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The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
Open accessAbstract
Expression, genetic variation, and tissues: Human genomes show extensive genetic variation across individuals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within individuals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem samples covering 54 body sites from 175 individuals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 ; see also p. 640
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“For the second stage of vision-language alignment (stage 3 of TITAN), we curated a dataset of 182,862 slide-report pairs from a combination of in-house clinical reports and pathology notes from the GTEx consortium129.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [129]
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“Projects from large consortia like GTEx [156], the Epigenome Roadmap [176] and ENCODE [15] are already generating data from multiple-omics levels that facilitate these integrated analyses.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [156]
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“NaV1.2 is widely expressed throughout the human central nervous system, but not in peripheral tissues [7,8].”
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“Such regulatory relationships show substantial heterogeneity across human cell types, tissues, and developmental stages (10), and are often highly species-specific (11).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [10]
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“Corroborating this hypothesis, GTEx data show multiple eQTLs within these enhancers that are associated with alterations in SCN1A expression in humans49.”
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“We found strong significant associations (false discovery rate (FDR)<0.05) between a SNP and expression of nearby genes in subcutaneous adipose tissues (Supplementary Data 13).”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [29]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Self-citationBEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Sólymos, P., Stevens, M. H. H., Szöcs, E., et al. (2001) · doi:10.32614/cran.package.vegan · Cited by 27,099
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,457
Self-citationPrincipal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
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Research works printed but not linked: 1
- H. Ongen E.T. Dermitzakis http://biorxiv.org/content/early/2015/01/22/014126 (2015).
Cited by
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Items 501–600 of 1,042
Moon, J. S., Goeminne, L. J. E., Kim, J. T., Tian, J. W., Kim, S., Nga, H. T., Kang, S. G., Kang, B. E., Byun, J., Lee, Y., et al. (2020) · Aging Cell · doi:10.1111/acel.13195 · Cited by 141
Chahal, H. S., Lin, Y., Ransohoff, K. J., Hinds, D. A., Wu, W., Dai, H., Qureshi, A. A., Li, W., Kraft, P. L., Tang, J. Y., et al. (2016) · Nature Communications · doi:10.1038/ncomms12048 · Cited by 141
Morris, A. P., Le, T. H., Wu, H., Akbarov, A., van der Most, P. J., Hemani, G., Smith, G. D., Mahajan, A., Gaulton, K. J., Nadkarni, G. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07867-7 · Cited by 141
Fukunaga, T., Iwakiri, J., Ono, Y., & Hamada, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00462 · Cited by 141
Allele-specific expression reveals interactions between genetic variation and environment
Knowles, D. A., Davis, J. R., Edgington, H. A., Raj, A., Favé, M.-J., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Levinson, D. F., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4298 · Cited by 141
Self-citationMattioli, K., Volders, P.-J., Gerhardinger, C., Lee, J. C., Maass, P. G., Melé, M., & Rinn, J. L. (2019) · Genome Research · doi:10.1101/gr.242222.118 · Cited by 141
Predicting functional effects of missense variants in voltage-gated sodium and calcium channels
Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140
Regulation of ribosome biogenesis in skeletal muscle hypertrophy
Figueiredo, V. C., & McCarthy, J. J. (2019) · Physiology · doi:10.1152/physiol.00034.2018 · Cited by 140
Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD
