Phenome-Wide Association Studies to inform therapeutic development at Novartis Institutes for Biomedical Research
Principal Investigator: Dr Jason Laramie
Approved Research ID: 33087
Approval date: September 3rd 2018
Biology is highly complex, with many interdependent and interacting processes, whose modulation can have various effects. Thus, a single pharmacotherapy can treat multiple (possibly unrelated) clinical disorders, and a single disease may benefit from multiple therapies, owing to its multifactorial etiology. This study aims to use UK Biobank resources to (i) identify disorders that may benefit from existing pharmacotherapies, (ii) prioritize complementary targets for existing indications, and (iii) evaluate unanticipated adverse effects of target modulation. We will conduct a Phenome-Wide Association Study (PheWAS) of genetic variation in genes related to select drug targets in relation to available clinical phenotypes. This study is directly aimed at increasing the number and range of disorders being tested in clinical trials with pharmacotherapies under development at Novartis Institutes for Biomedical Research. The ultimate goal is to increase the number of people whose illnesses can be treated effectively with pharmacotherapies, which is part of the core mission of the UK Biobank. Because the UK Biobank is such a large phenotypically-rich population- based resource, this one investigation we propose has the potential to fuel numerous parallel indication efforts across Novartis research disease areas. Common variation in genes related to drug targets will be tested for association with all available traits collected in the UK Biobank, including but not limited to hospital codes, physical and cognitive function measures, imaging derived phenotypes, molecular & cellular test results and psychosocial & mental health information. We will also derive traits related to tendon disease, activity measured by accelerometers, and use machine learning to examine retinal and liver imaging. Over the three year study period, these results will contribute to drug development by using human data to prioritize targets and their primary indications, understand potential safety concerns, and stratify patients.