Approved research

Development of a novel multi-dimensional polygenic risk score method that better predicts complex disease

Lay summary

Predicting susceptibility of an individual to disease is essential in the clinical context of early disease detection and prevention. Currently, risk prediction for diseases often relies on basic information of an individual, such as age, gender, BMI, physical exercise, smoking status, alcohol consumption, blood pressure, blood chemistries, and family history. However, although recent studies have led to a better understanding of genetic factors underlying several highly heritable diseases, examination of the genetic factors is usually ignored in common health management. Polygenic risk score (PRS) is a method quantifying the genetic risk of having a disease based on variations in multiple genetic loci. Recent researches on PRS have shown promising result for some highly heritable diseases. However, current methods of PRS still have some limitations. Those limitations include ethnically biased reference data, incomplete understanding of the genetic architecture of complex disease, etc. In this project, we aim to develop a novel polygenic risk prediction method of high accuracy by accounting for trans-ethnic genetic architecture, environmental factors, and/or non-linear gene-gene interaction. We will test our newly developed method on multi-ethnic real-world data for validation. A more accurate and trans-ethnically applicable genetic risk assessment of complex disease will result in enhanced guidance in the targeted prevention of complex disease globally.