Tom R. Gaunt

The paper identifies NK cell activation as a genetic and functional contributor to MDD susceptibility using single-cell transcriptomics and population genetics.

This study uses Mendelian randomization to identify risk factors for kidney function decline in CKD and the general population, finding causal effects of type-2 diabetes and high TSH.

The paper identifies age-dependent methylation quantitative trait loci (longitudinal mQTLs) across the lifespan using blood DNAm data from two cohorts, finding consistent genotype-by-age effects and associations with multiple phenotypes.

This study uses Mendelian Randomization to prioritize drug targets for immune-mediated diseases by analyzing immune cell abundance and protein QTLs, identifying potential repurposing opportunities.

The paper introduces GPMap, a resource that maps genetic associations across traits and molecular measurements to identify pleiotropy and improve disease mechanism understanding.

CanDrivR-CS distinguishes recurrent and rare cancer variants using cancer-specific gradient boosting models, achieving high F1 scores in cross-validation.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

This study uses Mendelian randomization to assess how cardiometabolic drugs affect kidney function, finding specific drug targets influence eGFR and related biomarkers.

This study uses Mendelian randomization to identify two proteins, CCL27 and TNFRSF14, that influence subsequent cardiovascular events after a stroke, suggesting inflammation as a contributing factor.

DrivR-Base extracts and integrates molecular features for variant effect prediction models using data from AlphaFold, ENCODE, and Variant Effect Predictor.

This paper compares two Mendelian Randomization approaches to assess pathway-specific effects of blood pressure and BMI on cardiovascular traits, finding distinct contributions from different Mendelian disease categories.

This paper uses Mendelian randomization and electronic health records to show SGLT2 inhibitors reduce prostate cancer risk, with additional evidence from differential expression analysis.

The paper maps human traits from UK Biobank to the Experimental Factor Ontology using language models, finding BioSentVec and BlueBERT-EFO perform best.

The paper integrates brain RNA-seq and genotype data to identify eQTLs and their downstream effects on brain-related diseases.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

This paper shows that meta-analysis fine-mapping is often miscalibrated due to cohort heterogeneity and introduces a QC method to identify suspicious loci.

This paper uses proteome-wide Mendelian randomization across multiple ancestries to identify causal protein-disease associations and drug targets in over 1.26 million individuals.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

The paper constructs an atlas linking 129 polygenic risk scores to 249 metabolites in 83,004 UK Biobank participants, finding associations with BMI and smoking liability on systemic inflammation.

PWCoCo improves genetic colocalisation by integrating conditional and colocalisation analyses, enabling robust detection of independent signals and secondary loci in high-dimensional omics data.

This paper evaluates block jackknife resampling in Mendelian randomization to reduce overfitting bias from overlapping samples, showing it outperforms external GWAS and overlapping sample methods in robustness.

This study uses multi-ancestry Mendelian randomization to identify six protein targets for COVID-19 severity, showing ancestry-specific effects.

The paper uses genetic variation to assess the causal impact of food choices on health outcomes, identifying robust associations between eating patterns and several diseases.

The paper investigates the stability and regulatory implications of DNA co-methylation across age and ancestry, finding stable patterns and links to transcription factor binding and chromatin interactions.

This study combines protein and gene expression QTLs to evaluate their concordance in drug target validation, finding that 25-30% of QTL pairs show discordant effects.

The paper develops a variance prioritisation method to detect gene-interaction effects on blood biomarker concentrations using UK Biobank data, identifying novel gene-environment and gene-gene interactions.

The paper estimates selection coefficients for DNA methylation sites and finds evidence of both positive and negative selection, highlighting DNAm's role in biological pathways.

This study uses multivariable Mendelian randomization to show that higher bone mineral density causally increases osteoarthritis risk independently of body mass index.

The paper uses Mendelian randomization and clinical/biological evidence to prioritize lipid-modifying drug targets for coronary heart disease prevention.

This study shows genetic associations of CRP variants with BMI differ between individuals with and without cardiometabolic diseases, suggesting BMI modifies these genetic effects.