Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19.
dc.contributor.author | Zhou, Dan | |
dc.contributor.author | Gamazon, Eric R | |
dc.date.accessioned | 2022-04-24T01:02:59Z | |
dc.date.available | 2022-04-24T01:02:59Z | |
dc.date.issued | 2022-03-22 | |
dc.identifier.issn | 2056-7944 | |
dc.identifier.other | 35318325 | |
dc.identifier.other | PMC8940898 | |
dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/336397 | |
dc.description.abstract | We 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.language | eng | |
dc.publisher | Springer Science and Business Media LLC | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | nlmid: 101685193 | |
dc.source | essn: 2056-7944 | |
dc.title | Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19. | |
dc.type | Article | |
dc.date.updated | 2022-04-24T01:02:58Z | |
prism.issueIdentifier | 1 | |
prism.publicationName | NPJ Genom Med | |
prism.volume | 7 | |
dc.identifier.doi | 10.17863/CAM.83814 | |
dcterms.dateAccepted | 2022-02-15 | |
rioxxterms.versionofrecord | 10.1038/s41525-022-00296-y | |
rioxxterms.version | VoR | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.contributor.orcid | Zhou, Dan [0000-0002-5313-8164] | |
dc.contributor.orcid | Gamazon, Eric R [0000-0003-4204-8734] | |
dc.identifier.eissn | 2056-7944 | |
pubs.funder-project-id | U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01HG011138, R35HG010718, R56AG068026) | |
pubs.funder-project-id | NIGMS NIH HHS (R01 GM140287) | |
pubs.funder-project-id | NHGRI NIH HHS (R01 HG011138, R35 HG010718) | |
pubs.funder-project-id | NIA NIH HHS (R56 AG068026) | |
cam.issuedOnline | 2022-03-22 |
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