ABSTRACT
Multiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable. Most of these studies ascertained COVID-19 cases in medical clinics and hospitals, which can lead to an overrepresentation of cases with severe outcomes, such as hospitalization, intensive care unit admission, or ventilation. Here, we demonstrate the utility and validity of deep phenotyping with self-reported outcomes in a population with a large proportion of mild and subclinical cases. Using these data, we defined eight different phenotypes related to COVID-19 outcomes: four that align with previously studied COVID-19 definitions and four novel definitions that focus on susceptibility given exposure, mild clinical manifestations, and an aggregate score of symptom severity. We assessed replication of 13 previously identified COVID-19 genetic associations with all eight phenotypes and found distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. We then performed a discovery GWAS, which suggested some novel phenotypes may better capture protective associations and also identified a novel association in chr11/GALNT18 that reproduced in two fully independent populations.
Subject(s)
Genomic Instability , COVID-19ABSTRACT
The growing toll of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection and severe outcomes, underscoring the need to assess susceptibility and severity patterns in large datasets. The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors, and exposures for over 563,000 adult individuals in the U.S., including over 4,700 COVID-19 cases as measured by a self-reported positive nasal swab test. We observed significant associations between several risk factors and COVID-19 susceptibility and severity outcomes. Many of the susceptibility associations were accounted for by differences in known exposures; a notable exception was elevated susceptibility odds for males after adjusting for known exposures and age. We also leveraged the dataset to build risk models to robustly predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and life-threatening critical illness amongst COVID-19 cases (AUC=0.87 and 0.90, respectively). The results highlight the value of self-reported epidemiological data at scale to provide public health insights into the evolving COVID-19 pandemic.