Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease
| dc.contributor.author | Kellis, Manolis | |
| dc.date.accessioned | 2022-07-13T16:43:57Z | |
| dc.date.available | 2022-07-13T16:43:57Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/143716 | |
| dc.description.abstract | Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index. | en_US |
| dc.language.iso | en | |
| dc.publisher | Elsevier BV | en_US |
| dc.relation.isversionof | 10.1016/J.CELL.2021.03.050 | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs License | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.source | PMC | en_US |
| dc.title | Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Kellis, Manolis. 2021. "Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease." Cell, 184 (10). | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
| dc.relation.journal | Cell | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2022-07-13T16:39:45Z | |
| dspace.orderedauthors | de Goede, OM; Nachun, DC; Ferraro, NM; Gloudemans, MJ; Rao, AS; Smail, C; Eulalio, TY; Aguet, F; Ng, B; Xu, J; Barbeira, AN; Castel, SE; Kim-Hellmuth, S; Park, Y; Scott, AJ; Strober, BJ; Brown, CD; Wen, X; Hall, IM; Battle, A; Lappalainen, T; Im, HK; Ardlie, KG; Mostafavi, S; Quertermous, T; Kirkegaard, K; Montgomery, SB; Anand, S; Gabriel, S; Getz, GA; Graubert, A; Hadley, K; Handsaker, RE; Huang, KH; Li, X; MacArthur, DG; Meier, SR; Nedzel, JL; Nguyen, DT; Segrè, AV; Todres, E; Balliu, B; Bonazzola, R; Brown, A; Conrad, DF; Cotter, DJ; Cox, N; Das, S; Dermitzakis, ET; Einson, J; Engelhardt, BE; Eskin, E; Flynn, ED; Fresard, L; Gamazon, ER; Garrido-Martín, D; Gay, NR; Guigó, R; Hamel, AR; He, Y; Hoffman, PJ; Hormozdiari, F; Hou, L; Jo, B; Kasela, S; Kashin, S; Kellis, M; Kwong, A; Li, X; Liang, Y; Mangul, S; Mohammadi, P; Muñoz-Aguirre, M; Nobel, AB; Oliva, M; Park, Y; Parsana, P; Reverter, F; Rouhana, JM; Sabatti, C; Saha, A; Stephens, M; Stranger, BE; Teran, NA; Viñuela, A; Wang, G; Wright, F; Wucher, V; Zou, Y; Ferreira, PG; Li, G; Melé, M; Yeger-Lotem, E; Bradbury, D; Krubit, T; McLean, JA; Qi, L; Robinson, K; Roche, NV; Smith, AM; Tabor, DE; Undale, A; Bridge, J; Brigham, LE; Foster, BA; Gillard, BM; Hasz, R; Hunter, M; Johns, C; Johnson, M; Karasik, E; Kopen, G; Leinweber, WF; McDonald, A; Moser, MT; Myer, K; Ramsey, KD; Roe, B; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Jewell, SD; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Barcus, ME; Branton, PA; Sobin, L; Barker, LK; Gardiner, HM; Mosavel, M; Siminoff, LA; Flicek, P; Haeussler, M; Juettemann, T; Kent, WJ; Lee, CM; Powell, CC; Rosenbloom, KR; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Abell, NS; Akey, J; Chen, L; Demanelis, K; Doherty, JA; Feinberg, AP; Hansen, KD; Hickey, PF; Jasmine, F; Jiang, L; Kaul, R; Kibriya, MG; Li, JB; Li, Q; Lin, S; Linder, SE; Pierce, BL; Rizzardi, LF; Skol, AD; Smith, KS; Snyder, M; Stamatoyannopoulos, J; Tang, H; Wang, M; Carithers, LJ; Guan, P; Koester, SE; Little, AR; Moore, HM; Nierras, CR; Rao, AK; Vaught, JB; Volpi, S | en_US |
| dspace.date.submission | 2022-07-13T16:39:47Z | |
| mit.journal.volume | 184 | en_US |
| mit.journal.issue | 10 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
