Zachary F. Gerring

This study identifies genetic pleiotropy between major depressive disorder and four physical disease clusters, finding strong associations with gastrointestinal diseases and uncovering novel biological pathways.

The paper uses computational drug repurposing to identify N-acetylglucosamine as a potential therapy for CLN3 Batten disease, showing it reduces lipofuscin burden in cell models.

This study identifies six genome-wide significant risk loci for tic disorders and links genetic risk to specific brain cell types and circuits.

This study identifies genetic variants associated with cervical spinal cord morphology and links them to neurological and systemic diseases.

This paper introduces GNA, a framework that uses network modeling on GWAS data to identify genetic architecture and trait-specific biology.

This study investigates the association between blood mitochondrial DNA copy number and Parkinson's disease, finding that peripheral immune dysfunction, not mitochondrial dysfunction, underlies the observed associations.

This study conducts a large GWAS of retinol, identifies genetic loci, and uses MR-pheWAS to explore retinol's causal relationships with various health traits.

The paper develops TWAS models using eQTL summary statistics to predict gene expression and identifies novel schizophrenia associations.

This study identifies genetic influences on hub connectivity in the human connectome using diffusion-weighted imaging in twins and transcriptomic data.

The paper identifies novel genetic risk loci for depression and anxiety using factor analysis and genomic SEM, revealing shared and trait-specific genetic influences.

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.

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.

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

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 uses Mendelian randomization to find a causal link between major depression and alcohol dependence using large genetic datasets.

This paper uses GWAS data from MDD to identify potential drug targets and repurposing opportunities by analyzing drug-target networks and gene expression effects.

This study identifies 4 new risk loci for cannabis use through GWAS, finds genetic overlap with mental health, and shows a causal link between schizophrenia and cannabis use.