Sun, W., Kechris, K. J., Jacobson, S. R., Drummond, M., Hawkins, G. A., Yang, J., Chen, T., Quibrera, P. M., Anderson, W. H., Barr, R. G., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006011 · Cited by 140
Self-citationA rare IL33 loss-of-function mutation reduces blood eosinophil counts and protects from asthma
Smith, D. E., Helgason, H., Sulem, P., Björnsdóttir, U. S., Lim, A. C., Sveinbjörnsson, G., Hasegawa, H., Brown, M. E., Ketchem, R. R., Gavala, M. L., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006659 · Cited by 140
Carbonic anhydrases in metazoan model organisms: molecules, mechanisms, and physiology
Aspatwar, A., Tolvanen, M. E. E., Barker, H. R., Syrjänen, L., Valanne, S., Purmonen, S., Waheed, A., Sly, W. S., & Parkkila, S. (2022) · Physiological Reviews · doi:10.1152/physrev.00018.2021 · Cited by 140
Single-cell genomic and transcriptomic landscapes of primary and metastatic colorectal cancer tumors
Wang, R., Li, J., Zhou, X., Mao, Y., Wang, W., Gao, S., Wang, W., Gao, Y., Chen, K., Yu, S., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01093-z · Cited by 140
Defining essential genes for human pluripotent stem cells by CRISPR–Cas9 screening in haploid cells
Yilmaz, A., Peretz, M., Aharony, A., Sagi, I., & Benvenisty, N. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0088-1 · Cited by 139
Girdhar, K., Hoffman, G. E., Jiang, Y., Brown, L., Kundakovic, M., Hauberg, M. E., Francoeur, N. J., Wang, Y.-C., Shah, H., Kavanagh, D. H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0187-0 · Cited by 139
Meta-analysis of RNA-seq expression data across species, tissues and studies
Sudmant, P. H., Alexis, M. S., & Burge, C. B. (2015) · Genome biology · doi:10.1186/s13059-015-0853-4 · Cited by 139
Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues
Wheeler, H. E., Shah, K. P., Brenner, J., Garcia, T., Aquino-Michaels, K., Cox, N. J., Nicolae, D. L., & Im, H. K. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006423 · Cited by 139
Self-citationHancock, D. B., Guo, Y., Reginsson, G. W., Gaddis, N., Lutz, S. M., Sherva, R., Loukola, A., Minică, C. C., Markunas, C. A., Han, Y., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.193 · Cited by 139
Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability
Morgan, M. D., Pairo‐Castineira, E., Rawlik, K., Canela‐Xandri, O., Rees, J., Sims, D., Tenesa, A., & Jackson, I. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07691-z · Cited by 139
Violante, S. N., Achetib, N., van Roermund, C. W. T., Hagen, J., Dodatko, T., Vaz, F. M., Waterham, H. R., Chen, H., Baes, M. I., Yu, C., et al. (2018) · The FASEB Journal · doi:10.1096/fj.201801498r · Cited by 139
Irmisch, A., Bonilla, X., Chevrier, S., Lehmann, K.-V., Singer, F., Toussaint, N. C., Esposito, C., Mena, J., Milani, E. S., Casanova, R., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.01.004 · Cited by 138
Self-citationThe genetic architecture of structural left–right asymmetry of the human brain
Sha, Z., Schijven, D., Carrión-Castillo, A., Joliot, M., Mazoyer, B. M., Fisher, S. E., Crivello, F., & Francks, C. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01069-w · Cited by 138
Genome-wide association study implicates CHRNA2 in cannabis use disorder
Demontis, D., Rajagopal, V. M., Thorgeirsson, T. E., Als, T. D., Grove, J., Leppälä, K., Gudbjartsson, D. F., Pallesen, J., Hjorthøj, C. R., Reginsson, G. W., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0416-1 · Cited by 137
Combinatorial expression of GPCR isoforms affects signalling and drug responses
Martí-Solano, M., Crilly, S. E., Malinverni, D., Munk, C., Harris, M., Pearce, A., Quon, T., Mackenzie, A. E., Wang, X., Peng, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2888-2 · Cited by 136
Genetic prediction of complex traits with polygenic scores: a statistical review
Ma, Y., & Zhou, X. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.06.004 · Cited by 136
Thériault, S., Gaudreault, N., Lamontagne, M., Rosa, M., Boulanger, M.-C., Messika-Zeitoun, D., Clavel, M.-A., Capoulade, R., Dagenais, F., Pibarot, P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03260-6 · Cited by 135
The genetic background of inflammatory bowel disease: from correlation to causality
Venema, W. T. U., Voskuil, M. D., Dijkstra, G. D. H., Weersma, R. K., & Festen, E. A. M. (2016) · The Journal of Pathology · doi:10.1002/path.4817 · Cited by 135
