GEMINI - Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions
- Timothy Frayling, Prof. Human Genetics
- University of Exeter, UK
- Timothy Frayling, Prof. Human Genetics, University of Exeter, UK
- Jose Maria Valdera, Prof. Health Services & Policy Research, University of Exeter, UK
- Conxa Violan, PhD, MD. IGTP, Catalonia, Spain
- Rafael de Cid, PhD. GCAT Chief Scientist, Genomes for Life-GCAT Lab, IGTP, Badalona, Spain
- Natalia Blay, PhD student. Genomes for Life-GCAT Lab, IGTP, Badalona, Spain
- Albert Rosso, PhD IDIAP Jordi Gol, Catalonia, Spain
- University of Exeter, UK
- Germans Trias i Pujol Research Institute (IGTP), Catalonia
- IDIAP Jordi Gol, Catalonia
Co-existence of multiple chronic conditions in a single individual (multimorbidity) is challenging using conventional study designs. Confounding, bias and reverse causality are often complex and severe and may partly explain apparently paradoxical associations.
To address these challenges by combining genetic and conventional approaches and using large-scale data resources from the UK, Spain, US and Canada, including 3 multi-million patient GP data sources. We will identify clusters of disease, use novel causal inference methodology to identify shared biological determinants, and study in-depth a set of disease clusters. By understanding biological determinants of multimorbidity clustering and identifying which are associated with markedly altered clinical outcomes, we will help clarify which multi-morbidity combinations are of most clinical importance to understand.
We will define multimorbidity as the presence of 2 or more chronic conditions but focus on those each occurring in >1% of men or women aged 40 plus and that are genetically correlated with other conditions. To address inequalities of multimorbidity we will study the excess burden in women and ethnic minorities. A multi-modal, data driven approach will be critical. Clustering that is consistent across genetic and observational data will be more reflective of shared determinants. Genetic approaches provide a test of lifelong exposure to risk factors and provide strong causal inferences. The widespread availability of genome wide information also means that we can study shared risk factors that are not measured in many studies (e.g. insulin resistance) and calculate disease clustering between, as well as within, databases.
To achieve our vision we have formed a new multi-disciplinary research team (supported by outstanding external advisors), including researchers with extensive experience in multimorbidity in three GP databases; physical and mental decline in the elderly; specialists in key diseases (diabetes, vascular, dementia, musculo-skeletal) and with expertise in genetics and causal inference.