New paper: Identifying direct risk factors for COVID-19 hospitalization in UK Biobank using Doublethink

This week sees publication of our new paper Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink in Proceedings of the National Academy of Sciences (PNAS). This work was joint with Nicolas Arning and Helen Fryer.

In this study we applied a novel approach called Doublehthink to implement an exposome-wide association study (ExWAS) of non-genetic risk factors that influence risk of COVID-19 hospitalization in UK Biobank (UKB). Inspired by Bayesian model averaging, our approach enhances power by testing both individual variables and arbitrary groups of variables.

We employed Doublethink to reveal exposome-wide significant signals across nine individual variables and seven groups of variables, notably factors like aging, dementia and prior infection overlooked by 85% of previous studies of the UK Biobank. We found significant direct effects among some commonly reported risk factors like age, sex, and obesity, but not others like cardiovascular disease. The effects of hypertension, depression, and diabetes appeared to be mediated via general comorbidity. 

Biobank-scale epidemiology has transformed the study of common diseases, particularly through the discovery of genetic risk factors using genome-wide association studies (GWAS) methods. ExWAS applies similiar logic to pursue agnostic, data-driven discovery of non-genetic risk factors.

Doublethink offers an ExWAS method that can test thousands of variables in hundreds of thousands of UK Biobank participants. It controls both Bayesian (false discovery rate; FDR) and classical (familywise error rate; FWER) measures of false positives. Its Bayesian model-averaging approach enables an agnostic approach to variable selection, but it also addresses drawbacks of Bayesian methods like computational burden and reliance on prior assumptions for its false positive control.

Through its capacity to interpret biobank-scale data, our new Doublethink-based ExWAS approach paves the way for future systematic analyses of risk factors for infectious and chronic diseases in UK Biobank and beyond.

Introducing Doublethink: joint Bayesian-frequentist model-averaged hypothesis testing

This week Nick Arning, Helen Fryer and I released two related preprints describing a new method called Doublethink, and its application to identifying risk factors for COVID-19 hospitalization in UK Biobank:

• Doublethink: simultaneous Bayesian-frequentist model-averaged hypothesis testing. Fryer, Arning, Wilson (2023) arXiv doi: 10.48550/arXiv.2312.17566

• Identifying direct risk factors in UK Biobank with simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink. Arning, Fryer, Wilson (2024) medRxiv doi: 10.1101/2024.01.01.24300687

Doublethink: Bayesian-frequentist model-averaged hypothesis testing

Doublethink enables joint Bayesian and frequentist hypothesis testing when there is model uncertainty by interconverting Bayesian posterior odds and classical (frequentist) p-values. It has broad implications because (i) it reveals connections between the Bayesian approach to model averaging and the classical approach to multiple testing, and (ii) it brings the benefits of Bayesian model averaging to classical statistics.

Doublethink addresses two fundamental problems in hypothesis testing:

1. In classical tests, the statistical evidence that one variable directly affects an outcome generally depends on which other variables are assumed to directly affect it.
2. In Bayesian tests, the statistical evidence that one variable directly affects an outcome depends on the prior assumptions.

These issues are addressed by computing p-values from Bayesian model-averaged posterior odds, which (1) account for model uncertainty and (2) are theoretically invariant to prior assumptions, assuming large sample sizes.

Doublethink simultaneously controls the frequentist family-wise error rate (FWER) and the Bayesian false discovery rate (FDR). It builds on Johnson's Bayesian tests based on likelihood ratio statistics, and Karamata's theory of regular variation.

Identifying direct risk factors in UK Biobank with Doublethink

We applied Doublethink to identify direct risk factors for COVID-19 hospitalization in UK Biobank. This is a well-studied problem but we took an 'exposome-wide' approach in which we evaluated whether 1,900 variables measured in the UK Biobank each affected the outcome. This is still an under-utilized approach in epidemiology, which usually focuses on candidate risk factors.

Exposome-wide approaches have potential benefits over candidate risk factor approaches, including:

• The ability to discover unexpected results.
• Stringent control for multiple testing.
• Avoidance of bias in choosing candidate risk factors or deciding to publish.

However, we only studied the direct effects of variables on the outcome. This means we cannot make statements about the total (direct and indirect) effects of a variable, e.g. smoking, on the outcome, which are needed in applications like assessing potential interventions.

We identified individual variables and groups of variables that were 'exposome-wide significant' at 9% FDR and 0.05% FWER, after accounting for the direct effects of all other variables.

Comparing our results to over 100 published studies of COVID-19 in UK Biobank, we

• Recapitulated several commonly reported direct risk factors, e.g. age, sex, and obesity.
• Excluded others, e.g. diabetes, cardiovascular disease, and hypertension, which might be mediated through other variables that measure general comorbidity.
• Identified some infrequently reported direct risk factors, both individually, e.g. lung infection, and as groups, e.g. constipation/urinary tract infection, which might reflect underlying kidney disease.

The ability to test groups of variables, which increases sensitivity, was one of the benefits of Doublethink's model-averaging approach. It is particularly helpful in large biobanks that measure thousands of variables, because correlation between variables is pervasive, and can dilute the significance of individual variables that measure similar phenomena, like the numerous types of deprivation index. It serves as a flexible alternative to pre-analysis variable filtering algorithms, while controlling the risk of false positives by pre-defining significance thresholds for all possible tests.

To read more, please check out the preprints here and here.

