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CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

文献类型：期刊论文

作者Yang, Yi1,2; Shi, Xingjie2,3; Jiao, Yuling4; Huang, Jian5; Chen, Min6; Zhou, Xiang7; Sun, Lei8; Lin, Xinyi2,9,10; Yang, Can11; Liu, Jin2
刊名BIOINFORMATICS
出版日期2020-04-01
卷号36期号:7页码:2009-2016
ISSN号1367-4803
DOI10.1093/bioinformatics/btz880
英文摘要Motivation: Although genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required. Results: In this study, we propose a novel probabilistic model, CoMM-S-2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S-2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S-2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S-2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.
资助项目Duke-NUS Medical School[R-913-200-098-263] ; Duke-NUS Medical School[R-913-200127-263] ; AcRF Tier 2 from the Ministry of Education, Singapore[MOE2016-T2-2029] ; AcRF Tier 2 from the Ministry of Education, Singapore[MOE2018-T2-1-046] ; AcRF Tier 2 from the Ministry of Education, Singapore[MOE2018-T2-2-006] ; National Science Foundation of China[61501389] ; Hong Kong Research Grant Council[16307818] ; Hong Kong Research Grant Council[12316116] ; Hong Kong Research Grant Council[12301417]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:000536489400004
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/51482]  
专题应用数学研究所
通讯作者Liu, Jin
作者单位1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
2.Duke NUS Med Sch, Ctr Quantitat Med, Program Hlth Serv & Syst Res, Singapore 169857, Singapore
3.Nanjing Univ Finance & Econ, Dept Stat, Nanjing 210046, Peoples R China
4.Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China
5.Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
7.Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA
8.Duke NUS Med Sch, Cardiovasc & Metab Disorders Program, Singapore 169857, Singapore
9.Singapore Clin Res Inst, Singapore 138669, Singapore
10.ASTAR, Singapore Inst Clin Sci, Singapore 117609, Singapore
推荐引用方式
GB/T 7714		 Yang, Yi,Shi, Xingjie,Jiao, Yuling,et al. CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies[J]. BIOINFORMATICS,2020,36(7):2009-2016.
APA Yang, Yi.,Shi, Xingjie.,Jiao, Yuling.,Huang, Jian.,Chen, Min.,...&Liu, Jin.(2020).CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies.BIOINFORMATICS,36(7),2009-2016.
MLA Yang, Yi,et al."CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies".BIOINFORMATICS 36.7(2020):2009-2016.

入库方式： OAI收割

来源：数学与系统科学研究院

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