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...

SummaryThe inflammatory bowel diseases (IBD) are chronic gastrointestinal inflammatory disorders that affect millions worldwide. Genome-wide association studies have identified 200 IBD-associated loci, but few have been conclusively resolved to specific functional variants. Here we report fine-mapping of 94 IBD loci using high-density genotyping in 67,852 individuals. We pinpointed 18 associations to a single causal variant with >95% certainty, and an additional 27 associations to a single variant with >50% certainty. These 45 variants are significantly enriched for protein-coding changes (n=13), direct disruption of transcription factor binding sites (n=3) and tissue specific epigenetic marks (n=10), with the latter category showing enrichment in specific immune cells among associations stronger in CD and in gut mucosa among associations stronger in UC. The results of this study suggest that high-resolution fine-mapping in large samples can convert many GWAS discoveries into statistically convincing causal variants, providing a powerful substrate for experimental elucidation of disease mechanisms.

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.

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...

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10−8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10−11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry.