| Title: | Developing scalable statistical methods for association studies of large-scale genetic variants and time-to-event outcomes |
| Lead Institution: | East China Normal University |
| Principal investigator: |
Application 97366
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About
Most diseases are complex and stem from interactions between genes and environment factors. Better understanding the connection between genes and the environment helps to find new ways to prevent and treat diseases. The UK Biobank involves the largest group of people in the world, and enables one to extract genetic variants and environment factors, it will be a uniquely rich resource for investigating gene-environment interaction. However, the analysis of interactions has long been a challenging problem due to the large number of candidate interaction effects. Aims: We will develop scalable and accurate statistical models/methods to discover novel interactions of genes and environment factors (e.g., smoking, alcohol) that underpin diseases. We hope that our findings will explain the genetic/environmental risk factors for diseases prognosis; identify novel interactions of genes with environmental risk factors; help to propose effective prevention strategies such as guidelines of lifestyles.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15927 | Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study | Zhengsheng Gu (+8) | 2025 | JMIR Aging |
Enabling scientific discoveries that improve human health