Eric R. Gamazon

This study identifies a tRNA-derived small RNA that reduces cardiac fibrosis and hypertrophy through cell-type-specific mechanisms in mouse and human cardiac cells.

This study identifies transcriptomic and neurological mechanisms of stuttering using GWAS, TWAS, MR, and neuroimaging analyses of large datasets.

This study uses spatial transcriptomics to analyze gene expression in heart transplant recipients during rejection, identifying distinct transcriptomic profiles associated with different rejection types and outcomes.

The paper identifies ancestry-specific eQTL effects in Hispanic/Latino and African American populations, showing how local ancestry influences gene expression and disease risk.

This study examines how aging and immunosuppression affect immune cell composition and rejection risk in heart transplant recipients, finding lower rejection odds with age and specific transcriptomic changes.

The paper links PSMC3 expression to Alzheimer's Disease through neuroimaging and genetic analyses, showing its association with brain structure changes and dementia family history.

The paper develops a cross-ancestry information transfer framework to improve protein abundance prediction and protein-trait association identification in underrepresented populations.

This study sequenced 35,024 individuals with predominantly African ancestry to address gaps in genomics and healthcare, finding under-documented disease variants and novel associations with drug adverse events and glycemic control.

This paper shows that obesity-related extracellular vesicles from visceral adipose tissue impair cardiac function and contribute to arrhythmia by affecting cardiomyocytes, fibroblasts, and macrophages.

This paper introduces TReD, a framework integrating population-level disease signatures and cell-based drug responses to identify potential therapeutics for COVID-19 and T2D.

This paper introduces TWRCI, an algorithm that identifies root causal genes and their causal graph using genetic and bulk RNA-seq data, outperforming existing methods in uncovering disease mechanisms.

The paper identifies obesity-related transcriptional changes in circulating extracellular vesicles and links them to adipose tissue epigenetics and inflammatory disease risk.

The paper integrates proteomics and transcriptomics with genetic analyses to identify molecular mechanisms and targets of coronary artery disease.

This paper identifies root causal genes using Perturb-seq data and transfers the findings to bulk RNA-seq to reveal patient-specific root causes of disease.

The paper develops a framework to model cell-type-specific gene expression dynamics in neurons and applies it to identify genes associated with schizophrenia and other complex diseases.

This paper identifies a founder mutation in G6PC3 causing congenital neutropenia and shows it leads to loss of protein function and altered glycolysis.

This study identifies population-specific eQTLs in brain tissues from European, African American, and Asian ancestry individuals, revealing new schizophrenia risk genes and variants missed in European-only datasets.

This paper evaluates how functional variant data and penetrance estimates improve risk stratification for LQTS in KCNH2 missense variant carriers.

GeneMAP uses GWAS and multiomic data to predict gene-metabolite associations, identifying SLC25A48 as a mitochondrial choline transporter.

The paper generates a transcriptomic atlas of the human brain using UK Biobank data to link gene expression with neuroimaging phenotypes, revealing genetically determined aspects of neuropsychiatric health.

The paper investigates the relationship between cell death gene expression and human phenotypes using GTEx and UK Biobank data, finding significant associations and tissue-specific effects.

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.

HYFA improves multi-tissue gene expression imputation by leveraging hypergraphs and transfer learning, outperforming existing methods and enabling cell-type signature resolution from bulk data.

The paper introduces CoRE-BED, a framework that identifies novel regulatory elements and shows their association with SNP heritability in complex traits.

This study identifies gene expression changes linked to exfoliation syndrome using TWAS and finds enrichment for inflammatory genes, suggesting inflammation's role in the disease.

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.

The paper uses deep learning to model mutational effects on Aβ42 peptide aggregation traits from DMS data, showing improved performance with geometric-structural features.

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

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 outlines best practices for conducting multi-ancestry transcriptome-wide association studies (TWAS) using the Global Biobank Meta-analysis Initiative data, addressing challenges in ancestry and tissue specificity.