Principal Investigator: Professor Peter Donnelly
Department: Wellcome Trust Centre for Human Genetics
Institution: University of OxfordTags: 15357, personalised genomics, Pharmacogenomics, PheWAS, pleiotropy
1a: Progress in the field of pharmacogenetics is increasingly allowing a better understanding of the molecular mechanisms modulating drug sensitivity, efficacy and toxicity. Preliminary evidence suggests that genetic variants in genes involved in these processes, known as pharmacogenes, can also lead to an altered risk of disease in carriers. Therefore, their diverse established and intertwined roles in metabolic pathways make them an ideal system to study pleiotropy, a phenomenon in which a single locus affects distinct traits. Here, we propose to use the UK Biobank data to systematically identify pleiotropic relationships among known pharmacogenes, taking advantage of innovative computational methods.
1b: The field of pharmacogenomics has become one of today’s most promising aspects of personalised genomics. Identification of variants that may have systemic disease risk as well as altered drug response would provide insights to help optimizing genetic testing strategies and drug therapy, leading to better prevention of adverse effects of treatment. Furthermore, identifying relationships between phenotypes and pharmacogenes, most of which have known functions, will lead to improved understanding of the molecular mechanisms of disease.
1c: We will select a collection of genes previously identified in pharmacogenomics studies that consists of established pharmacogenes, as well as GWAS hits for drug response. Phenotype data and electronic medical records information from participants will be used to perform statistical tests of association with each pharmacogenetic marker. As a first approach, the Phenome-Wide Association Study (PheWAS) methodology is appropriate to explore genotype-to-phenotypes associations. As this method harbours non-negligible methodological challenges, especially in properly defining the “phenome” and in dealing with covariates, novel approaches to identify pleiotropic interactions will be developed and applied to this extensive list of pharmacogenes.
1d: Full cohort