Hemani, G., Zheng, J., Wade, K. H., Laurin, C., Elsworth, B., Burgess, S., Bowden, J., Langdon, R., Tan, V. Y., Yarmolinsky, J., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078972 · Cited by 135
Wang, Q., Chen, R., Cheng, F., Wei, Q., Ji, Y., Hai, Y., Zhong, X. Y., Tao, R., Wen, Z., Sutcliffe, J. S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0382-7 · Cited by 134
Self-citationWerling, D. M., Pochareddy, S., Choi, J., An, J., Sheppard, B. K., Peng, M., Li, Z., Dastmalchi, C., Santpere, G., Sousa, A. M. M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.053 · Cited by 134
Self-citationRodin, R. E., Dou, Y., Kwon, M., Sherman, M. A., D’Gama, A. M., Doan, R. N., Rento, L. M., Girskis, K. M., Bohrson, C. L., Kim, S. N., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00765-6 · Cited by 135
Khetarpal, S. A., Schjoldager, K. T.-B. G., Christoffersen, C., Raghavan, A., Edmondson, A. C., Reutter, H. M., Bouhouche, A., Ouazzani, R., Peloso, G. M., Vitali, C., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.07.012 · Cited by 133
Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis
Ishigaki, K., Kochi, Y., Suzuki, A., Tsuchida, Y., Tsuchiya, H., Sumitomo, S., Yamaguchi, K., Nagafuchi, Y., Nakachi, S., Kato, R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3885 · Cited by 133
Genetic advances in systemic lupus erythematosus: an update
Chen, L., Morris, D. L., & Vyse, T. J. (2017) · Current Opinion in Rheumatology · doi:10.1097/bor.0000000000000411 · Cited by 132
Controlling the Rate of GWAS False Discoveries
Brzyski, D., Peterson, C. B., Sobczyk, P., Candès, E. J., Bogdan, M., & Sabatti, C. (2016) · Genetics · doi:10.1534/genetics.116.193987 · Cited by 132
DNMT1 as a therapeutic target in pancreatic cancer: mechanisms and clinical implications
Wong, K. K. (2020) · Cellular Oncology · doi:10.1007/s13402-020-00526-4 · Cited by 132
Wu, S., Fukumoto, T., Lin, J., Nacarelli, T., Wang, Y., Ong, D., Liu, H., Fatkhutdinov, N., Zundell, J. A., Karakashev, S. V., et al. (2021) · Nature Cancer · doi:10.1038/s43018-020-00160-x · Cited by 132
Self-citationDavis, J. R., Frésard, L., Knowles, D. A., Pala, M., Bustamante, C. D., Battle, A. J., & Montgomery, S. B. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.021 · Cited by 131
Self-citationThe state of play in higher eukaryote gene annotation
Mudge, J. M., & Harrow, J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.119 · Cited by 132
Characterization of noncoding regulatory DNA in the human genome
Elkon, R., & Agami, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3863 · Cited by 132
He, W., Tu, C., Liu, Q., Meng, L., Yuan, S., Luo, A., He, F.-S., Shen, J., Li, W., Du, J., et al. (2018) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2017-104992 · Cited by 131
Genetic Associations between Voltage-Gated Calcium Channels and Psychiatric Disorders
Andrade, A., Brennecke, A., Mallat, S., Brown, J., Gomez-Rivadeneira, J., Czepiel, N., & Londrigan, L. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20143537 · Cited by 131
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Harmanci, A. O., & Gerstein, M. (2016) · Nature Methods · doi:10.1038/nmeth.3746 · Cited by 130
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Huda, M. N., Kim, M., & Bennett, B. J. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.632335 · Cited by 130
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Schon, K. R., Ratnaike, T. E., van den Ameele, J., Horváth, R., & Chinnery, P. F. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.06.009 · Cited by 129
Hukku, A., Pividori, M., Luca, F., Piqué-Regi, R., Im, H. K., & Wen, X. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.11.012 · Cited by 129
Self-citationCostanzo, M. C., von Grotthuss, M., Massung, J., Jang, D., Caulkins, L., Koesterer, R., Gilbert, C., Welch, R., Kudtarkar, P., Hoang, Q., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.03.001 · Cited by 129
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Wang, G.-Z., Belgard, T. G., Mao, D., Chen, L., Berto, S., Preuss, T. M., Lu, H., Geschwind, D. H., & Konopka, G. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.022 · Cited by 129
