A statistical framework for cross-tissue transcriptome-wide association analysis

被引：248

作者：

Hu, Yiming ^{[1
]}

Li, Mo ^{[1
]}

Lu, Qiongshi ^{[2
]}

Weng, Haoyi ^{[3
]}

Wang, Jiawei ^{[4
]}

Zekavat, Seyedeh M. ^{[5
,6
,7
]}

Yu, Zhaolong ^{[4
]}

Li, Boyang ^{[1
]}

Gu, Jianlei ^{[8
]}

Muchnik, Sydney ^{[9
]}

Shi, Yu ^{[1
]}

Kunkle, Brian W. ^{[10
]}

Mukherjee, Shubhabrata ^{[11
]}

Natarajan, Pradeep ^{[6
,7
,12
,13
]}

Naj, Adam ^{[14
,15
,16
]}

Kuzma, Amanda ^{[16
]}

Zhao, Yi ^{[16
]}

Crane, Paul K. ^{[11
]}

Lu, Hui ^{[8
]}

Zhao, Hongyu ^{[1
,4
,8
,9
]}

Abner, Erin ^{[17
]}

Adams, Perrie M. ^{[18
]}

Albert, Marilyn S. ^{[19
]}

Albin, Roger L. ^{[20
,21
,22
]}

Apostolova, Liana G. ^{[23
,24
,25
,26
]}

Arnold, Steven E. ^{[27
]}

Asthana, Sanjay ^{[28
,29
,30
]}

Atwood, Craig S. ^{[28
,29
,30
]}

Baldwin, Clinton T. ^{[31
]}

Barber, Robert C. ^{[32
]}

Barnes, Lisa L. ^{[33
,34
,35
]}

Barral, Sandra ^{[36
,38
]}

Beach, Thomas G. ^{[39
]}

Becker, James T. ^{[40
,41
,42
]}

Beecham, Gary W. ^{[43
,44
]}

Beekly, Duane ^{[45
]}

Bennett, David A. ^{[33
,35
]}

Bigio, Eileen H. ^{[46
,47
]}

Bird, Thomas D. ^{[48
,49
]}

Blacker, Deborah ^{[50
,51
]}

Boeve, Bradley F. ^{[52
]}

Bowen, James D. ^{[53
]}

Boxer, Adam ^{[54
]}

Burke, James R. ^{[55
]}

Burns, Jeffrey M. ^{[56
]}

Buxbaum, Joseph D. ^{[57
,58
,59
,60
]}

Cairns, Nigel J. ^{[61
]}

Cantwell, Laura B. ^{[62
]}

Cao, Chuanhai ^{[63
]}

Carlson, Chris S. ^{[64
]}

机构：

[1] Yale Sch Publ Hlth, Dept Biostat, New Haven, CT 06510 USA

[2] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI USA

[3] Chinese Univ Hong Kong, Jockey Club Sch Publ Hlth & Primary Care, Div Biostat, Shatin, Hong Kong, Peoples R China

[4] Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA

[5] Yale Sch Med, New Haven, CT USA

[6] Massachusetts Gen Hosp, Cardiovasc Res Ctr, Boston, MA 02114 USA

[7] Brd Inst MIT & Harvard, Program Med & Populat Genet, Cambridge, MA USA

[8] Shanghai Jiao Tong Univ, SJTU Yale Joint Ctr Biostat, Sch Life Sci & Biotechnol, Dept Bioinformat & Biostat, Shanghai, Peoples R China

[9] Yale Sch Med, Dept Genet, New Haven, CT 06510 USA

[10] Univ Miami, Miller Sch Med, John P Hussman Inst Human Genom, Miami, FL 33136 USA

[11] Univ Washington, Dept Med, Seattle, WA 98195 USA

[12] Harvard Med Sch, Dept Med, Boston, MA 02115 USA

[13] Massachusetts Gen Hosp, Ctr Genom Med, Boston, MA 02114 USA

[14] Univ Penn, Ctr Clin Epidemiol & Biostatist, Perelman Sch Med, Philadelphia, PA 19104 USA

[15] Univ Penn, Perelman Sch Med, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA

