Approved Research

Developing scalable computational tools for understanding polygenic trait architectures in large-scale genome-wide association studies

Lay summary

Aims: To develop scalable statistical models/methods for understanding the architectures of polygenic traits. To reveal the risk factors or basis of human diseases.

Scientific rationale: Several cohorts to date have typically been characterized by small numbers of disease cases, which may yield unstable estimates, incomplete or inadequate measures of potential risk factors, and incomplete or inadequate measures of confounding factors. The UK Biobank involves the largest group of people in the world, it will be a uniquely rich resource for investigating a particular disease. We will develop efficient statistical methods to validate the small cohorts studies we have done leveraging UK Biobank studies. This will help us to understand the causes of diseases better, and to find new ways to prevent and treat many different conditions.

Project duration: 36 months (expected).

Public health impact: We hope that our findings will explain the genetic/familial risk factors for common diseases; assess rare genetic variants and the small effect sizes of associations; well-address the interactions of genes with other disease risk factors, such as environmental exposures and diets; help to propose effective prevention strategies such as guidelines of lifestyles.