Scientists from AncestryDNA found three newly-defined phenotypes (observable characteristics or traits for an individual) that capture genetic associations which may provide protection against COVID-19.
The findings are revealed through the analysis of self-reported outcomes of COVID-19 from more than 700,000 customers in the United States.
They suggest that this method of data collection can complement clinical studies of COVID-19, notably for milder cases of the disease.
The research is published in Nature Genetics and was conducted by Kristin Rand et al.
Genetic association studies can help to identify the genes and biological pathways underlying a given physical outcome or trait.
Large-scale genetic studies of susceptibility to COVID-19 have focused on severe disease that is associated with hospitalization.
However, most infections with SARS-CoV-2 do not lead to severe disease, and although many clinical risk factors are known, these do not fully explain the variation in outcomes.
In the study, the team analyzed self-reported outcomes of COVID-19 in questionnaires collected from 736,723 customers in the United States.
Using these genetic and survey data, the authors analyzed four previously studied phenotypes related to the risk of developing severe disease.
The team also defined three new, protective phenotypes related to the risk of infection following household exposure to SARS-CoV-2 and to symptom severity.
These newly-identified phenotypes are linked to regions of the genetic code that reduce the risk of COVID-19 and therefore may be useful targets for therapy.
This research demonstrates that large-scale, survey-based analysis of direct-to-consumer testing can complement traditional studies in hospitalized populations—an approach that the authors suggest could be applied to other diseases going forward.
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If you care about COVID, please read studies that brain shortcuts may explain why many people are against COVID vaccine and masks, and almost 1 in 3 older people develop new health problems after COVID-19 infection.
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