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Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors 1 , but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals 2 and about 1,000 individuals 3-6 , respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1, neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1-an enzyme that adds sialic acid to the cell surface, which is used for viral entry-reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2, the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7 ). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza., (© 2024. The Author(s).)

Objectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data., Design: A cross-sectional study., Setting: AncestryDNA customers in the USA who consented to research., Participants: 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 USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test., Results: We 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 men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation 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., Conclusions: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic., Competing Interests: Competing interests: Authors affiliated with AncestryDNA may have equity in Ancestry., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2-2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10 - 8 ) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10 - 13 ), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone., (© 2022. The Author(s).)