Journal article
CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies
Bioinformatics, Vol.36(7), pp.2009-2016
04/01/2020
DOI: 10.1093/bioinformatics/btz880
PMID: 31755899
Abstract
AbstractMotivationAlthough 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.ResultsIn this study, we propose a novel probabilistic model, CoMM-S2, 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-S2 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-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.Availability and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.Supplementary informationSupplementary data are available at Bioinformatics online.
Details
- Title: Subtitle
- CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies
- Creators
- Yi Yang - National University of SingaporeXingjie Shi - Nanjing University of Finance and EconomicsYuling Jiao - Zhongnan University of Economics and LawJian Huang - University of IowaMin Chen - Chinese Academy of SciencesXiang Zhou - University of Michigan–Ann ArborLei Sun - National University of SingaporeXinyi Lin - National University of SingaporeCan Yang - Hong Kong University of Science and TechnologyJin Liu - National University of Singapore
- Contributors
- Alfonso Valencia (Editor)
- Resource Type
- Journal article
- Publication Details
- Bioinformatics, Vol.36(7), pp.2009-2016
- DOI
- 10.1093/bioinformatics/btz880
- PMID
- 31755899
- ISSN
- 1367-4803
- eISSN
- 1460-2059
- Grant note
- DOI: 10.13039/100016017, name: Duke-NUS Medical School, award: MOE2016-T2-2-029, MOE2018-T2-1-046, MOE2018-T2-2-006, R-913-200-098-263, R-913-200-127-263; DOI: 10.13039/501100001459, name: Ministry of Education, Singapore, award: 61501389; DOI: 10.13039/501100001809, name: National Science Foundation of China, award: 16307818, 12316116, 12301417; name: Hong Kong Research Grant Council
- Language
- English
- Date published
- 04/01/2020
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257630302771
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