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Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
| dc.date.accessioned | 2021-10-27T20:22:50Z | |
| dc.date.available | 2021-10-27T20:22:50Z | |
| dc.date.issued | 2020-12-01 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/135294 | |
| dc.description.abstract | © 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975). | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.isversionof | 10.1038/s41597-020-00642-8 | |
| dc.rights | Creative Commons Attribution 4.0 International license | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.source | Scientific Reports | |
| dc.title | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions | |
| dc.type | Article | |
| dc.relation.journal | Scientific Data | |
| dc.eprint.version | Final published version | |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | |
| dc.date.updated | 2021-03-18T15:07:34Z | |
| dspace.orderedauthors | Sieberts, SK; Perumal, TM; Carrasquillo, MM; Allen, M; Reddy, JS; Hoffman, GE; Dang, KK; Calley, J; Ebert, PJ; Eddy, J; Wang, X; Greenwood, AK; Mostafavi, S; Akbarian, S; Bendl, J; Breen, MS; Brennand, K; Brown, L; Browne, A; Buxbaum, JD; Charney, A; Chess, A; Couto, L; Crawford, G; Devillers, O; Devlin, B; Dobbyn, A; Domenici, E; Filosi, M; Flatow, E; Francoeur, N; Fullard, J; Gil, SE; Girdhar, K; Gulyás-Kovács, A; Gur, R; Hahn, CG; Haroutunian, V; Hauberg, ME; Huckins, L; Jacobov, R; Jiang, Y; Johnson, JS; Kassim, B; Kim, Y; Klei, L; Kramer, R; Lauria, M; Lehner, T; Lewis, DA; Lipska, BK; Montgomery, K; Park, R; Rosenbluh, C; Roussos, P; Ruderfer, DM; Senthil, G; Shah, HR; Sloofman, L; Song, L; Stahl, E; Sullivan, P; Visintainer, R; Wang, J; Wang, YC; Wiseman, J; Xia, E; Zhang, W; Zharovsky, E; Addis, L; Addo, SN; Airey, DC; Arnold, M; Bennett, DA; Bi, Y; Biber, K; Blach, C; Bradhsaw, E; Brennan, P; Canet-Aviles, R; Cao, S; Cavalla, A; Chae, Y; Chen, WW; Cheng, J; Collier, DA; Dage, JL; Dammer, EB; Davis, JW; Davis, J; Drake, D; Duong, D; Eastwood, BJ; Ehrlich, M; Ellingson, B; Engelmann, BW; Esmaeelinieh, S; Felsky, D; Funk, C; Gaiteri, C | |
| dspace.date.submission | 2021-03-18T15:07:35Z | |
| mit.journal.volume | 7 | |
| mit.journal.issue | 1 | |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed |
