Sumitra Muralidhar

This study evaluates cancer-specific polygenic risk scores (PRS) across diverse ancestries and finds they predict corresponding cancers with varying effectiveness and limited pleiotropy.

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

The paper describes methylation profiling in the Million Veteran Program, identifies smoking-associated epigenetic signatures, and reports novel probe-smoking associations through ancestry-stratified EWAS.

This study conducts an X-chromosome-wide association study to identify genetic signals for heart failure and its subtypes, finding novel loci and replicating some in UK Biobank.

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.

The paper identifies causal protein drivers and drug targets for heterogeneous kidney diseases using proteome-wide Mendelian randomization and EHR data.

The paper explores primary care providers' experiences with receiving genetic results from a national biobank study, highlighting challenges in integrating genetic information into routine care.

The paper identifies twelve novel genetic risk loci for lung cancer subtypes through multi-ancestry GWAS and shows smoking-independent genetic risk.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.

This paper optimizes the SAIGE algorithm using GPUs to accelerate GWAS on large datasets, achieving 20x speedup on the Million Veteran Program data.

The paper describes the design and pilot results of a study returning genetic results for familial hypercholesterolemia to MVP participants, showing a reduction in LDL-C after genetic counseling.

This study identifies 240 novel genetic loci associated with mosaic loss of chromosome Y through a multi-ancestry GWAS of the Million Veteran Program and UK Biobank.

The paper analyzes pharmacogenetic variant frequencies in the MVP dataset to represent US population diversity across different ancestries.

This study conducts a trans-ancestry GWAS using chronic ALT elevation as a proxy for NAFLD, identifies 77 significant loci, and validates 17 SNPs with histology and imaging data, revealing genetic links to metabolic and inflammatory traits.

The paper validates polygenic risk scores for schizophrenia, bipolar disorder, and depression using VA EHR data and shows their association with disease risk and hospitalization.

The paper describes the design and performance of the MVP 1.0 custom Axiom array for genotyping a diverse cohort, achieving high-quality genetic data with substantial ancestral diversity.