Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19.

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics.

SUMMARY: Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities. AVAILABILITY AND IMPLEMENTATION: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

Modeling the role of negative cooperativity in metabolic regulation and homeostasis.

A significant proportion of enzymes display cooperativity in binding ligand molecules, and such effects have an important impact on metabolic regulation. This is easiest to understand in the case of positive cooperativity. Sharp responses to changes in metabolite concentrations can allow organisms to better respond to environmental changes and maintain metabolic homeostasis. However, despite the fact that negative cooperativity is almost as common as positive, it has been harder to imagine what advantages it provides. Here we use computational models to explore the utility of negative cooperativity in one particular context: that of an inhibitor binding to an enzyme. We identify several factors which may contribute, and show that acting together they can make negative cooperativity advantageous.