[16] Univ Penn, Perelman Sch Med, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA

[17] Univ Kentucky, Dept Epidemiol, Sanders Brown Ctr Aging, Coll Publ Hlth, Lexington, KY USA

[18] Univ Texas Southwestern Med Ctr Dallas, Dept Psychiat, Dallas, TX 75390 USA

[19] Johns Hopkins Univ, Dept Neurol, Baltimore, MD 21218 USA

[20] Univ Michigan, Dept Neurol, Ann Arbor, MI 48109 USA

[21] VA Ann Arbor Healthcare Syst, Geriatr Res Educ & Clin Ctr, Ann Arbor, MI USA

[22] Michigan Alzheimer Dis Ctr, Ann Arbor, MI USA

[23] Indiana Univ, Dept Radiol, Indiana, PA USA

[24] Indiana Univ, Dept Med & Mol Genet, Indiana, PA USA

[25] Indiana Univ, Indian Alzheimers Dis Ctr, Indiana, PA USA

[26] Indiana Univ, Dept Neurol, Indiana, PA USA

[27] Univ Penn, Dept Psychiat, Perelman Sch Med, Philadelphia, PA 19104 USA

[28] Univ Wisconsin, Geriatr Res Educ & Clin Ctr, Madison, WI USA

[29] Univ Wisconsin, Dept Med, Madison, WI USA

[30] Wisconsin Alzheimers Dis Res Ctr, Madison, WI USA

[31] Boston Univ, Genet Program, Dept Med, Boston, MA 02215 USA

[32] Univ North Texas, Dept Pharmacol & Neurosci, Hlth Sci Ctr, Ft Worth, TX USA

[33] Rush Univ, Dept Neurol Sci, Med Ctr, Chicago, IL 60612 USA

[34] Rush Univ, Med Ctr, Dept Behav Sci, Chicago, IL 60612 USA

[35] Rush Univ, Rush Alzheimers Dis Ctr, Med Ctr, Chicago, IL 60612 USA

[36] Columbia Univ, Dept Neurol, Taub Inst Alzheimers Dis & Aging Brain, New York, NY USA

[37] Columbia Univ, Gertrude H Sergievsky Ctr, New York, NY USA

[38] Columbia Univ, Dept Neurol, New York, NY USA

[39] Banner Sun Hlth Res Inst, Civin Lab Neuropathol, Phoenix, AZ USA

[40] Univ Pittsburgh, Dept Psychiat, Sch Med, Pittsburgh, PA USA

[41] Univ Pittsburgh, Sch Med, Dept Neurol, Pittsburgh, PA 15261 USA

[42] Univ Pittsburgh, Sch Med, Dept Psychol, Pittsburgh, PA USA

[43] Univ Miami, John P Hussman Inst Human Genom, Miami, FL USA

[44] Univ Miami, Dept Human Genet, Dr John T Macdonald Fdn, Miami, FL USA

[45] Univ Washington, Natl Alzheimers Coordinating Ctr, Seattle, WA 98195 USA

[46] Northwestern Univ, Dept Pathol, Feinberg Sch Med, Chicago, IL 60611 USA

[47] Northwestern Univ, Cognit Neurol & Alzheimers Dis Ctr, Feinberg Sch Med, Chicago, IL 60611 USA

[48] Univ Washington, Dept Neurol, Seattle, WA 98195 USA

[49] VA Puget Sound Hlth Care Syst, GRECC, Seattle, WA USA

[50] Harvard Sch Publ Hlth, Dept Epidemiol, Boston, MA USA

来源：

NATURE GENETICS | 2019年 / 51卷 / 03期

基金：

国家重点研发计划; 英国惠康基金; 英国医学研究理事会;

关键词：

GENE-EXPRESSION; IDENTIFIES VARIANTS; INTEGRATIVE ANALYSIS; SUSCEPTIBILITY LOCI; COMMON VARIANTS; RISK PREDICTION; ALZHEIMERS; GWAS; METAANALYSIS; DISEASE;

D O I：

10.1038/s41588-019-0345-7

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

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.

引用

页码：568 / +

页数：12

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