Rewley House Lecture: Role of data science in the pandemic

This year I was invited to give the Rewley House Lecture, a multidisciplinary research talk open to all, at the Department for Continuing Education, where I am Director of Studies in Data Science.

I talked about how data science has been used during the COVID-19 pandemic, spanning vaccine design, clinical trials, surveillance and policy advice, and highlighting the identification of risk factors for disease.

If you like this talk, you might be interested in the following courses available this academic year:

• Infectious Disease Modelling: Mathematical Techniques (September 2022)
• Infectious Disease Modelling: Applied Methods in R (January 2023)
• Pandemic Data Science (April 2023)

Presentation: Genome-wide association studies of COVID-19

An updated version of this talk given at the Nuffield Department for Population Health's annual symposium 2021:

Presentation: Genome-wide Association Studies of COVID-19

An online recording of the talk about Genome-wide Association Studies of COVID-19 at the UK Biobank 2020 meeting on 23 June 2020. The full conference is also online.

The group's research response to COVID-19

This is an update on the group's research response to the COVID-19 pandemic. As an infectious disease group we have been keen to contribute to the international research effort where we could be useful, while recognising the need to continue our research on other important infections where possible.

• Bugbank. Thanks to a pre-existing collaboration between our group, Public Health England and UK Biobank, we were in a position to help rapidly facilitate COVID-19 research via SARS-CoV-2 PCR-based swab test results. Beginning mid-March, we worked to provide regular (usually weekly) updates of tests results, which were made available to all UK Biobank researchers beginning April 17th. This is one of several resources on COVID-19 linked to UK Biobank. Beginning in May we provided feeds to other cohorts: INTERVAL, COMPARE, Genes & Health and the NIHR BioResource. We provide updates on this work through the project website www.bugbank.uk. We have published a paper describing the dynamic data linkage in Microbial Genomics (press release). Key collaborators in this project are Jacob Armstrong (Big Data Institute) Naomi Allen (UK Biobank) and David Wyllie and Anne Marie O'Connell (Public Health England).

• COVID-19 Host Genetics Initiative. Along with groups at McGill and the Broad Institute, I have contributed analyses of UK Biobank to investigate genetic risk factors for COVID-19. The Host Genetics Initiative is drawing on more than 200 cohorts from around the world to conduct meta-analysis to tackle questions such as why some people suffer severe COVID-19 while others get mild or asymptomatic infection (media coverage). The meta-analysis results are publicly released periodically. This led to the independent replication of a genetic risk factor on chromosome 3 discovered by a Spanish-Italian cohort and published in the New England Journal of Medicine.

• Epidemiological risk factors for COVID-19. Graduate student Nicolas Arning and I are developing an approach to quantify the effects of lifestyle and medical risk factors for COVID-19 in the UK Biobank that accounts for inherent uncertainty in which risk factors to consider. The new method employs the harmonic mean p-value, a model-averaging approach for big data that we published previously. We are in the process of evaluating the performance of the approach, comparing it to machine learning, and interpreting the results.

• Antibody testing for the UK Government. Postdoc Justine Rudkin has been working in the lab with Derrick Crook, Sir John Bell and others to measure the efficacy of antibody tests for the UK Government. They have tested many hundreds of kits to establish the sensitivity and specificity of the tests to help evaluate the utility of a national testing programme. This work was crucial in demonstrating the limitations of early blood-spot based tests, and the credibility of subsequent generations of antibody tests. The work has been published in Wellcome Open Research.

• Office of National Statistics Antibody Survey. Justine has also been working in the lab with Sarah Walker to set up robots for her large antibody testing cohort study. Sarah is leading an infection survey with the Office of National Statistics to investigate exposure to SARS-CoV-2 in England and Wales.

Work on other infections that has continued during the lockdown. Postdoc Sarah Earle continues research into pathogen genetic risk factors for diseases including tuberculosis and meningococcal meningitis, while Steven Lin has continued to pursue work on hepatitis C virus genetics and epidemiology. Many of our close collaborators are infection doctors and they have of course been recalled to clinical duties. Laboratory work in the group has been severely disrupted, particularly several of Justine's Staphylococcus aureus projects. We are keen to pick up on those projects where we left off when the chance arrives.

Teaching: Online lectures and practical on Phylogenetics in Practice

On March 16th, we were in the interesting position of running an infectious disease course at the Big Data Institute on the day the national lockdown was announced in response to the COVID-19 pandemic. As a result, we were among the first in the university to do remote teaching, something Katrina Lythgoe and the rest of us had prepared for in anticipation of the lockdown a week earlier that never happened.

These are the two online lectures in the Health Data Sciences CDT that I gave called Phylogenetics in Practice.

The online practical, which applies phylogenetics approaches to understand the Zika virus epidemic, is implemented as a Docker container, and available here.

Presentation on identifying COVID-19 inpatients from Public Health England data

This is a presentation I gave at the COVID-19 Host Genetics Initiative meeting on 2nd July 2020 about using Public Health England's Second Generation Surveillance System to identify COVID-19 inpatients among SARS-CoV-2 positive individuals in England.

For further information, please see this bugbank blog post comparing inpatients identified using SGSS and Hospital Episode Statistics.

Royal Society Summer Science Exhibition 2020

This year the Royal Society's Summer Science Exhibition was online, and included highlights and updates from previous exhibitors, among them ours from 2018. This video was posted on Tuesday's session: