Shefali S. Verma

This study maps POAG risk variants to candidate effector genes in ocular cell types, identifying ARHGEF12 as a potential target.

TRACE uses a fine-tuned biomedical language model to identify drug-gene pairs from TWAS data, achieving high recovery rates in validation studies.

This paper uses GWAS and mediation analysis on CT-derived phenotypes to show genetic variants influence disease through intermediate imaging traits.

The PMBB Geno-Pheno Toolkit provides modular pipelines for cross-biobank association analyses, supporting GWAS, ExWAS, and PheWAS with containerized workflows.

DRIVE-KG integrates multi-omics data into a knowledge graph to discover SNP-phenotype associations and predict endometriosis status, uncovering new variant-phenotype links and improving classification performance.

This paper develops endophenotype-informed polygenic risk scores for POAG in African ancestry individuals, showing improved clinical interpretability and risk stratification compared to aggregate scores.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This study investigates how disease-associated Kv1.3 variants affect protein folding and stability, finding that R114G variant shows reduced folding probability and structural instability.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

This study investigates the association between polygenic risk scores for blood pressure traits and preeclampsia, finding shared genetic architecture but no improvement in prediction models.

The paper examines how genetic and environmental factors, including social determinants of health, influence women's health outcomes by analyzing polygenic risk scores and environmental exposures in the All of Us cohort.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

This study identifies ancestry-specific and sex-specific genetic loci for primary open-angle glaucoma through a meta-analysis of 15 biobanks, highlighting shared biology with vascular mechanisms and cell proliferation.

The paper identifies epistatic SNP-SNP interactions in long-range LD across five diseases using UK Biobank and eMERGE data, finding associations involving conserved genes and essential genes.

The paper evaluates the frequency and impact of pharmacogenetic alleles in a diverse biobank population, finding high prevalence of actionable alleles and disparities by ancestry.

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 study evaluates how ancestry, GWAS sample size, and LD reference panels affect BMI PRS performance across different age groups and ancestries.

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 defines a standard format for GWAS summary statistics to improve data sharing and analysis.

This study evaluates polygenic risk scores (PRS) for cancer prediction in a racially diverse biobank, finding moderate improvements in discriminatory accuracy for European ancestry but limited gains for African ancestry.

The paper uses multi-ancestry biobank data and integrative methods to identify gene-disease associations across different ancestry groups, finding shared and ancestry-specific genetic links.

The paper calculates polygenic risk scores for substance use disorders and finds associations with related phenotypes in electronic health records.

This paper conducts a PheWAS using a Korean health check-up database and compares results across ethnic groups to identify phenotype-genotype associations and their clinical relevance.

The paper identifies a novel POAG-associated SNP in African Americans using a genome-wide association study.

The paper evaluates polygenic risk scores for psychiatric disorders in electronic health records, finding cross-trait associations and confirming genetic architecture from large consortia.

This study develops polygenic risk scores for cardio-renal-metabolic diseases in the Penn Medicine Biobank and finds limited predictive value in African ancestry individuals.

The paper introduces a collective feature selection approach to identify epistatic variants by combining multiple methods, showing improved performance in detecting true positive interactions in both simulated and real BMI data.

This study simulates PheWAS to assess how sample size, case-control ratio, minor allele frequency, and disease penetrance affect statistical power for binary and quantitative traits.