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 excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds alternative splicing events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4-2.1 times more sQTLs, many of which help us ascribe molecular effects to diseaseassociated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available at https: // github. com/ davidaknowles/ leafcutter .

14To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we 15 performed a tissue-specific gene association study in a cohort of over 100K individuals (n cases ⇡ 26K, 16 n controls ⇡ 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. 17We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously 18 reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 1913 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDR rep < 0.05) in an 20 independent study (n cases ⇡ 10K, n controls ⇡ 62K). We also found enrichment of significant associations 21 in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and 22 breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis 23 suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later 24 onset type 2 diabetes. 25. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/108134 doi: bioRxiv preprint first posted online Feb. 13, 2017; 2 Introduction 26Type 2 diabetes (T2D) is a complex disease characterized by impaired glucose homeostasis resulting from 27 dysfunction in insulin-secreting pancreatic islets and decreased insulin sensitivity in peripheral tissues 28 [1]. In addition to environmental factors such as a sedentary lifestyle and poor diet, genetic susceptibility 29 is an important contributor to the development of T2D [2]. Genome-wide association studies (GWAS) 30 have uncovered more than 100 loci that significantly associate with either T2D or glucose-related traits 31 [3, 4, 2]. However, the majority of single nucleotide polymorphisms (SNPs) significantly associated with 32 T2D reside in intronic and intergenic regions rather than protein-encoding regions [5, 6]. The results from 33GWAS suggest an important role for genetic variation that regulates gene expression rather than altering 34 codon sequence [7] and have motivated e↵orts to map the regulatory landscape of the genome [8, 9, 10]. 35Indeed, sets of trait-associated SNPs are enriched for variants that associate with gene expression (i.e. 36expression quantitative trait loci or eQTLs) [11] and that occupy DNAse hypersensitivity sites (DHS) [12] 37 -regions overrepresented for eQTLs per se [13]. Moreover, DHS explain a disproportionately high share 38 of SNP heritability [14] across 11 complex traits [15] and eQTLs mapped in insulin-responsive peripheral 39 tissues similarly "concentrate" SNP heritability estimates for T2D [16]. human hepatocytes [17]. Moreover, Sort1 knockdown and overexpression studies in mice altered LDL-C 46 and very low density lipoprotein (VLDL) levels [17]. In a study of the FTO locus harboring the strongest 47 associati...

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.