GWAS results provide the unique opportunity 6 to gain insight into these questions. In this study, we harmonized and systematically analysed 7 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, 8we provide an extensive overview of pleiotropy and genetic architecture. We show that trait 9 associated loci cover more than half of the genome, and 90% of those loci are associated with 10 multiple trait domains. We further show that potential causal genetic variants are enriched in 11 coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is 12 related to an estimate of the required sample size to detect 90% of causal genetic variants. 13Our results provide novel insights into how genetic variation contributes to trait variation. All 14 GWAS results can be queried and visualized at the GWAS ATLAS resource 15 (http://atlas.ctglab.nl). 16 across a variety of traits and domains in terms of SNP heritability and trait polygenicity (see 42 Fig. 1). 43 44 Catalogue of 4,155 GWAS summary statistics for 2,965 unique traits 45 Extended DataWe collected publicly available full GWAS summary statistics (last update 23 rd October 46 2018; see Methods). This resulted in 3,555 GWAS summary statistics from 294 studies. We 47 additionally performed GWAS on 600 traits available from the UK Biobank release 2 cohort 48 (UKB2; release May 2017) 12 , by selecting non-binary traits with >50,000 European 49 individuals with non-missing phenotypes, and binary traits for which the number of available 50 cases and controls were each >10,000 and total sample size was >50,000 (see Methods, 51 Supplementary Table 1-2). In total, we collected 4,155 52 Supplementary Information 1 andGWASs from 295 unique studies and 2,965 unique traits (see Supplementary Table 3 for a 53 full list of collected GWASs). Traits were manually classified into 27 standard domains 54 based on previous studies 13,14 . The average sample size across curated GWASs was 56,250 55 subjects. The maximum sample size was 898,130 subjects for a Type 2 Diabetes meta-56 analysis 15 . The 4,155 GWAS results are made available in an online database 57 (http://atlas.ctglab.nl). The database provides a variety of information per trait, including 58 SNP-based and gene-based Manhattan plots, gene-set analyses 16 , SNP heritability 59 estimates 17 , genetic correlations, cross GWAS comparisons and phenome-wide plots. 60For the present study, we restricted our analyses to reasonably powered GWASs (i.e. sample 61 size >50,000), to avoid including SNP effect estimates with relatively large standard errors 62 (see Methods). By selecting a GWAS with the largest sample size per trait, it resulted in 558 63...

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.