Academic Career and Research Areas
Prof. Zeggini works in the field of Translational Genomics. Her research leverages big biomedical data and aims to translate insights from genomics into mechanisms of disease development and progression, shortening the path to translation and empowering precision medicine. She develops and applies robust statistical and computational tools to integrate information gleaned from deep molecular, genomics epidemiological approaches to address important biomedical research challenges.
Prof. Zeggini obtained a BSc in Biochemistry at UMIST and a PhD in the Immunogenetics of Juvenile Arthritis at the University of Manchester. In 2004, she moved to the Wellcome Centre for Human Genetics in Oxford and in 2008 joined the Wellcome Sanger Institute Human Genetics Faculty. In 2018, she founded the Institute of Translational Genomics at the Helmholtz Zentrum München. In 2020, Prof. Zeggini was appointed to the TUM Liesel Beckmann Distinguished Professorship for Translational Genomics at TUM.
- Member of EMBO (2021)
- Athens Lions Club, Greece (2020)
- Elected Fellow of the Academy of Medical Sciences, London, UK (2020)
- World Economic Forum Young Scientist Award (2017)
- Suffrage Science Heirloom Award, MRC CSC, UK (2014)
Key Publications (all publications)
Zeggini E et al: "Translational Genomics and Precision Medicine: moving from the lab to clinic". Science. 2019; 365(6460): 1409-1413.Abstract
Tachmazidou I, …, Zeggini E: "Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank". Nature Genetics. 2019; 51: 230-236.Abstract
Zengini E, ..., Zeggini E. "Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis". Nature Genetics. 2018; 50: 549-558.Abstract
Gurdasani D, …, Sandhu MS*, Zeggini E*: "The African Genome Variation Project: A framework for medical genetics in Africa". Nature. 2015; 517: 327-332.Abstract
Zeggini E*, Weedon MN*, Lindgren CM*, Frayling TM*, et al: "Replication of genome-wide association signals in U.K. samples reveals risk loci for type 2 diabetes". Science. 2007; 316(5829):Abstract