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Dr Eric R Gamazon

Dr Eric  R Gamazon

Statistical Genetics

Functional Genomics

Evolutionary Genomics

Machine Learning

Computational Biology

Molecular Dynamics

Coarse-grained Models

Density Functional Theory (DFT)


Biography:

I was trained in genomics and computational biology and have conducted research in human genetics in the Section of Genetic Medicine of The University of Chicago, the Faculty of Medicine (AMC) of the University of Amsterdam, and the Division of Genetic Medicine in Vanderbilt University School of Medicine.

Departments and Institutes

Department of Medicine:

Research Interests

I develop and apply genomic and computational methods to investigate the genetic architecture of complex traits, including disease risk and drug response. I am interested in what can be learned from DNA sequence and multi-omics data about disease mechanism, therapeutic intervention, molecular evolution, and biological function. In recent highly interdisciplinary work, I am 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).

I am actively involved in an international effort (GTEx Consortium) to systematically characterize the effect of genetic variation on gene regulation in a comprehensive set of tissues and create a genomic resource to elucidate the molecular mechanisms underlying disease-associated regions of the genome. I also work on integrating large-scale DNA biobank data and electronic health records to identify genes involved in health and disease.

Keywords

Genomics ; Cancer Biology ; Functional Genomics ; Molecular mechanisms of disease ; Omics data management storage and annotation ; Evolutionary Genomics ; Molecular Dynamics ; Computational biology ; Computational Chemistry ; Bioinformatics ; Bayesian methods ; Methylation ; Data Science ; High-throughput sequencing ; Machine learning ; Big Data in Biology ; Statistical Genetics ; Deep Learning ; Prediction ; Regulation of gene expression / transcription ; Next Generation Sequencing

Key Publications

1. Gamazon ER*, Segre AV*, van de Bunt M, et al. (2018) Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nature Genetics. doi: 10.1038/s41588-018-0154-4.

2. Gamazon ER, Wheeler HE, Shah KP, et al. (2015) A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics. doi: 10.1038/ng.3367.

3. The GTEx Consortium* (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science. 348 (6235):648-660. *Gamazon ER was co-chair of the GTEx GWAS Working Group and a member of the GTEx Analysis Working Group.

4. Smemo S, Tena JT, Kim K, Gamazon ER, et al. (2014) Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 507(7492):371-5. doi: 10.1038/nature13138. Epub 2014 Mar 12.

5. Imputing gene expression in uncollected tissues within and beyond GTEx. Wang J, Gamazon ER, Pierce BL, Stranger BE, Im HK, et al. American Journal of Human Genetics. 2016 Mar 29. pii: S0002-9297(16)00071-9. doi: 10.1016/j.ajhg.2016.02.020.

6. Genetic architecture of microRNA expression: implications for the transcriptome and complex traits. Gamazon ER, Ziliak D, Im HK, LaCroix B, Park DS, et al. American Journal of Human Genetics. 2012 Jun 8;90(6):1046-63.

7. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. Nicolae DL, Gamazon E, Zhang W, et al. PLoS Genetics. 2010 Apr 1;6(4):e1000888.

 

Other Publications

Full publication list: https://www.ncbi.nlm.nih.gov/myncbi/browse/collection/50263236/?sort=date&direction=descending