| Title: |
A statistical framework for cross-tissue transcriptome-wide association analysis |
| Authors: |
Hu, Y; Li, M; Lu, Q; Weng, H; Wang, J; Zekavat, SM; Yu, Z; Li, B; Gu, J; Muchnik, S; Shi, Y; Kunkle, BW; Mukherjee, S; Natarajan, P; Naj, A; Kuzma, A; Zhao, Y; Crane, PK; Lu, H; Zhao, H; Abner, E; Adams, PM; Albert, MS; Albin, RL; Apostolova, LG; Arnold, SE; Asthana, S; Atwood, CS; Baldwin, CT; Barber, RC; Barnes, LL; Barral, S; Beach, TG; Becker, JT; Beecham, GW; Beekly, D; Bennett, DA; Bigio, EH; Bird, TD; Blacker, D; Boeve, BF; Bowen, JD; Boxer, A; Burke, JR; Burns, JM; Buxbaum, JD; Cairns, NJ; Cantwell, LB; Cao, C; Carlson, CS; Carlsson, CM; Carney, RM; Carrasquillo, MM; Chui, HC; Cribbs, DH; Crocco, EA; Cruchaga, C; De Jager, PL; DeCarli, C; Dick, M; Dickson, DW; Doody, RS; Duara, R; Ertekin-Taner, N; Evans, DA; Faber, KM; Fairchild, TJ; Fallon, KB; Fardo, DW; Farlow, MR; Farrer, LA; Ferris, S; Foroud, TM; Frosch, MP; Galasko, DR; Gearing, M; Geschwind, DH; Ghetti, B; Gilbert, JR; Goate, AM; Graff-Radford, NR; Green, RC; Growdon, JH; Haines, JL; Hakonarson, H; Hamilton, RL; Hamilton-Nelson, KL; Hardy, J; Harrell, LE; Honig, LS; Huebinger, RM; Huentelman, MJ; Hulette, CM; Hyman, BT; Jarvik, GP; Jin, L-W; Jun, G; Kamboh, MI; Karydas, A; Katz, MJ; Kauwe, JSK; Kaye, JA; Keene, CD; Kim, R; Kowall, NW; Kramer, JH; Kukull, WA; Kuzma, AP; LaFerla, FM; Lah, JJ; Larson, EB; Leverenz, JB; Levey, A; Li, G; Lieberman, AP; Lipton, RB; Lopez, OL; Lunetta, KL; Lyketsos, CG; Malamon, J; Marson, DC; Martin, ER; Martiniuk, F; Mash, DC; Masliah, E; Mayeux, R; McCormick, WC; McCurry, SM; McDavid, AN; McDonough, S; McKee, AC; Mesulam, M; Miller, BL; Miller, CA; Miller, JW; Montine, TJ; Morris, JC; Myers, AJ; Naj, AC; O'Bryant, S; Olichney, JM; Parisi, JE; Paulson, HL; Pericak-Vance, MA; Peskind, E; Petersen, RC; Pierce, A; Poon, WW; Potter, H; Qu, L; Quinn, JF; Raj, A; Raskind, M; Reiman, EM; Reisberg, B; Reisch, JS; Reitz, C; Ringman, JM; Roberson, ED; Rogaeva, E; Rosen, HJ; Rosenberg, RN; Royall, DR; Sager, MA; Sano, M; Saykin, AJ; Schellenberg, GD; Schneider, JA; Schneider, LS; Seeley, WW; Smith, AG; Sonnen, JA; Spina, S; St George-Hyslop, P; Stern, RA; Swerdlow, RH; Tanzi, RE; Trojanowski, JQ; Troncoso, JC; Tsuang, DW; Valladares, O; Van Deerlin, VM; Van Eldik, LJ; Vardarajan, BN; Vinters, HV; Vonsattel, JP; Wang, L-S; Weintraub, S; Welsh-Bohmer, KA; Wilhelmsen, KC; Williamson, J; Wingo, TS; Woltjer, RL; Wright, CB; Wu, C-K; Younkin, SG; Yu, C-E; Yu, L |
| Source: |
Nature Genetics , 51 (3) pp. 568-576. (2019) |
| Publisher Information: |
NATURE PUBLISHING GROUP |
| Publication Year: |
2019 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Science & Technology; Life Sciences & Biomedicine; Genetics & Heredity; GENE-EXPRESSION; IDENTIFIES VARIANTS; INTEGRATIVE ANALYSIS; SUSCEPTIBILITY LOCI; COMMON VARIANTS; RISK PREDICTION; ALZHEIMERS; GWAS; METAANALYSIS; DISEASE |
| Description: |
Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene–trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10084678/1/Hu_NG_2019.pdf; https://discovery.ucl.ac.uk/id/eprint/10084678/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10084678/1/Hu_NG_2019.pdf; https://discovery.ucl.ac.uk/id/eprint/10084678/ |
| Rights: |
open |
| Accession Number: |
edsbas.A4D8097A |
| Database: |
BASE |