Novel COVID-19 phenotype definitions reveal phenotypically distinct patterns of genetic association and protective effects

INTRODUCTION PARAGRAPHMultiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable.1-5 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.

Common genetic variants identify therapeutic targets for COVID-19 and individuals at high risk of severe disease

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5x10-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

COVID-19 Susceptibility and Severity Risks in a Survey of Over 500,000 People

BackgroundThe enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analyzing population-scale datasets in real time to monitor and better understand the evolving pandemic. MethodsThe 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. in just under four months, including over 4,700 COVID-19 cases as measured by a self-reported positive test. ResultsWe replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for males even after adjusting for known exposures and age (adjusted odds ratio [aOR]=1.36, 95% confidence interval [CI] = (1.19, 1.55)). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex, and genetic ancestry groups within the study. ConclusionThe results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic. THUMBNAILO_ST_ABSWhat is already known on this subjectC_ST_ABSO_LIThe COVID-19 pandemic has exacted a historic toll on human lives, healthcare systems and global economies, with over 83 million cases and over 1.8 million deaths worldwide as of January 2021. C_LIO_LICOVID-19 risk factors for susceptibility and severity have been extensively investigated by clinical and public health researchers. C_LIO_LISeveral groups have developed risk models to predict COVID-19 illness outcomes based on known risk factors. C_LI What this study addsO_LIWe performed association analyses for COVID-19 susceptibility and severity in a large, at-home survey and replicated much of the previous clinical literature. C_LIO_LIAssociations were further adjusted for known COVID-19 exposures, and we observed elevated positive test odds for males even after adjustment for these known exposures. C_LIO_LIWe developed risk models and evaluated them across different age, sex, and genetic ancestry cohorts, and showed robust performance across all cohorts in a holdout dataset. C_LIO_LIOur results establish large-scale, self-reported surveys as a potential framework for investigating and monitoring rapidly evolving pandemics. C_LI

AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci

Human infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test, ncases=2,407) and severity (hospitalization, ncases=250). The severity GWAS replicated associations with severe COVID-19 near ABO and SLC6A20 (P<0.05). Furthermore, we identified three novel loci with P<5x10-8. The strongest association was near IVNS1ABP, a gene involved in influenza virus replication1, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity: SRRM1 and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.