期刊
NATURE COMMUNICATIONS
卷 9, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-018-03621-1
关键词
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资金
- US National Institutes of Health [R01MH107666, T32 MH020065, R01 MH101820, P30 DK20595, F31 DK101202, P50 DA037844, P50 MH094267]
- Loyola University Chicago
- Common Fund of the Office of the Director of the National Institutes of Health
- NCI
- NHGRI
- NHLBI
- NIDA
- NIMH
- NINDS
- NCI SAIC-Frederick, Inc. (SAIC-F) [10XS170, 10XS171, X10S172]
- Laboratory, Data Analysis, and Coordinating Center (LDACC) [HHSN268201000029C]
- SAIC-F [10ST1035, HHSN261200800001E]
- Wellcome Trust [076113, 085475]
- Biological Sciences Division at the University of Chicago
- Institute for Translational Medicine, CTSA from the National Institutes of Health [UL1 TR000430]
- [DA006227]
- [DA033684]
- [N01MH000028]
- [MH090941]
- [MH101814]
- [MH090951]
- [MH090937]
- [MH101820]
- [MH101825]
- [MH090936]
- [MH101819]
- [MH090948]
- [MH101782]
- [MH101810]
- [MH101822]
Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Mono-genic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.
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