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...

40Understanding the genetics of gene regulation provides information on the cellular mechanisms 41 through which genetic variation influences complex traits. Expression quantitative trait loci, or 42 eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. 43 While most analyses of human data has focused on regulation of expression by nearby variants 44 (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important 45 phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants 46 on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue 47 Expression (GTEx) project data 1 , consisting of 449 individuals with RNA-sequencing data 48 across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we 49 demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single 50 tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying 51 strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe 52 three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that 53 has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator 54 near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs 55 across human tissues, which contribute to an improved understanding of the tissue-specific 56 cellular mechanisms of regulatory genetic variation. 57 Introduction 58Variation in the human genome influences complex disease risk through changes at a cellular 59 level. Many disease-associated variants are also associated with gene expression levels through 60 which they mediate disease risk. The majority of expression quantitative trait locus (eQTL) 61 studies 1-6 thus far have focused on local-or cis-eQTLs because of the relative simplicity of 62 association mapping in human for both statistical and biological reasons 7, or 63 genetic variants that affect gene expression levels of distant target genes, have received much 64 less attention in comparison to cis-eQTLs, in part due to the considerable multiple hypotheses 65 testing burden 9 . Far fewer replicable, strong effect trans-eQTLs have been discovered in human 66 data as compared to cis-eQTLs, unlike in model organisms such as Saccharomyces cerevisiae or 67 Arabidopsis thaliana 7,10,11 . However, a handful of replicable trans-eQTLs have now been 68 identified in human tissues 3,12,13 . Additionally, recent work has suggested that trans-eQTLs 69 contribute substantially to the genetic regulation of complex diseases 12 , motivating a careful 70 examination of the role of trans-eQTLs across human tissues in disease etiology. 71Here, we identify trans-eQTLs in the Genotype-Tissue Expression (GTEx) v6 data, which 72 include 449 individuals with imputed genotypes and RNA-seq data across 44 tissues for a total 7...

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

Long non-coding RNA (lncRNA) genes are known to have diverse impacts on gene regulation. However, it is still a major challenge to distinguish functional lncRNAs from those that are byproducts of surrounding transcriptional activity. To systematically identify hallmarks of biological function, we used the GTEx v8 data to profile the expression, regulation, network relationships and trait associations of lncRNA genes across 49 tissues encompassing 87 distinct traits. In addition to revealing widespread differences in regulatory patterns between lncRNA and protein-coding genes, we identified novel disease-associated lncRNAs, such as C6orf3 for psoriasis and LINC01475/RP11-129J12.1 for ulcerative colitis. This work provides a comprehensive resource to interrogate lncRNA genes of interest and annotate cell type and human trait relevance.One Sentence SummarylncRNA genes have distinctive regulatory patterns and unique trait associations compared to protein-coding genes.