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

doi:10.1093/bioinformatics/btz880

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	CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies
	Yang, Yi 2,3; Shi, Xingjie 3,4; Jiao, Yuling 5; Huang, Jian 6; Chen, Min 7; Zhou, Xiang 8; Sun, Lei 1; Lin, Xinyi 3,9,10; Yang, Can 11; Liu, Jin 3
刊名	BIOINFORMATICS
	2020-04-01
卷号	36 期号:7 页码:2009-2016
ISSN号	1367-4803
DOI	10.1093/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
内容类型	期刊论文
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.Duke NUS Med Sch, Cardiovasc & Metab Disorders Program, Singapore 169857, Singapore 2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China 3.Duke NUS Med Sch, Ctr Quantitat Med, Program Hlth Serv & Syst Res, Singapore 169857, Singapore 4.Nanjing Univ Finance & Econ, Dept Stat, Nanjing 210046, Peoples R China 5.Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China 6.Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA 7.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 8.Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA 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.