Guhan Ram Venkataraman

This study uses multi-ancestry admixture mapping in the UK Biobank to identify ancestry-associated disease loci, finding 13 genome-wide significant associations and four novel risk loci.

The paper develops a method to identify genetic predictors of cardiometabolic disease progression using time-to-event data from the UK Biobank, finding ten polygenic hazard scores that significantly predict disease progression.

This study uses mass spectrometry proteomics to identify protein altering variants and map pQTLs, confirming target specificity and identifying novel variants not accessible by affinity proteomics.

This study identifies novel plasma biomarkers for Alzheimer's disease and related dementias using unbiased proteomics in a large cohort.

The paper identifies ten new genes with coding variants linked to Crohn's disease risk using sequencing data from over 30,000 patients and 80,000 controls.

The paper develops sparse polygenic risk scores for 813 traits using UK Biobank data, showing significant predictive performance and a correlation between variant count and performance.

The paper introduces a Bayesian model comparison approach, MRP, for rare variant association studies using summary statistics, demonstrating improved power and discovery of gene-trait associations across multiple phenotypes.

The paper introduces a Bayesian mixture model to cluster rare-variant effects across multiple phenotypes and demonstrates its application using UK Biobank data.

This paper introduces a latent factor model for polygenic risk scores that decomposes genetic risk into interpretable components, showing improved interpretability over standard PRS for traits like BMI and myocardial infarction.

The paper analyzes HLA allele associations with 677 phenotypes in the UK Biobank, identifying 88 replicated and 90 novel associations, and uncovering non-additive and homozygous effects on disease risk.

The paper identifies rare protein-altering variants in ANGPTL7 that lower intraocular pressure and reduce glaucoma risk by analyzing genotyping data from UK Biobank and FinnGen.

The paper identifies 138 associations between common protein-altering variants in GPCR genes and various phenotypes using UK Biobank data.

UK Biobank genetics of 38 blood and urine lab tests yields 1,857 independent loci; combining biomarker and disease polygenic scores improves FinnGen prediction of incident renal failure and alcoholic cirrhosis.