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.

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 v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem 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.

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

The resources generated by the GTEx consortium oer unprecedented opportunities to advance our understanding of the biology of human traits and diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genetic loci discovered by genome-wide association studies (GWAS). Across a broad set of complex traits and diseases, we nd widespread dosedependent eects of RNA expression and splicing, with higher impact on molecular phenotypes translating into higher impact downstream. Using colocalization and association approaches that take into account the observed allelic heterogeneity, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotypephenotype link. Harmonized GWAS and QTL datasetsThe nal GTEx data release (v8) includes 54 primary human tissues, 49 of which included at least 65 samples and were used for cis-QTL mapping ( Fig. 1) (9). This phase increases the number of available tissues relative to previous GTEx publications (v6p; 44 tissues) (8) and doubles the sample size from 7,051 RNA-Seq samples from 449 individuals to 15,253 samples from 838 individuals, now all with whole genome sequencing data as opposed to genotype imputation in v6p. Furthermore, the v8 core data resources now include splicing QTLs (9), allowing parallel analysis of both expression and splicing variation underlying complex traits. Using these resources, we investigated the contribution of expression and splicing QTLs in cis (eQTL and sQTL, respectively) to complex trait variance and etiology.We retained 87 GWAS datasets representing 74 distinct complex traits for further analyses (table S1 and g. S1) after stringent quality control (g. S2; (21)) and data harmonization(g. S3, g. S4). 6We found a signicantly higher correlation in mediating eect between primary and secondary eQTLs for a given gene compared to a null distribution obtained by sampling GWAS eect sizes from a bivariate normal distribution to account for the small observed LD between primary and secondary eQTLs ( Fig. 2D-E) while keeping the observed eQTL eect sizes (p < 1 × 10 −30 ).Interestingly, the correlation between primary and secondary eQTLs for non-colocalized genes (rcp < 0.01), which were used as controls (9, 21), was signicantly higher than this more accurate null, indicating that even eQTLs with very low colocalization probability include many genes that are likely causal. Given this concordance between multiple independent eQTLs, it is clear that with widespread allelic heterogeneity detected with currently available sample sizes, methods that assume single causal variants are highly limited. The approaches described here enable insights into how multiple regulatory effects converge to mediate the same trait association. 7 * alphabetic order

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Background Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

The integration of transcriptomic studies and genome‐wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA‐sequencing samples from 948 post‐mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine‐mapping (dap‐g) and borrowing information across tissues (mashr) can lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision‐recall and receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.