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dc.contributor.authorJin, Xiuyuan
dc.contributor.authorZhang, Liye
dc.contributor.authorJi, Jiadong
dc.contributor.authorJu, Tao
dc.contributor.authorZhao, Jinghua
dc.contributor.authorYuan, Zhongshang
dc.date.accessioned2022-08-06T15:01:11Z
dc.date.available2022-08-06T15:01:11Z
dc.date.issued2022-08-06
dc.date.submitted2022-01-12
dc.identifier.citationBMC Genomics, volume 23, issue 1, article-number 562
dc.identifier.issn1471-2164
dc.identifier.others12864-022-08809-w
dc.identifier.other8809
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/339909
dc.description.abstractBACKGROUND: Transcriptome-wide association studies (TWASs) have shown great promise in interpreting the findings from genome-wide association studies (GWASs) and exploring the disease mechanisms, by integrating GWAS and eQTL mapping studies. Almost all TWAS methods only focus on one gene at a time, with exception of only two published multiple-gene methods nevertheless failing to account for the inter-dependence as well as the network structure among multiple genes, which may lead to power loss in TWAS analysis as complex disease often owe to multiple genes that interact with each other as a biological network. We therefore developed a Network Regression method in a two-stage TWAS framework (NeRiT) to detect whether a given network is associated with the traits of interest. NeRiT adopts the flexible Bayesian Dirichlet process regression to obtain the gene expression prediction weights in the first stage, uses pointwise mutual information to represent the general between-node correlation in the second stage and can effectively take the network structure among different gene nodes into account. RESULTS: Comprehensive and realistic simulations indicated NeRiT had calibrated type I error control for testing both the node effect and edge effect, and yields higher power than the existed methods, especially in testing the edge effect. The results were consistent regardless of the GWAS sample size, the gene expression prediction model in the first step of TWAS, the network structure as well as the correlation pattern among different gene nodes. Real data applications through analyzing systolic blood pressure and diastolic blood pressure from UK Biobank showed that NeRiT can simultaneously identify the trait-related nodes as well as the trait-related edges. CONCLUSIONS: NeRiT is a powerful and efficient network regression method in TWAS.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectResearch
dc.subjectTWAS
dc.subjectBiological networks
dc.subjectDirichlet process regression
dc.subjectPointwise mutual information
dc.subjectBlood pressure
dc.titleNetwork regression analysis in transcriptome-wide association studies.
dc.typeArticle
dc.date.updated2022-08-06T15:01:11Z
prism.publicationNameBMC Genomics
dc.identifier.doi10.17863/CAM.87331
dcterms.dateAccepted2022-08-02
rioxxterms.versionofrecord10.1186/s12864-022-08809-w
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1471-2164
pubs.funder-project-idNational Natural Science Foundation of China (81872712)
pubs.funder-project-idNatural Science Foundation of Shandong Province (ZR2019ZD02)
cam.issuedOnline2022-08-06


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