Cichońska, A., Rousu, J., Marttinen, P., Kangas, A. J., Soininen, P., Lehtimäki, T. J., Raitakari, O. T., Järvelin, M., Salomaa, V. V., Ala‐Korpela, M., et al. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw052 · Cited by 128
Costantino, S., Libby, P., Kishore, R., Tardif, J., El‐Osta, A., & Paneni, F. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx568 · Cited by 128
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Cannataro, V. L., Gaffney, S. G., & Townsend, J. P. (2018) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djy168 · Cited by 128
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Gibson, J., Russ, T. C., Clarke, T., Howard, D. M., Hillary, R. F., Evans, K. L., Walker, R. M., Bermingham, M. L., Morris, S. W., Campbell, A., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008104 · Cited by 128
Pearson, B. L., Simon, J. M., McCoy, E. S., Salazar, G., Fragola, G., & Zylka, M. J. (2016) · Nature Communications · doi:10.1038/ncomms11173 · Cited by 127
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Shaul, Y. D., Yuan, B., Thiru, P., Nutter-Upham, A., McCallum, S., Lanzkron, C., Bell, G. W., & Sabatini, D. M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1337 · Cited by 127
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Kan, M., Shumyatcher, M., & Himes, B. E. (2017) · Respiratory Research · doi:10.1186/s12931-017-0631-9 · Cited by 129
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Guo, C., McDowell, I. C., Nodzenski, M., Scholtens, D. M., Allen, A. S., Lowe, W. L., & Reddy, T. E. (2017) · BMC Genomics · doi:10.1186/s12864-017-3785-4 · Cited by 129
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Zhang, W., Bojorquez-Gomez, A., Velez, D. O., Xu, G., Sanchez, K. S., Shen, J. P., Chen, K., Licon, K., Melton, C. A., Olson, K. M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0091-2 · Cited by 126
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Wang, M. H., Cordell, H. J., & Steen, K. V. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.04.008 · Cited by 126
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Fang, Z., Weng, C., Li, H., Tao, R., Mai, W., Liu, X., Lu, L., Lai, S., Duan, Q., Álvarez‐González, C. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.043 · Cited by 126
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Scott, L. J., Erdos, M. R., Huyghe, J. R., Welch, R., Beck, A. T., Wolford, B. N., Chines, P. S., Didion, J. P., Narisu, N., Stringham, H. M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11764 · Cited by 125
Self-citationMunz, M., Willenborg, C., Richter, G. M., Jockel‐Schneider, Y., Graetz, C., Staufenbiel, I., Wellmann, J., Berger, K., Krone, B., Hoffmann, P., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx151 · Cited by 125
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Favé, M.-J., Lamaze, F. C., Soave, D. M., Hodgkinson, A., Gauvin, H., Bruat, V., Grenier, J., Gbeha, E., Skead, K., Smargiassi, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03202-2 · Cited by 125
Initiative, T. I. S. C., Allison, T. F., Andrews, P. W., Avior, Y., Barbaric, I., Benvenisty, N., Bock, C., Brehm, J. L., Brüstle, O., Damjanov, I., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04011-3 · Cited by 125
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Xie, R., Wen, J., Quitadamo, A., Cheng, J., & Shi, X. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4226-0 · Cited by 125
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Gottschlich, A., Thomas, M., Grünmeier, R., Lesch, S., Rohrbacher, L., Igl, V., Briukhovetska, D., Benmebarek, M.-R., Vick, B., Dede, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01684-0 · Cited by 123
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Sandoval, G. J., Pulice, J. L., Pakula, H., Schenone, M., Takeda, D. Y., Pop, M. S., Boulay, G., Williamson, K. E., McBride, M. J., Pan, J., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.040 · Cited by 122
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Banovich, N. E., Li, Y., Raj, A., Ward, M. C., Greenside, P., Calderon, D., Tung, P., Burnett, J. E., Myrthil, M., Thomas, S. M., et al. (2017) · Genome Research · doi:10.1101/gr.224436.117 · Cited by 122
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Initiative, T. C.-1. H. G., & Ganna, A. (2021) · medRxiv · doi:10.1101/2021.03.10.21252820 · Cited by 122
