The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

64Expression quantitative trait locus (eQTL) mapping provides a powerful means to identify func-65 tional variants influencing gene expression and disease pathogenesis. We report the identification 66 of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues and 449 individuals as part 67 of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated 68 protein-coding genes, with one-third having multiple independent effects. We identify numerous 69 tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in di-70 verse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping 71 algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. 72 We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tis-73 sues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding 74 the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project 75 provides an exceptional resource that has improved our understanding of gene regulation across 76 tissues and the role of regulatory variation in human genetic diseases. 77 Introduction 78 Genome-wide association studies (GWAS) have identified a wealth of genetic variants associated 79 with complex traits and disease risk. However, characterizing the molecular and cellular mechanisms 80 through which these variants act remains a major challenge that limits our understanding of disease 81 pathogenesis and the development of therapeutic interventions. Expression quantitative trait locus 82 (eQTL) studies provide a systematic approach to characterize the molecular consequences of genetic 83 variation across tissues and cell types 1-4 . Multiple studies have identified eQTLs for thousands of 84 genes 5-7 , providing novel insights into gene regulation and enabling the interpretation of GWAS 85 signals 8-12 . These studies have largely been performed in a few easily accessible cell types and cell 86 lines, precluding interpretation of the systemic and tissue-specific consequences of genetic variation. 87To overcome these limitations, the Genotype Tissue Expression (GTEx) project was designed to 88 identify and characterize eQTLs across a broad range of tissues. During the pilot phase, which 89 focused on nine tissues, the GTEx project highlighted patterns of eQTL tissue-specificity and 90 demonstrated the value of multi-tissue study designs for identifying causal genes and tissues for 91 trait-associated variants 1 . These results indicated that the identification of eQTLs across an even 92 broader range of tissues would drastically improve characterization of the gene-and tissue-specific 93 consequences of genetic variants. 94Here, we report on the discovery of cis-eQTLs across an expanded collection of 44 tissues in 95 the GTEx V6p study. This dataset consists of 7,051 transcriptomes from 449 individuals and 96 4...

SummaryPolygenic risk scores (PRS) aim to quantify the contribution of multiple genetic loci to an individual’s likelihood of a complex trait or disease. However, existing PRS estimate genetic liability using common genetic variants, excluding the impact of rare variants. We identified rare, large-effect variants in individuals with outlier gene expression from the GTEx project and then assessed their impact on PRS predictions in the UK Biobank (UKB). We observed large deviations from the PRS-predicted phenotypes for carriers of multiple outlier rare variants; for example, individuals classified as “low-risk” but in the top 1% of outlier rare variant burden had a 6-fold higher rate of severe obesity. We replicated these findings using data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) biobank and the Million Veteran Program, and demonstrated that PRS across multiple traits will significantly benefit from the inclusion of rare genetic variants.

BackgroundIdentification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered GWAS, and therefore few associated loci and causal genes have been identified.ResultsHere, we perform and integrate LD-adjusted colocalization analyses across nine cardiometabolic traits combined with eQTLs and sQTLs from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. To elucidate upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 physiological and pharmacological cardiometabolic regulators in human adipocytes, hepatocytes, and skeletal muscle cells, and map their protein-protein interactions.ConclusionsOur use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our state-of-the-art colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.