Eric R. Gamazon

This paper develops a machine learning framework for analyzing mass cytometry data to uncover genetic perturbations and cell-type dynamics in hematopoietic systems.

The paper uses convolutional neural networks to model mutational effects on biochemical phenotypes in SARS-CoV-2 and human ACE2, showing strong predictive power for binding affinity, expression, and antibody escape.

The paper analyzes the human transcriptome using co-expression networks and identifies biologically meaningful clusters and associations with CRP and cancer metastasis.

This paper introduces GAIN-GTEx, a gene expression imputation method that outperforms existing approaches on RNA-Seq data from cancer types and diverse tissues.

eMAGMA converts SNP-level GWAS summary statistics into gene-level associations using tissue-specific eQTL data, outperforming existing methods in identifying causal genes for complex diseases.

This paper conducts a genome-wide association study (GWAS) to identify genetic risk factors for trigeminal neuralgia, finding two significant loci associated with the condition.

The paper identifies 1,645 genes associated with 16 mental health phenotypes using transcriptome-wide association studies and finds higher transcriptomic correlations than genetic correlations for some phenotype pairs.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper identifies 70 gene-level associations with 26 infectious disease traits using transcriptome-wide association studies and explores their molecular basis and implications for host-pathogen interactions.

The paper integrates genomic and proteomic data to identify genetic variants influencing host proteins interacting with SARS-CoV-2, revealing potential drug targets and disease mechanisms.

This study integrates genetic and gene expression data to identify functional gene networks and drug targets in Alzheimer's disease.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

The paper identifies 99 risk genes and gene co-expression modules associated with major depression using a multi-tissue eQTL-based approach, highlighting tissue-specific biological pathways.

This study develops a statistical framework using local ancestry to improve eQTL mapping and heritability estimation in admixed populations, showing improved accuracy and error control.

The paper identifies genes and methylation regions associated with West African ancestry in African Americans and links them to disease and drug response pathways.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.

This paper challenges claims that GCTA cannot reliably estimate heritability, showing errors in the analysis that invalidate those claims.

This study uses PrediXcan to identify and replicate two novel genome-wide significant genes associated with bipolar disorder risk using existing GWAS data.