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Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing

Nicolas Arning1, Helen R Fryer1, Daniel J Wilson1,2

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|January 2, 2026
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Summary

This study used a novel Doublethink method to identify nongenetic risk factors for COVID-19 hospitalization from UK Biobank data. It found aging, dementia, and prior infection were significant direct risk factors, highlighting an agnostic exposome-wide approach.

Keywords:
COVID-19 hospitalizationFDRFWERUK Biobankexposome-wide association studies

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Computational Biology

Background:

  • Traditional epidemiological studies often focus on candidate risk factors, potentially introducing bias and overlooking novel associations.
  • Genome-wide association studies (GWAS) excel at identifying genetic risk factors, but nongenetic risk factor discovery using big data lags.
  • Modern biobanks offer vast potential risk factor data, necessitating advanced analytical methods to avoid bias and control for multiple testing.

Purpose of the Study:

  • To implement a Doublethink-based exposome-wide association study (EWAS) to identify direct nongenetic risk factors for COVID-19 hospitalization.
  • To leverage the UK Biobank dataset comprising 201,917 participants and 1,912 potential risk factors.
  • To simultaneously control Bayesian False Discovery Rate (FDR) and frequentist Familywise Error Rate (FWER) using a novel hypothesis testing approach.

Main Methods:

  • Utilized a Doublethink model-averaged hypothesis testing approach, incorporating Markov Chain Monte Carlo (MCMC) for analysis.
  • Conducted an exposome-wide association study on 1,912 variables within the UK Biobank cohort.
  • Focused on COVID-19 hospitalization data from the 2020 outbreak.

Main Results:

  • Identified nine individual and seven groups of variables as exposome-wide significant for COVID-19 hospitalization.
  • Found significant direct effects for factors including aging, dementia, and prior infection, alongside common factors like age, sex, and obesity.
  • Observed that effects of hypertension, depression, and diabetes appeared mediated through general comorbidity, while cardiovascular disease did not show significant direct effects.

Conclusions:

  • The Doublethink approach provides a powerful, agnostic method for identifying direct risk factors in large biobanks, controlling both Bayesian FDR and frequentist FWER.
  • Overlooked factors like aging and dementia emerged as significant direct risk factors for COVID-19 hospitalization.
  • This joint Bayesian-frequentist hypothesis testing framework offers flexible post hoc analysis and highlights the benefits of an unbiased, exposome-wide discovery strategy.