Aims: The primary goal will be to develop statistical models and (freely accessible) software that will enable scientists to better understand the genetic risk factors playing a role in determining complex traits- including a) disease traits such as cardio-vascular diseases, obesity, diabetes, autoimmune diseases, mental illnesses, etc. b) non-disease quantitative markers such as weight, cholesterol, blood pressure etc. and c) response to medicines or other interventions. The specific aims will be to develop models/tools and apply them on UKBB dataset to: Aim-1) discover genetic risk factors having causative effect, Aim-2) their ‘interactions’ (i.e., relationships among genetic or environmental or life-style risk factors) and Aim-3) Inter-relationship among various complex traits.
Scientific Rationale: Complex traits arise in an individual’s life course depending on the individual genetic make-up, exposure history and lifestyle (diet, physical activity etc.). Complex traits are also inter-related – for example, developing high-cholesterol predisposes individuals to cardiovascular (heart) diseases later. The UK Biobank being a large cohort study provides the first unique resource to enable researchers globally to understand the trajectory of multiple complex traits in an individual through their clinical history data, genetic data and detailed exposure history data. However, sophisticated mathematical models are required to make appropriate inferences from such a rich and complex cohort dataset. The UK Biobank data set will be used crucially in all steps of the project, firstly for building mathematical models and testing them and then again for applying those models to study various complex traits.
Project Duration: We are requesting for a 3-year rolling period. We shall generate and test some statistical models within the first 3 years. We shall then make these methods available through software to the community to enable other researchers. Beyond 3 years, we shall ourselves carefully apply these models to specific classes of diseases in UKBB data to draw useful insights about complex disease onset and progression. During this second phase we also expect to make further refinements and improvements of our models.
Public Health Impact: Developing such statistical models will help the biomedical science community to better understand factors involved in complex disease onset, progression, and differential outcome. This would facilitate future development of public health measures such as screening, personalized disease prevention and management. Further, identification of specific genetic features involved can guide future research on development or repurposing of drugs targeting the specific genes or biological pathways to cure or manage these diseases.
