The Burgess Research Group 

Stephen Burgess, Group leader: Stephen completed his BA and MMath (Part III) in Mathematics at the University of Cambridge. He studied for a PhD in the MRC Biostatistics Unit, Cambridge, from 2008-11 working on methods for Mendelian randomization analysis under the supervision of Simon Thompson. He joined the Cardiovascular Epidemiology Unit in the Department of Public Health and Primary Care of the University of Cambridge in 2011. In 2013, Stephen was awarded a Wellcome Trust entry-level fellowship (Sir Henry Wellcome Post-doctoral Fellowship) to continue theoretical and applied work in the field of Mendelian randomization. In 2017, he moved to the MRC Biostatistics Unit on a Wellcome Trust/Royal Society intermediate fellowship (Sir Henry Dale Fellowship) to establish a research group in the MRC Biostatistics Unit, where he is now a Programme Leader. In 2023, he was given a Career Development Award by the Wellcome Trust to continue work in this area. He leads a small team of researchers split between the MRC Biostatistics Unit and the Cardiovascular Epidemiology Unit. He is always open to requests for collaboration, either on theoretical or applied Mendelian randomization projects.

‪Stephen Burgess‬ - ‪Google Scholar‬

Amy Mason: Amy is a research associate in the CEU (Cardiovascular Epidemiology Unit​) at the University of Cambridge. She uses the statistical methods developed by this group and the UK Biobank dataset to answer causal questions about heart disease and other illnesses.  Amy's current work plans include coding R packages for easier investigations of non-linear causal associations, and investigating the evidence for traditional risk factors of acute coronary disease in non-White European populations. 

Amy Mason‬ - ‪Google Scholar‬

SUMnlmr, an R package that facilitates flexible and reproducible non-linear Mendelian randomisation analyses | Link

Ashish Patel: Ash is a research associate working on methods to tackle unobserved confounding, selection bias, and general causal inference problems encountered in biostatistics. He is also interested how to make honest inferences after data-driven model selection. He completed his PhD in Economics at the University of Cambridge, where his research centred on improving inference in models that are described by moment restrictions, with a particular focus on empirical likelihood and missing data.

Selection of invalid instruments can improve estimation in Mendelian randomization | Link

Ville Karhunen: Ville is a senior research associate at the MRC Biostatistics Unit. Ville's background is in statistics, and he has completed a PhD in statistical and molecular epidemiology. His main research interests are causal inference and high-dimensional data-analysis, and their applications in molecular epidemiology.

Ville Karhunen - GitHub 

Ang Zhou: Ang is a research associate at the MRC Biostatistics Unit. Before joining Stephen's group, his research focused on utilizing genetic data to understand the causal relationships between nutritional and modifiable lifestyle factors, and health outcomes, particularly in cognitive and cardiometabolic functions. In his current role, Ang is transitioning his research towards methods development, with a focus on instrument selection for the cis-Mendelian randomization analysis.

Ang Zhou - Google Scholar

Janne Pott: Janne is a research associate with a background in Genetic Epidemiology and sex-stratified genetic analyses. She is particularly interested in gene-sex-interactions in dimorphic traits and how that translate into causality on complex diseases. Her current work she focuses on steroid hormones and lipids to identify tissue-dependent causal effects using multivariate MR.

Janne Pott - Google Scholar

Toinét Cronjé: Toinét is a research associate with experience working across academia and industry, and currently at the interface of the two. Her research focuses on advancing the methods and application of the drug target Mendelian randomization paradigm to inform drug development for psychiatric disorders.

‪Héléne Toinét Cronjé - ‪Google Scholar

Benjamin Woolf: Benji is currently a part time PhD student in Genetic Epidemiology at the at University of Bristol and a part time Research Assistant at the MRC Biostatistics Unit. He is interested in most things to do with Mendelian randomisation, especially novel applications such as to second-hand smoking and new drug targets.

MVMRmode: Introducing an R package for plurality valid estimators for multivariable Mendelian randomisation | Link

Nasir Bashir: Nasir joined the group as a PhD student in October 2024, under a fellowship from the Wellcome Trust. His research centres around the development and application of methods to make inference from large-scale data. Nasir originally trained as a dentist at the University of Birmingham, followed by clinical academic posts at the University of Bristol, University of Leeds, and general practice. He then retrained in mathematics, obtaining an MSc in Statistics from the University of Sheffield.

Nasir Bashir - Google Scholar

Long running guests

Andrew Grant: Andrew was a research associate in the MRC Biostatistics Unit at the University of Cambridge between 2018 and 2022, now working as a lecturer at the University of Sydney. 

‪Andrew J. Grant‬ - ‪Google Scholar‬

Pleiotropy robust methods for multivariable Mendelian randomization | Link

Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity. | Link

Apostolos Gkatzionis: Apostolos was a post-doctoral researcher at the MRC Biostatistics Unit in Cambridge between 2017 and 2020, and is now working as a Senior Research Associate at the MRC Integrative Epidemiology Unit in Bristol. He is a statistician by training, with a particular interest in causal inference and genetic epidemiology. In his current research, he is developing statistical methods to adjust for selection bias in Mendelian randomization studies. While being a member of Steve Burgess' group, he has also worked on variable selection methods for pleiotropy-robust Mendelian randomization, as well as Mendelian randomization using correlated genetic variants.

‪Apostolos Gkatzionis - ‪Google Scholar

Contextualizing selection bias in Mendelian randomization: how bad is it likely to be? | Link

Statistical methods for cis-Mendelian randomization | Link

Dipender Gill: Dipender is a clinician scientist who trained at the University of Oxford and went on to become a Specialist Registrar in Clinical Pharmacology and Therapeutics at Imperial College Healthcare NHS Trust. In 2019, he completed a PhD at Imperial College London focusing on the leverage of genetic data to identify causal mechanisms and therapeutic targets in cardiometabolic disease. More recently, he has transferred to St. George's Hospital and St. George's, University of London, where he is appointed as a an NIHR Clinical Lecturer. Dipender also works one day a week as a Specialist in the Genetics Department of the Novo Nordisk Research Centre in Oxford and has continued an honorary affiliation at Imperial College London, where he is supported by the British Heart Foundation Centre of Research Excellence. With over 100 peer-reviewed publications, Dipender remains active in both clinical practice and research.

Dipender Gill‬ - ‪Google Scholar‬

Verena Zuber: Verena was a post-doctoral researcher at the MRC Biostatistics Unit in Cambridge between 2016 and 2018, and is now working as a lecturer at Imperial College London.

Verena Zuber - ‪Google Scholar‬

Leveraging genetic data to elucidate the relationship between Covid-19 and ischemic stroke | Link

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease | Link