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Appointment
  • UKRI Rutherford Innovation Fellow
  • Infectious Disease Informatics
  • Institute of Health Informatics
  • Faculty of Pop Health Sciences
Biography

Tom Lumbers is UKRI Rutherford Fellow at Health Data Research UK, co-lead Genomics Group at the UCL Institute of Health Informatics, Honorary Consultant Cardiologist at Barts Heart Centre, and a Visiting Scientist at the Broad Institute of Harvard and MIT. He received his Ph.D. in Molecular Biology at Imperial College London and subsequently completed training in Genetic Epidemiology at University College London. His cardiology training was at Barts Heart Centre and his clinical interests include noninvasive general cardiology, heart failure, and cardiovascular genetics. Tom’s research focuses on defining the genetic architecture of heart failure and left ventricular dysfunction to generate insights into causal factors and molecular disease mechanisms. He coordinates the HERMES Consortium (hermesconsortium.org), an international collaboration in heart failure genetics, and is co-lead of the phenotype working group at BigData@Heart, an EU public-private consortium (bigdata-heart.eu). He has received grant funding from the Medical Research Council, National Institute of Health Research, American Heart Association Precision Medicine Initiative. 

Research Summary

Tom’s research focuses on defining the genetic architecture of heart failure and left ventricular dysfunction to generate insights into causal factors and molecular disease mechanisms. His team and HERMES collaborators have delivered the first in a series of large genome-wide association studies of heart failure which will help to unravel the underlying complex causal basis for this disorder. These analyses are complemented by Mendelian randomisation analyses for causal inference, with a particular focus on proteins as the inferential target, integrating genetic data derived from studies of plasma proteins. Tom is developing tools to deliver scalable and computable disease phenotypes for heart failure to derive insights from large healthcare data resources for genetic research and quality improvement.  

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