C14ORF39/SIX6OS1 is a constituent of the synaptonemal complex and is essential for mouse fertility
Gómez-H, L., Felipe‐Medina, N., Sánchez‐Martín, M., Davies, O. R., Ramos, I., García‐Tuñón, I., de Rooij, D. G., Dereli, I., Tóth, A., Barbero, J. L., et al. (2016) · Nature Communications · doi:10.1038/ncomms13298 · Cited by 121
Genetic architecture of epigenetic and neuronal ageing rates in human brain regions
Lu, A. T.-H., Hannon, E. J., Levine, M. E., Crimmins, E. M., Lunnon, K., Mill, J. S., Geschwind, D. H., & Horvath, S. (2017) · Nature Communications · doi:10.1038/ncomms15353 · Cited by 121
Phenotypic Consequences of a Genetic Predisposition to Enhanced Nitric Oxide Signaling
Emdin, C. A., Khera, A. V., Klarin, D. M., Natarajan, P., Zekavat, S. M., Nomura, A., Haas, M. E., Aragam, K. G., Ardissino, D., Wilson, J. G., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.028021 · Cited by 121
Marenholz, I., Grosche, S., Kalb, B., Rüschendorf, F., Blümchen, K., Schlags, R., Harandi, N., Price, M., Hansen, G., Seidenberg, J., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01220-0 · Cited by 121
High-throughput allele-specific expression across 250 environmental conditionsPreprint
Moyerbrailean, G. A., Richards, A. L., Kurtz, D. A., Kalita, C. A., Davis, G. O., Harvey, C. T., Alazizi, A., Watza, D., Sorokin, Y., Hauff, N., et al. (2016) · Genome Research · doi:10.1101/gr.209759.116 · Cited by 124
Self-citationTelonis, A. G., & Rigoutsos, I. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-1947 · Cited by 120
Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration
Lim, Y. W., Chen‐Harris, H., Mayba, O., Lianoglou, S., Wüster, A., Bhangale, T., Khan, Z., Mariathasan, S., Daemen, A., Reeder, J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804506115 · Cited by 120
Pan, J., McKenzie, Z. M., D’Avino, A. R., Mashtalir, N., Lareau, C. A., Pierre, R. S., Wang, L., Shilatifard, A., & Kadoch, C. (2019) · Nature Genetics · doi:10.1038/s41588-019-0363-5 · Cited by 120
Genome-wide association study of musical beat synchronization demonstrates high polygenicity
Niarchou, M., Gustavson, D. E., Sathirapongsasuti, J. F., Anglada-Tort, M., Eising, E., Bell, E., McArthur, E., Straub, P., Aslibekyan, S. W., Auton, A., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01359-x · Cited by 123
Salvage of ribose from uridine or RNA supports glycolysis in nutrient-limited conditions
Skinner, O. S., Blanco-Fernández, J., Goodman, R. P., Kawakami, A., Shen, H., Kemény, L. V., Joesch-Cohen, L. M., Rees, M. G., Roth, J. A., Fisher, D. E., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00774-2 · Cited by 120
Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk
Tehranchi, A. K., Myrthil, M., Martin, T., Hie, B. L., Golan, D., & Fraser, H. B. (2016) · Cell · doi:10.1016/j.cell.2016.03.041 · Cited by 120
Ng, M. C. Y., Graff, M., Lu, Y., Justice, A. E., Mudgal, P., Liu, C., Young, K. L., Yanek, L. R., Feitosa, M. F., Wojczynski, M. K., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006719 · Cited by 119
Identification and Analysis of Human Sex-Biased MicroRNAs
Cui, C., Yang, W., Shi, J., Zhou, Y., Yang, J., Cui, Q., & Zhou, Y. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.03.004 · Cited by 119
Self-citationGenome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence
Jansen, P. R., Nagel, M., Watanabe, K., Wei, Y., Savage, J. E., de Leeuw, C., van den Heuvel, M. P., van der Sluis, S., & Posthuma, D. (2020) · Nature Communications · doi:10.1038/s41467-020-19378-5 · Cited by 119
Toward Best Practices for Imaging Transcriptomics of the Human Brain
Arnatkevičiūtė, A., Markello, R. D., Fulcher, B., Mišić, B., & Fornito, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.016 · Cited by 119
Lock, E. F. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1401544 · Cited by 118
Overlap of Genetic Risk between Interstitial Lung Abnormalities and Idiopathic Pulmonary Fibrosis
Hobbs, B. D., Putman, R. K., Araki, T., Nishino, M., Guðmundsson, G., Gudnason, V. G., Eiríksdóttir, G., Zilhäo, N. R., Dupuis, J., Xu, H., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201903-0511oc · Cited by 118
SigCom LINCS: data and metadata search engine for a million gene expression signatures
