A global overview of pleiotropy and genetic architecture in complex traits

Abstract

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...

Phenome-wide search for pleiotropic loci highlights key genes and molecular pathways for human complex traits

“…We identified a very strong enrichment for cis-eQTLs (data collected from the Genotype Tissue Expression (GTEx) project 21 ) in the set of pleiotropic variants (1953/2191 compared to 247,551/469,013, p-value = 1.2 * 10 -293 ), suggesting that variants with high pleiotropic effect tend to have a significant influence on gene expression ( Figure 3c). These results are in good concordance with the other reports on genetic pleiotropy in the UKB dataset 16 . We also assessed the prevalence of certain functional variant types in the previously defined set of pleiotropic variants.…”

“…In our study, we aimed at identification of highly pleiotropic loci in the human genome and dissecting the key genetic architecture components relevant to human complex traits. The UKB genetic dataset contains a massive amount of similar and/or related traits which are commonly separated into several major domains 16,30 . To overcome this limitation and group traits that share a significant proportion of their genetic architecture independently of their arbitrary classification, we used a hierarchical clustering approach using the simple Jaccard distance metric calculated using sets of significantly associated variants.…”

“…Pleiotropy of trait-associated variants in the human genome has also attracted lots of attention in the field; and Mendelian randomization based approaches have been proposed to detect pleiotropy in GWAS data 14 . At the same time, UK Biobank dataset offers an excellent opportunity to analyze shared components of the genetic architecture of human phenotypes on the phenomescale, with global overviews suggesting widespread signals of pleiotropy in the UK Biobank data 15,16 . In this study, we developed a statistical framework to explore the landscape of phenome-wide associations in GWAS summary statistics derived from UK Biobank dataset, and identified multiple shared blocks of genetic architecture of diverse human complex traits.…”

A Rare Mutation in SMAD9 Associated With High Bone Mass Identifies the SMAD-Dependent BMP Signaling Pathway as a Potential Anabolic Target for Osteoporosis

“…Detailed methodology has been published. (19) Significance for pleiotropic associations used a traditional genome-wide significance threshold for SNP-trait PheWAS (p < 5 × 10 −8 ). (17) Phenomewide association study (PheWAS) PheWAS was conducted using GWASATLAS (https://atlas.ctglab.…”

“…S5 in File S1). PheWAS involving nearly 3000 traits (19) identified no clear evidence for pleiotropy for the c.65T>C, p.Leu22Pro SMAD9 variant (rs111748421) (Supplemental Fig. Specifically, SMAD9 was more strongly associated with eBMD (P JOINT = 7.94 × 10 −17 ), when compared with neighboring genes within AE800 kb (p > 2.4 × 10 −2 ) (Supplemental Table S2 in File S1).…”