A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 diseaseassociated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg=0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

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Intelligence is highly heritable and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

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)

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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...