Principal Investigator: Dr Valentina Iotchkova
Institution: University of OxfordTags: 34405, cardiovascular, causality, genetics, Haematological, methods, regulatory
The wealth of data and expertise for evaluating the genetic contribution to human traits in health and disease is continuously growing. However, the genetic architecture of complex traits, which are affected by multiple genes, lifestyle and environmental factors, still remains largely unexplained.
We propose to use the power of UK Biobank data, together with the development of novel computational methods to address the following key questions. First, to identify and understand genetic changes that lead to changes in phenotypic traits, such as haemoglobin levels or cardiovascular disease. Then, to use this information to infer direction of causality between different disease and disease risk factor traits. In parallel, we aim to identify currently unknown regulatory variants and regions with functional significance for gene regulation, human health and disease. Finally, we plan to explore the role of variability of the genomic sequence context and associated genetic variation in defining the initiation and progression of complex diseases.
Our main focus will be on quantitative measurements representing biomarkers of cardiovascular/haematological disease, however for comparison purposes, we plan to also examine different sets of traits, such as anthropometric measures.
Our research will expand basic science discoveries of human genetic variation through novel statistical and computational method development, and evaluation of whole-genome genotypes and cardiovascular and haematological traits. These efforts are expected to provide insights into the identification of new disease genes and druggable pathways. Additionally, interrogating regulatory variation in genomic regions, such as the globin clusters, has the potential to make a significant impact on global health as genetic variants in these regions influence anaemia and malaria with estimated 1.6 billion and 300-600 million people affected, respectively.
We request access to data on the full set of UK Biobank samples for a rolling 3-year period.