Dr Eric R. Gamazon and his team of collaborators develop and apply genomic and computational methods to investigate the genetic architecture of complex traits, including disease risk and drug response. He is interested in what can be learned from DNA sequence and multi-omics data about disease mechanism, therapeutic intervention, molecular evolution, and genome function. In recent highly interdisciplinary work, he is developing computational approaches to studying the structure, dynamics, and stability of biological molecules within Density Functional Theory (DFT), molecular dynamics, and coarse-grained modelling and using experimental data (e.g., from X-ray crystallography or NMR spectroscopy).
An ongoing project involves understanding the effect of genetic variation on gene regulation to gain insights into disease mechanisms and therapeutic targets. He utilizes large-scale DNA biobank data linked to electronic health records to identify genes involved in human health and disease, to discover novel biomarkers, and to enable a comprehensive systems view of the disease phenome.
He is part of the GTEx Consortium and the T2D-GENES Consortium. He was also a member of the International Warfarin Pharmacogenetics Consortium GWAS team.
He is a member of the faculty in the Division of Genetic Medicine in the Department of Medicine, the Vanderbilt Genetics Institute, and the Vanderbilt Data Science Institute.
In 2018, he was elected to a Clare Hall Visiting Fellowship to advance his research and scholarship and mentor graduate students in the University of Cambridge. Fellows become Life Members of Clare Hall, Cambridge and are welcomed back any time to participate in the intellectual life of the college. He is also a visiting scholar in the Department of Medicine, the MRC Epidemiology Unit, and the MRC Biostatistics Unit of the University of Cambridge.
Here is a list of select publications.
Prediction ; Bayesian methods ; Statistical Genetics ; Regulation of gene expression / transcription ; Computational biology ; Functional Genomics ; Data Science ; Cancer Biology ; High-throughput sequencing ; Machine learning ; Omics data management storage and annotation ; Methylation ; Bioinformatics ; Big Data in Biology ; Evolutionary Genomics ; Next Generation Sequencing ; Pharmacogenomics ; Neuropsychiatric Genetics ; Molecular Dynamics ; X-ray Crystallography ; NMR Spectroscopy ; Structural Biology ; Coarse-grained Models ; Density Functional Theory