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Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19
- Source :
- npj Genomic Medicine, Vol 7, Iss 1, Pp 1-13 (2022)
- Publication Year :
- 2022
- Publisher :
- Nature Portfolio, 2022.
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 20567944
- Volume :
- 7
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- npj Genomic Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.43735fdd9211407b9f25bef4d922e752
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41525-022-00296-y