Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19.

Change log

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.

31 Biological Sciences, 3105 Genetics, Genetics, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, 2 Aetiology
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NPJ Genom Med
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Springer Science and Business Media LLC
U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01HG011138, R35HG010718, R56AG068026)
NIGMS NIH HHS (R01 GM140287)
NHGRI NIH HHS (R01 HG011138, R35 HG010718)
NIA NIH HHS (R56 AG068026)