Dr Eric Gamazon

Contact information


I was trained in statistical genetics, computational biology, and genomics 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. I am a recipient of the inaugural Genomic Innovator Award (https://www.nih.gov/news-events/news-releases/nih-announces-six-inaugura...) from the National Institutes of Health (NIH) in the US. I am a Life Member of Clare Hall, Cambridge.

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. An ongoing project involves understanding gene regulation across tissues and cell types to gain insights into disease mechanisms and therapeutic targets. I utilize large-scale DNA biobank data linked to electronic health records, along with data science and computation, to identify genes involved in human health and disease in diverse populations, to discover novel biomarkers, and to enable a comprehensive systems view of the disease phenome.

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.

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 techniques (e.g., X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy).

My research goals are to understand the biology of genomes and to advance genomic medicine.


1. Zhou, D., Jiang, Y., Zhong, X., Cox, N.J., Liu, C., and Gamazon, E.R. (2020). A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nature Genetics 52, 1239-1246.
2. Gamazon ER, et al. (2019) Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. Nature Genetics. doi: 10.1038/s41588-019-0409-8.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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, see: https://www.ncbi.nlm.nih.gov/myncbi/eric.gamazon.3/bibliography/public/

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

  • Supports and connects the growing data science and AI research community 
  • Builds research capacity in data science and AI to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Acts as a gateway for external organisations 

Join us.