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Burgess, S. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.26250 · Cited by 118
Predictability of human differential gene expression
Crow, M., Lim, N., Ballouz, S., Pavlidis, P., & Gillis, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802973116 · Cited by 117
Profiling cancer testis antigens in non–small-cell lung cancer
Djureinovic, D., Hallström, B. M., Horie, M., Mattsson, J. S. M., Fleur, L. L., Fagerberg, L., Brunnström, H., Lindskog, C., Madjar, K., Rahnenführer, J., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.86837 · Cited by 117
Accurate and sensitive quantification of protein-DNA binding affinity
Rastogi, C., Rube, H. T., Kribelbauer, J. F., Crocker, J., Loker, R., Martini, G. D., Laptenko, O., Freed-Pastor, W. A., Prives, C. L., Stern, D. L., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714376115 · Cited by 117
Genome-wide meta-analysis reveals shared new loci in systemic seropositive rheumatic diseases
Acosta‐Herrera, M., Kerick, M., González‐Serna, D., Wijmenga, C., Franke, A., Gregersen, P. K., Padyukov, L., Worthington, J., Vyse, T. J., Alarcón-Riquelme, M. E., et al. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-214127 · Cited by 117
Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk
Huo, Y., Li, S., Liu, J., Li, X., & Luo, X. (2019) · Nature Communications · doi:10.1038/s41467-019-08666-4 · Cited by 117
Self-citationGenome-wide association study of cerebral small vessel disease reveals established and novel loci
Chung, J., Marini, S., Pera, J., Norrving, B., Jiménez-Conde, J., Roquer, J., Fernández‐Cadenas, I., Tirschwell, D., Selim, M. H., Brown, D. L., et al. (2019) · Brain · doi:10.1093/brain/awz233 · Cited by 117
Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study
Yu Chen, H., Dina, C., Small, A. M., Shaffer, C. M., Levinson, R. T., Helgadóttir, A., Capoulade, R., Munter, H. M., Martinsson, A., Cairns, B. J., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad142 · Cited by 116
Lessons from ten years of genome‐wide association studies of asthma
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Clinical application of next-generation sequencing to the practice of neurology
Rexach, J. E., Lee, H., Martínez-Agosto, J. A., Nemeth, A. H., & Fogel, B. L. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30033-x · Cited by 117
An expanded landscape of human long noncoding RNA
Jiang, S., Cheng, S., Ren, L., Wang, Q., Kang, Y.-J., Ding, Y., Hou, M. L., Yang, X., Lin, Y., Liang, N., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz621 · Cited by 116
Metadata
- Authors
- The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., Lek, M., Ward, L. D., Kheradpour, P., Iriarte, B., Meng, Y., Palmer, C. D., Esko, T., Winckler, W., Hirschhorn, J. N., Kellis, M., MacArthur, D. G., Getz, G., Shabalin, A. A., Li, G., Zhou, Y.-H., Nobel, A. B., Rusyn, I., Wright, F. A., Lappalainen, T., Ferreira, P. G., Ongen, H., Rivas, M. A., Battle, A., Mostafavi, S., Monlong, J., Sammeth, M., Mele, M., Reverter, F., Goldmann, J. M., Koller, D., Guigó, R., McCarthy, M. I., Dermitzakis, E. T., Gamazon, E. R., Im, H. K., Konkashbaev, A., Nicolae, D. L., Cox, N. J., Flutre, T., Wen, X., Stephens, M., Pritchard, J. K., 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, J. A., Lonsdale, J. T., Moser, M. T., Gillard, B. M., Karasik, E., Ramsey, K., Choi, C., Foster, B. A., Syron, J., Fleming, J., Magazine, H., Hasz, R., Walters, G. D., Bridge, J. P., Miklos, M., Sullivan, S., Barker, L. K., Traino, H. M., Mosavel, M., Siminoff, L. A., Valley, D. R., Rohrer, D. C., Jewell, S. D., Branton, P. A., Sobin, L. H., Barcus, M., Qi, L., McLean, J., Hariharan, P., Um, K. S., Wu, S., Tabor, D., Shive, C., Smith, A. M., Buia, S. A., Undale, A. H., Robinson, K. L., Roche, N., Valentino, K. M., Britton, A., Burges, R., Bradbury, D., Hambright, K. W., Seleski, J., Korzeniewski, G. E., Erickson, K., Marcus, Y., Tejada, J., Taherian, M., Lu, C., Basile, M., Mash, D. C., Volpi, S., Struewing, J. P., Temple, G. F., Boyer, J., Colantuoni, D., Little, R., Koester, S., Carithers, L. J., Moore, H. M., Guan, P., Compton, C., Sawyer, S. J., Demchok, J. P., Vaught, J. B., Rabiner, C. A., Lockhart, N. C., Ardlie, K. G., Getz, G., Wright, F. A., Kellis, M., Volpi, S., & Dermitzakis, E. T.
