Stephen Burgess

This study uses Mendelian randomization to investigate ECS gene variants and their associations with dementia risk and biomarkers, finding some links but no strong causal evidence.

This study uses genetic data to show that HMGCR inhibition affects coronary artery disease and type 2 diabetes through different causal pathways, with BMI and LDL-C playing distinct roles.

This study identifies a genetic association between thromboembolism risk and long COVID in individuals of European ancestry, suggesting thromboembolism as a potential contributor to long COVID pathogenesis.

This study identifies genetic determinants of plasma protein variance across ancestries and shows their impact on cardiometabolic disease risk.

The paper benchmarks four colocalization methods using protein QTL data to assess their reliability and consistency in detecting shared genetic causes.

This paper investigates the causal impact of opioid use disorder on brain health outcomes using observational and genetic analyses, finding increased dementia risk associated with both OUD diagnoses and genetic predisposition to OUD.

MR-SPLIT reduces bias in one-sample Mendelian randomization by addressing weak instruments and selection bias through sample-splitting and cross-fitting.

This paper introduces Bayesian methods for Mendelian randomization using summary statistics to address correlated pleiotropy in both univariable and multivariable settings.

This paper introduces Multi-response Mendelian randomization (MR2) to identify shared and distinct exposures for multiple disease outcomes, showing improved power and accuracy by accounting for residual correlations between outcomes.

This paper introduces a doubly-ranked stratification method for non-linear Mendelian randomization to avoid parametric assumptions, showing it provides unbiased estimates even with non-linear or heterogeneous instrument-exposure relationships.

This paper introduces a new method for one-sample Mendelian Randomization that reduces bias from weak instruments and enables cross-ethnic analysis.

SUMnlmr enables non-linear Mendelian randomisation analyses using summarised data, improving flexibility and reproducibility in studies without individual-level data.

This study identifies 144 genetic loci associated with abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target through Mendelian Randomization and pre-clinical validation.

This study uses Mendelian Randomization to show that a higher genetic response to statins is linked to increased intracerebral hemorrhage risk among statin users.

The paper clusters genetic variants associated with body mass index to identify distinct biological mechanisms underlying obesity and its impact on coronary heart disease.

The paper uses polygenic scores to identify plasma proteins causally linked to cardiometabolic diseases and finds some are targets for existing therapies.

This study uses Mendelian randomization to identify plasma proteins associated with stroke risk and potential therapeutic targets.

This paper introduces a Bayesian variable selection algorithm for Mendelian randomization that identifies genetic variants with minimal pleiotropy, showing significant risk-increasing effects of blood pressure on coronary heart disease.

This study conducts the first GWAS of eGFR in Africans, identifies two genome-wide significant loci, and highlights the need for more African-specific genetic research on kidney function.

This paper introduces HyPrColoc, a Bayesian algorithm for multi-trait colocalization using GWAS summary statistics, identifying 43 regions where coronary heart disease colocalizes with other traits.

This study uses Mendelian randomization to show that genetically proxied IL-6 signaling inhibition reduces COVID-19 risk but increases pneumonia risk.

This study uses Mendelian randomization to examine if ACE inhibitors or cardiometabolic risk factors affect lung ACE2 and TMPRSS2 expression or plasma ACE2 levels, finding no overall association.

This paper uses Mendelian randomization and colocalization to identify causal relationships between plasma proteins and disease phenotypes, finding 111 putatively causal effects.

This paper conducts a genome-wide association study (GWAS) of skin and soft tissue infections (SSTIs), identifies genetic loci, and shows that BMI and smoking increase SSTI risk.

MR-Clust clusters genetic variants with similar causal estimates in Mendelian randomization to identify distinct causal mechanisms.

The paper develops a polygenic response score to identify subgroups with different treatment effects by testing gene-gene interactions using pharmacomimetic genetic variants.

This study identifies 118 genetic loci associated with 2,319 metabolite features and finds 46 causal effects of metabolites on common diseases using genome-wide association and Mendelian randomization.

The paper identifies a genetic variant in PROCR that influences protein C and factor VII levels, linking it to both reduced coronary artery disease risk and increased venous thromboembolism risk through opposing inflammatory and thrombotic mechanisms.

This study uses Mendelian randomisation to examine the causal effects of BMI, body fat composition, and height on cancer risk, finding limited evidence for BMI's role but associations for FMI and height.

This study identifies genetic associations with metabolite levels across multiple cohorts and links these variants to clinical outcomes like type 2 diabetes and macular telangiectasia type 2.