| Title: | Causal gene mapping for complex traits by leveraging genomics and proteomics data |
| Lead Institution: | Zhejiang University |
| Principal investigator: |
Application 102158
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
In this project, we aim to find out which genes are connected to certain diseases by using different methods. We also want to see how these genes might affect our health in multiple ways and how they can be influenced by our environment. We already know about many genetic markers that are related to common diseases (e.g., type 2 diabetes), but we don't fully understand the mechanisms. Because of the complexity of the human genome, the physical position of a genetic marker may mislead the identification of its related "driver gene" which causes the disease. Empirical evidence shows that we will have a hard time identifying a driver gene among a number of "passenger genes" if we purely rely on genetic variants as markers, indicating that more comprehensive approaches are needed. In this project, we will integrate information about genes, proteins, metabolites, and traits to find out which genes are potentially causing the disease. Given the hypothesis that people from different genetic backgrounds may share the same driver genes, we are proposing the use of novel approaches with higher resolution to deduct potential driver genes for common diseases. The findings of this project could help scientists to have a better understanding of diseases and to find new ways to treat them. This could mean better medicines that work for more people and personalized treatments based on a person's genes. They could also identify new target genes for existing drugs, which could help repurpose them for different conditions. Overall, this project could help us learn a lot about how our genes affect our health and how we can use this knowledge to improve healthcare.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8248 | A phenome-wide scan reveals convergence of common and rare variant associations | Dan Zhou (+4) | 2023 | Genome Medicine |
Enabling scientific discoveries that improve human health