Aug 2026· Royal Society Open Science· Vol 13· 0 citations· 44 references
Medicine
TL;DR
A biologically meaningful genetic overlap between obesity and COVID-19 severity is suggested, highlighting pleiotropy as a key feature in complex disease interactions and potential shared therapeutic targets.
Abstract
Abstract COVID-19 and obesity are complex conditions marked by immune and metabolic dysfunction, with the former still ranking among the leading causes of death from infectious diseases worldwide and the latter reaching pandemic proportions. Clinical evidence consistently shows that obesity increases the risk of severe COVID-19, yet the biological mechanisms underlying this association remain unclear. Given their physiological and clinical overlap, they may share genetic pathways. We investigated genetic variants jointly associated with body mass index (BMI) and COVID-19 using publicly available genome-wide data. A conjunctional false discovery rate (conjFDR) approach identified shared variants between BMI and three COVID-19 phenotypes: infection, hospitalization and very severe respiratory illness. Functional annotation and pathway enrichment analyses were performed to explore the biological context of these variants, followed by a phenome-wide association study (PheWAS) to characterize pleiotropy. Shared variants were enriched in immune, metabolic and hormonal signaling pathways, including metal ion transport and glycosylation. The overlap with BMI was strongest for hospitalized and severe cases, suggesting common mechanisms underlying disease progression rather than infection. These findings suggest a biologically meaningful genetic overlap between obesity and COVID-19 severity, highlighting pleiotropy as a key feature in complex disease interactions and potential shared therapeutic targets.
An approach comprising locus-specific stratification (LSS) and gene regulatory prioritisation score (GRPS), which uniquely considers multi-signals during fine-mapping and target gene identification, to address issues arising from multi-signals in complex diseases.
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