Article ; Online: Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects.
2022 Volume 54, Issue 4, Page(s) 374–381
Abstract: Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus ... ...
| Abstract | Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four 'expanded' phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four 'expanded' COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification. |
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| MeSH term(s) | COVID-19/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Phenotype ; Polymorphism, Single Nucleotide/genetics |
| Language | English |
| Publishing date | 2022-04-11 |
| Publishing country | United States |
| Document type | Journal Article ; Meta-Analysis |
| ZDB-ID | 1108734-1 |
| ISSN | 1546-1718 ; 1061-4036 |
| ISSN (online) | 1546-1718 |
| ISSN | 1061-4036 |
| DOI | 10.1038/s41588-022-01042-x |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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