Towards optimal human performance: Discovering molecular mechanisms of human phenotypes
Principal Investigator: Dr Naisha Shah
Approved Research ID: 51824
Approval date: July 3rd 2019
With the development of high-throughput studies and interest in big-data analyses, several organizations and groups have collected data from large cohorts to better understand human health and disease. To identify risk factors and novel biomarkers for disease prevention and treatment, gathering multi-layered data and longitudinal follow-up of individuals are utmost critical. To that end, UK BioBank (UKB) is a tremendous resource for valuable information collected longitudinally on a large population. UKB provides a unique opportunity to connect heterogenous measurements, which inquires an individual's health, from several modalities including genetics, MRI, clinical labs and EHR data. In addition, the large population size provides statistical power to gain insights into factors that contribute to optimal health. The principal goal of this study is to decipher molecular underpinnings of phenotypes to better understand human health and wellness. We plan to apply both supervised and unsupervised machine learning techniques to find novel association networks between molecular and phenotypic features and identify subgroups of individuals that share similar phenotypic profile to find factors that can lead to optimal health. In the process, we will also develop novel methods to further scientific research. Thus, aligning with UK BioBank's aim of 'improving the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses'. We expect that the project will run for five years and be updated as required.