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However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods\/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits\/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.<\/jats:p>","DOI":"10.1093\/bib\/bbac038","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T12:08:29Z","timestamp":1644235709000},"source":"Crossref","is-referenced-by-count":7,"title":["Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models"],"prefix":"10.1093","volume":"23","author":[{"given":"Ting","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics at Xuzhou Medical University, China"}]},{"given":"Jiahao","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics at Xuzhou Medical University, China"}]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics at Xuzhou Medical University, China"}]},{"given":"Yongyue","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biostatistics at Nanjing Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2710-3440","authenticated-orcid":false,"given":"Ping","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Center for Medical Statistics and Data Analysis and Key Laboratory of Human Genetics and Environmental Medicine at Xuzhou Medical University, China"}]}],"member":"286","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"2022031506395161700_ref1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002102","article-title":"Leveraging genetics to advance type 2 diabetes prevention","volume":"13","author":"Florez","year":"2016","journal-title":"PLoS Med"},{"key":"2022031506395161700_ref2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1038\/nrg.2016.27","article-title":"Developing and evaluating polygenic risk 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