Exploring the genomics of morbidity clustering in individuals Objective 1 will produce a UK-wide picture on which long-term health conditions occur together. Objective 2 will then use this information to understand how genetic differences between individuals could influence the risk of developing these groups of co-occurring conditions. We will also be using data from the UK Biobank, a study which holds the medical histories and genetic data of half a million volunteers. By looking at locations in the genome where we know people commonly differ, we can look for possible genetic causes that result in people having multiple long-term conditions. Because our genetic make-up is fixed at birth, this part of the study raises the exciting possibility of predicting an individual’s risk of future multiple long-term conditions and even developing preventative treatment. Genomics Albert Tenesa - Genomics Lead Image Albert Tenesa is Professor of Quantitative Genetics. He has over 10 years experience in developing computational and statistical tools to analyse big genomic datasets and has applied them to large cohorts like UK Biobank leading to a number of impactful publications and databases. He will lead the objective 2 genomics work in UK Biobank, and contribute to the objective 1 work developing clustering solutions to be used in objective 2. Find out more about Albert at the link below: Albert's profile page Konrad Rawlik - Data Curation Project Analyst Image Konrad Rawlik has a background in machine learning and is currently a Core Scientist at the Roslin Institute working on the development of statistical and computational methods for the genetic analysis of complex traits in large datasets, including developing the first published phenome-wide, genome-wide analysis of UK Biobank. By providing a perspective which bridges machine learning and genetics, he will contribute to the integration of objective 1 clustering solutions into objective 2 genomic analysis. Find out more about Konrad at the link below: Konrad's profile page Clinical Marcus Lyall - Clinical and Frailty expertise Image Marcus Lyall is a consultant physician who leads regional data-driven innovation in acute medicine, part of the governance team for the Lothian DataLoch, and a clinician data scientist researching outcomes and challenges in unscheduled care clinical pathways. He will contribute clinical and frailty expertise across the programme, particularly in relation to objective 4 work examining adverse events associated with complex multimorbidity and polypharmacy in acute care. This article was published on 2024-09-24
