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10.1038/s41525-022-00296-y http://scihub22266oqcxt.onion/10.1038/s41525-022-00296-y 35318325!8940898!35318325     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35318325&cmd=llinks): Failed to open stream: HTTP request failed! 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Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19 |pdf=|usr=}}{{35318325}} gab.com Text Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19 JO: NPJ Genom Med http://www.kidney.de/pget.php?&tw=1&pmid=35318325 #FOAvip 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. Twit Text FOAVip Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19 ????NPJ Genom Med http://www.kidney.de/pget.php?&tw=1&pmid=35318325 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35318325 #FOAvip English Wikipedia <ref>{{Cite pmid|35318325}}</ref> Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19 #MMPMID35318325 Zhou D; Gamazon ER NPJ Genom Med 2022[Mar]; 7 (1): 24 PMID35318325show ga 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. äIntegrative transcriptomic, evolutionary, and causal inference framewo(epmc).pdf DeepDyve Pubget Overpricing