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dc.contributor.authorZhou, Dan
dc.contributor.authorGamazon, Eric R
dc.date.accessioned2022-04-24T01:02:59Z
dc.date.available2022-04-24T01:02:59Z
dc.date.issued2022-03-22
dc.identifier.issn2056-7944
dc.identifier.other35318325
dc.identifier.otherPMC8940898
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336397
dc.description.abstractWe developed an integrative transcriptomic, evolutionary, and causal inference framework for a deep region-level analysis, which integrates several published approaches and a new summary-statistics-based methodology. To illustrate the framework, we applied it to understanding the host genetics of COVID-19 severity. We identified putative causal genes, including SLC6A20, CXCR6, CCR9, and CCR5 in the locus on 3p21.31, quantifying their effect on mediating expression and on severe COVID-19. We confirmed that individuals who carry the introgressed archaic segment in the locus have a substantially higher risk of developing the severe disease phenotype, estimating its contribution to expression-mediated heritability using a new summary-statistics-based approach we developed here. Through a large-scale phenome-wide scan for the genes in the locus, several potential complications, including inflammatory, immunity, olfactory, and gustatory traits, were identified. Notably, the introgressed segment showed a much higher concentration of expression-mediated causal effect on severity (0.9-11.5 times) than the entire locus, explaining, on average, 15.7% of the causal effect. The region-level framework (implemented in publicly available software, SEGMENT-SCAN) has important implications for the elucidation of molecular mechanisms of disease and the rational design of potentially novel therapeutics.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101685193
dc.sourceessn: 2056-7944
dc.titleIntegrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19.
dc.typeArticle
dc.date.updated2022-04-24T01:02:58Z
prism.issueIdentifier1
prism.publicationNameNPJ Genom Med
prism.volume7
dc.identifier.doi10.17863/CAM.83814
dcterms.dateAccepted2022-02-15
rioxxterms.versionofrecord10.1038/s41525-022-00296-y
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidZhou, Dan [0000-0002-5313-8164]
dc.contributor.orcidGamazon, Eric R [0000-0003-4204-8734]
dc.identifier.eissn2056-7944
pubs.funder-project-idU.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01HG011138, R35HG010718, R56AG068026)
pubs.funder-project-idNIGMS NIH HHS (R01 GM140287)
pubs.funder-project-idNHGRI NIH HHS (R01 HG011138, R35 HG010718)
pubs.funder-project-idNIA NIH HHS (R56 AG068026)
cam.issuedOnline2022-03-22


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International