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dc.contributor.authorHe, D
dc.contributor.authorFan, C
dc.contributor.authorQi, M
dc.contributor.authorYang, Y
dc.contributor.authorCooper, DN
dc.contributor.authorZhao, H
dc.date.accessioned2021-07-13T04:59:46Z
dc.date.available2021-07-13T04:59:46Z
dc.date.issued2021
dc.identifier.issn2158-3188
dc.identifier.doi10.1038/s41398-021-01294-x
dc.identifier.urihttp://hdl.handle.net/10072/405902
dc.description.abstractSchizophrenia (SCZ) is a polygenic disease with a heritability approaching 80%. Over 100 SCZ-related loci have so far been identified by genome-wide association studies (GWAS). However, the risk genes associated with these loci often remain unknown. We present a new risk gene predictor, rGAT-omics, that integrates multi-omics data under a Bayesian framework by combining the Hotelling and Box–Cox transformations. The Bayesian framework was constructed using gene ontology, tissue-specific protein–protein networks, and multi-omics data including differentially expressed genes in SCZ and controls, distance from genes to the index single-nucleotide polymorphisms (SNPs), and de novo mutations. The application of rGAT-omics to the 108 loci identified by a recent GWAS study of SCZ predicted 103 high-risk genes (HRGs) that explain a high proportion of SCZ heritability (Enrichment = 43.44 and p= 9.30 × 1 0 − 9). HRGs were shown to be significantly (padj= 5.35 × 1 0 − 7) enriched in genes associated with neurological activities, and more likely to be expressed in brain tissues and SCZ-associated cell types than background genes. The predicted HRGs included 16 novel genes not present in any existing databases of SCZ-associated genes or previously predicted to be SCZ risk genes by any other method. More importantly, 13 of these 16 genes were not the nearest to the index SNP markers, and them would have been difficult to identify as risk genes by conventional approaches while ten out of the 16 genes are associated with neurological functions that make them prime candidates for pathological involvement in SCZ. Therefore, rGAT-omics has revealed novel insights into the molecular mechanisms underlying SCZ and could provide potential clues to future therapies.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofpagefrom175
dc.relation.ispartofissue1
dc.relation.ispartofjournalTranslational Psychiatry
dc.relation.ispartofvolume11
dc.subject.fieldofresearchPattern recognition
dc.subject.fieldofresearchData mining and knowledge discovery
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchHealth services and systems
dc.subject.fieldofresearchPublic health
dc.subject.fieldofresearchPsychology
dc.subject.fieldofresearchcode460308
dc.subject.fieldofresearchcode460502
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode4203
dc.subject.fieldofresearchcode4206
dc.subject.fieldofresearchcode52
dc.titlePrioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationHe, D; Fan, C; Qi, M; Yang, Y; Cooper, DN; Zhao, H, Prioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data, Translational Psychiatry, 2021, 11 (1), pp. 175
dcterms.dateAccepted2021-02-03
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-07-12T00:55:21Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorYang, Yuedong


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