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The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 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. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

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.

UMR AGAP - équipe DAAV (Diversité, adaptation et amélioration de la vigne)139 auteurs : Ardlie KG, Deluca DS, Segrè AV, Sullivan TJ, Young TR, Gelfand ET, Trowbridge CA, Maller JB, Tukiainen T, Lek M, Ward LD, Kheradpour P, Iriarte B, Meng Y, Palmer CD, Esko T, Winckler W, Hirschhorn JN, Kellis M, MacArthur DG, Getz G, Shabalin AA, Li G, Zhou YH, Nobel AB, Rusyn I, Wright FA, Lappalainen T, Ferreira PG, Ongen H, Rivas MA, Battle A, Mostafavi S, Monlong J, Sammeth M, Melé M, Reverter F, Goldmann JM, Koller D, Guigó R, McCarthy MI, Dermitzakis ET, Gamazon ER, Im HK, Konkashbaev A, Nicolae DL, Cox NJ, Flutre T, Wen X, Stephens M, Pritchard JK, Tu Z, Zhang B, Huang T, Long Q, Lin L, Yang J, Zhu J, Liu J, Brown A, Mestichelli B, Tidwell D, Lo E, Salvatore M, Shad S, Thomas JA, Lonsdale JT, Moser MT, Gillard BM, Karasik E, Ramsey K, Choi C, Foster BA, Syron J, Fleming J, Magazine H, Hasz R, Walters GD, Bridge JP, Miklos M, Sullivan S, Barker LK, Traino HM, Mosavel M, Siminoff LA, Valley DR, Rohrer DC, Jewell SD, Branton PA, Sobin LH, Barcus M, Qi L, McLean J, Hariharan P, Um KS, Wu S, Tabor D, Shive C, Smith AM, Buia SA, Undale AH, Robinson KL, Roche N, Valentino KM, Britton A, Burges R, Bradbury D, Hambright KW, Seleski J, Korzeniewski GE, Erickson K, Marcus Y, Tejada J, Taherian M, Lu C, Basile M, Mash DC, Volpi S, Struewing JP, Temple GF, Boyer J, Colantuoni D, Little R, Koester S, Carithers LJ, Moore HM, Guan P, Compton C, Sawyer SJ, Demchok JP, Vaught JB, Rabiner CA, Lockhart NC, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET.; Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases., National Cancer Institute (U.S.), National Human Genome Research Institute (U.S.), National Heart, Lung, and Blood Institute, National Institute on Drug Abuse (NIDA), National Institute of Mental Health (U.S.), National Institute of Neurological Disorders and Stroke (U.S.)

The NIH Common Fund GTEx project is designed to serve as a data and post-mortem tissue resource to the research community. The project is testing the role of genomic variation in altering gene expression across a wide array of tissues in a large number of human post-mortem donors. Both data and tissue samples are available to the research community for additional studies.

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues., National Institutes of Health (U.S.) (US NIH to the Broad Institute of Harvard and MIT, R01 DA006227-17), National Institute of Drug Abuse, National Institute of Mental Health (U.S.), National Institute of Neurological Disorders and Stroke (U.S.)

The identification of the transcription factors involved in regulating axon pathfinding is far from complete. One important task for the near future of this field is to identify more transcriptional regulators of axon pathfinding. As we learn more about the nature of these transcription factors, it will be of interest to determine the level of control they exert over the pathway choices of an axon. That is, how directly is transcriptional regulation involved in the dynamic expression patterns that have been demonstrated for certain outgrowth and pathfinding related molecules? Expression of some surface proteins on axons is regulated with exquisite spatial and temporal detail at important decision points during axon outgrowth. For example, in the embryonic rat spinal cord, commissural neurons express TAG-1 while they are extending in the circumferential pathway toward the ventral midline. After the axons cross the midline, they turn abruptly and project in the longitudinal plane. Crossing the midline is associated with the downregulation of TAG-1 on the axons and upregulation of another cell-surface glycoprotein, L1. L1 is specifically expressed on the contralateral axon segment but is never expressed on the ipsilateral segment, cell bodies or dendrites of commissural neurons (Dodd et al. 1988xDodd, J, Morton, S.B, Karagogeos, D, Yamamoto, M, and Jessell, T.M. Neuron. 1988; 1: 105–116Abstract | Full Text PDF | PubMed | Scopus (504)See all ReferencesDodd et al. 1988). Likewise, in the grasshopper and in Drosophila, certain axonal glycoproteins are also selectively expressed in discrete segments of individual axons (2xBastiani, M.J, Harrelson, A.L, Snow, P.M, and Goodman, C.S. Cell. 1987; 48: 745–755Abstract | Full Text PDF | PubMed | Scopus (217)See all References, 10xPatel, N.H, Snow, P.M, and Goodman, C.S. Cell. 1987; 48: 975–988Abstract | Full Text PDF | PubMed | Scopus (356)See all References).In each of these cases, it is not known whether the switch in glycoprotein expression is cell autonomous or whether it is induced by external cues. If it occurs in response to an environmental stimulus the question remains, do transcription factors promote transcription of new message in anticipation of, or in response to the encounter with the stimulus? The latter case implies that a signal is sent from the growth cone back to the cell body altering transcription in the nucleus. However, some growth cones can navigate normally in vivo for several hours after being disconnected from their somas (e.g.,Harris et al. 1987xHarris, W.A, Holt, C.E, and Bonhoeffer, F. Development. 1987; 101: 123–133PubMedSee all ReferencesHarris et al. 1987). Indeed, the distance from the growth cone to the nucleus can be quite large, raising the question of whether this retrograde signaling mechanism is feasible given the current understanding of axonal transport and signaling mechanisms. Thus, transcriptional regulation of pathfinding cues is more likely to occur in advance of guidance cue encounters by growth cones. In this scenario, encounters with axon guidance cues would induce local changes in axonal proteins by one of two methods: either by post-translational alterations in proteins already present in the growth cone or by activating translation of previously transcribed messages located in growth cones or distal axons. Elucidating the signal transduction pathways activated in response to external guidance cues will tell us whether one or both of these mechanisms are at work.The recent identification of transcriptional regulators of axonal projection is an exciting advance that raises interesting questions about the role of transcriptional regulation in axon pathfinding. Primary among them is how tightly regulated are the pathfinding choices of an axon? Does there exist a single transcription factor that can turn on the expression of all of the various receptors and intracellular signaling molecules necessary for an axon to read a particular environment and the positive and negative cues that contribute to guide the growth cone to its appropriate target? Are there combinations of such factors that vary for slightly different projection subtypes (such as suggested in vertebrate motor neurons)? And how much feedback regulation of transcription is plausible from the environment encountered by the growth cone itself? An important challenge will be to determine the genes that are regulated by pathfinding-related transcription factors. This will help to shed light on the precise role of transcriptional regulation in axon pathfinding and will surely lead to the identification of new important players in the regulation of pathway choice and target recognition.