CoMM-S-2: a collaborative mixed model using summary statistics in transcriptome-wide association studies
Yang, Yi1,2; Shi, Xingjie2,3; Jiao, Yuling4; Huang, Jian5; Chen, Min6; Zhou, Xiang7; Sun, Lei8; Lin, Xinyi2,9,10; Yang, Can11; Liu, Jin2
Source PublicationBIOINFORMATICS
AbstractMotivation: 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.
Indexed BySCI
Funding ProjectDuke-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 Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000536489400004
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Document Type期刊论文
Corresponding AuthorLiu, Jin
Affiliation1.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
11.Hong Kong Univ Sci & Technol, Dept Math, Hong Kong 999077, Peoples R China
Recommended Citation
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.
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