| Title: | Gene-Environment Interaction Analysis Incorporating Sex, Cardiometabolic Diseases, and Multiple Deprivation Index Reveals Novel Genetic Associations With COVID-19 Severity |
| Journal: | Frontiers in Genetics |
| Published: | 12 Jan 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35096005/ |
| DOI: | https://doi.org/10.3389/fgene.2021.782172 |
| URL: | https://www.frontiersin.org/articles/10.3389/fgene.2021.782172/pdf |
| Citations: | 1 as of 8 Aug 2024 |
Publication 12608
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Abstract
Increasing evidence indicates that specific genetic variants influence the severity of outcomes after infection with COVID-19. However, it is not clear whether the effect of these genetic factors is independent of the risk due to more established non-genetic demographic and metabolic risk factors such as male sex, poor cardiometabolic health, and low socioeconomic status. We sought to identify interactions between genetic variants and non-genetic risk factors influencing COVID-19 severity via a genome-wide interaction study in the UK Biobank. Of 378,051 unrelated individuals of European ancestry, 2,402 were classified as having experienced severe COVID-19, defined as hospitalization or death due to COVID-19. Exposures included sex, cardiometabolic risk factors [obesity and type 2 diabetes (T2D), tested jointly], and multiple deprivation index. Multiplicative interaction was tested using a logistic regression model, conducting both an interaction test and a joint test of genetic main and interaction effects. Five independent variants reached genome-wide significance in the joint test, one of which also reached significance in the interaction test. One of these, rs2268616 in the placental growth factor (PGF) gene, showed stronger effects in males and in individuals with T2D. None of the five variants showed effects on a similarly-defined phenotype in a lookup in the COVID-19 Host Genetics Initiative. These results reveal potential additional genetic loci contributing to COVID-19 severity and demonstrate the value of including non-genetic risk factors in an interaction testing approach for genetic discovery.</p>
6 Authors
- Kenneth E. Westerman
- Joanna Lin
- Magdalena del Rocio Sevilla-Gonzalez
- Beza Tadess
- Casey Marchek
- Alisa K. Manning
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