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...

RNA is a critical component of chromatin in eukaryotes, both as a product of transcription, and as an essential constituent of ribonucleoprotein complexes that regulate both local and global chromatin states. Here, we present a proximity ligation and sequencing method called Chromatin-Associated RNA sequencing (ChAR-seq) that maps all RNA-to-DNA contacts across the genome. Using Drosophila cells, we show that ChAR-seq provides unbiased, de novo identification of targets of chromatin-bound RNAs including nascent transcripts, chromosome-specific dosage compensation ncRNAs, and genome-wide trans-associated RNAs involved in co-transcriptional RNA processing.

RNA sequencing (RNA-seq) is a complementary approach for Mendelian disease diagnosis for patients in whom exome-sequencing is not informative. For both rare neuromuscular and mitochondrial disorders, its application has improved diagnostic rates. However, the generalizability of this approach to diverse Mendelian diseases has yet to be evaluated. We sequenced whole blood RNA from 56 cases with undiagnosed rare diseases spanning 11 diverse disease categories to evaluate the general application of RNA-seq to Mendelian disease diagnosis. We developed a robust approach to compare rare disease cases to existing large sets of RNA-seq controls (N=1,594 external and N=31 family-based controls) and demonstrated the substantial impacts of gene and variant filtering strategies on disease gene identification when combined with RNA-seq. Across our cohort, we observed that RNA-seq yields a 8.5% diagnostic rate. These diagnoses included diseases where blood would not intuitively reflect evidence of disease. We identified RARS2 as an under-expression outlier containing compound heterozygous pathogenic variants for an individual exhibiting profound global developmental delay, seizures, microcephaly, hypotonia, and progressive scoliosis. We also identified a new splicing junction in KCTD7 for an individual with global developmental delay, loss of milestones, tremors and seizures. Our study provides a broad evaluation of blood RNA-seq for the diagnosis of rare disease.

Precise interpretation of the effects of protein-truncating variants (PTVs) is important for accurate determination of variant impact. Current methods for assessing the ability of PTVs to induce nonsense-mediated decay (NMD) focus primarily on the position of the variant in the transcript. We used RNA-sequencing of the Genotype Tissue Expression v8 cohort to compute the efficiency of NMD using allelic imbalance for 2,320 rare (genome aggregation database minor allele frequency <=1%) PTVs across 809 individuals in 49 tissues. We created an interpretable predictive model using penalized logistic regression in order to evaluate the comprehensive influence of variant annotation, tissue, and inter-individual variation on NMD. We found that variant position, allele frequency, including ultra-rare and singleton variants, and conservation were predictive of allelic imbalance. Furthermore, we found that NMD effects were highly concordant across tissues and individuals. Due to this high consistency, we demonstrate in silico that utilizing peripheral tissues or cell lines provides accurate prediction of NMD for PTVs.