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In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington\u2019s disease (P=5.58 \u00d7 10\u22125), Alzheimer\u2019s disease (P=5.58 \u00d7 10\u22125), Parkinson\u2019s disease (P=6.34 \u00d7 10\u22125), spliceosome (P=1.17 \u00d7 10\u22124), oxidative phosphorylation (P=1.09 \u00d7 10\u22124), and wnt signaling pathways (P=2.07 \u00d7 10\u22124). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 \u00d7 10\u22125), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.<\/jats:p>","DOI":"10.1042\/bsr20201084","type":"journal-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T10:54:56Z","timestamp":1598266496000},"update-policy":"https:\/\/doi.org\/10.1042\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets"],"prefix":"10.1042","volume":"40","author":[{"given":"Haozhen","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou 310003, P. R. 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China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1299-4802","authenticated-orcid":false,"given":"Yunlong","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou 325027, Zhejiang, China"},{"name":"School of Biomedical Engineering, School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Nursing, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou 310003, P. R. 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