- Affiliations
- Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales (FR)
- ORCID
- Deluca, David S.
- Segrè, Ayellet V.
- Young, Taylor R.
- Maller, Julian B.
- Tukiainen, Taru
- Lek, Monkol
- Ward, Lucas D.
- Meng, Yan
- Palmer, Cameron D.
- Esko, Tõnu
- Winckler, Wendy
- Hirschhorn, Joel N.
- MacArthur, Daniel G.
- Shabalin, Andrey A.
- Li, Gen
- Zhou, Yi-Hui
- Nobel, Andrew B.
- Rusyn, Ivan
- Lappalainen, Tuuli
- Ferreira, Pedro G.
- Ongen, Halit
- Rivas, Manuel A.
- Battle, Alexis
- Mostafavi, Sara
- Monlong, Jean
- Sammeth, Michael
- Goldmann, Jakob M.
- Guigó, Roderic
- McCarthy, Mark I.
- Gamazon, Eric R.
- Im, Hae Kyung
- Konkashbaev, Anuar
- Nicolae, Dan L.
- Cox, Nancy J.
- Flutre, Timothée
- Wen, Xiaoquan
- Stephens, Matthew
- Pritchard, Jonathan K.
- Tu, Zhidong
- Zhang, Bin
- Huang, Tao
- Long, Quan
- Lin, Luan
- Yang, Jialiang
- Zhu, Jun
- Moser, Michael T.
- Gillard, Bryan M.
- Choi, Christopher
- Foster, Barbara A.
- Mosavel, Maghboeba
- Siminoff, Laura A.
- Rohrer, Daniel C.
- Jewell, Scott D.
- Branton, Philip A.
- Tabor, David
- Lu, Chunrong
- Mash, Deborah C.
- Struewing, Jeffery P.
- Moore, Helen M.
- Compton, Carolyn
- Sawyer, Sherilyn J.
- Venue
- Science
- Published
- 2015-05-08
- Volume
- 348
- Issue
- 6235
- Pages
- 648-660
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4547484
- OpenAlex
- W1533942137
- Cited by
- 5,062
- References
- 82
- Citations by year
- 2026: 171 · 2025: 315 · 2024: 417 · 2023: 385 · 2022: 417 · 2021: 621 · 2020: 587 · 2019: 516 · 2018: 610 · 2017: 516 · 2016: 398 · 2015: 103
- FWCI
- 371.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- HAL (submittedVersion)
- UNC Libraries
- Funders
- U.S. National Institutes of Health
- NIH (HHSN261200800001E, R01 DA006227-17, R01 MH090941, R01 MH090951, R01 MH090937, R01 MH090936, R01 MH090948, R01 GM104371, R01AG046170, R01CA163772, U01AI111598-01)
- NIMH
- NIDA
- NIA
- NHLBI
- NHGRI
- NCI
- NIAID
- NINDS
- Office of the Director
- Roswell Park Cancer Institute (10XS171)
- Wellcome Trust (98381)
- European Research Council
- Louis-Jeantet Foundation
- Swiss National Science Foundation
- NDRI (10XS170)
- Science Care Inc. (10X172)
- IDOX (12ST1039)
- Van Andel Institute (10ST1035)
- Broad Institute (HHSN268201000029C)
- Clarendon Scholarship
- NDM Studentship
- Univ. of Oxford Green Templeton College Award
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- gene expression, tissue-specific gene regulation, expression quantitative trait loci, human genetic variation, regulatory networks, genome-wide association study, GTEx, RNA sequencing, tissue heterogeneity
- MeSH terms
- Alleles, Blood Pressure, Disease, Gene Expression Regulation, Multigene Family, Genotype, Humans, Organ Specificity, Pilot Projects, RNA Splicing, Genetic Variation, Genome, Human, Tibial Arteries, Sequence Analysis, RNA, GTPase-Activating Proteins, RNA, Untranslated, Quantitative Trait Loci, Gene Regulatory Networks, Genome-Wide Association Study, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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