Speaker: Inês Barroso, Bsc, MSc, PhD, University of Exeter Medical School, UK

Title: Using MAGIC to improve type 2 diabetes precision medicine

Host: Paul Franks

Inês Barroso is Professor of Diabetes within the Exeter Centre of Excellence for Diabetes Research (EXCEED), at the University of Exeter Medical School. She is co-lead of the diabetes theme within the newly established NIHR Exeter Biomedical Research Centre, and is the PI of an eight year Wellcome funded project focusing on precision type 2 diabetes diagnosis.

Inês obtained her BSc in biology from the University of Lisbon (1992), and her PhD in human molecular genetics from the University of Cambridge (1996). After her PhD, she was a founding scientist at a start-up biotechnology company where she became Director of Diabetes Target Validation. In 2002, Inês moved to the Wellcome Trust Sanger Institute where she worked for 16 years, which included serving as Head of the Human Genetics department for six years. Prior to joining the University of Exeter (Jan 2020), she spent a year at the MRC Epidemiology Unit as Director of Research, Mechanisms of Metabolic Disease. 

Her research traverses both the monogenic and polygenic fields, and focuses on the genetic aetiology of type 2 diabetes and obesity. Since 2009, she has led MAGIC, a consortium meta-analysing genome-wide association studies from diverse populations to uncover loci associated with glycaemic traits. She is particularly interested in improving understanding of glycated haemoglobin (HbA1c) measurement and the factors that may alter its reliability as a measure of glucose. She has recently received funding from Wellcome to develop a type 2 diabetes precision diagnosis approach using HbA1c, with the goal of improving diagnosis in diverse underserved population groups.

In addition, Inês is interested in using genetics to improve diabetes prediction, and to use epigenetic marks and genetics to improve understanding of disease genes and pathways. To do this, Inês’ research combines both genomic data and computational approaches to link genetic variants and genes to function and biological pathways.

Link to the MAGIC website