| Title: | Pleiotropy and genetic risk prediction of complex diseases |
| Lead Institution: | Cedars-Sinai Medical Center |
| Principal investigator: | Dr Jason Moore |
Application 86494
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
Accurate genetic risk predictions of disease susceptibility can aid in tailoring disease risk assessment, diagnosis, and treatment to individual patients or subgroups from the population for improving health and healthcare. However, the current strategy to develop a risk prediction model only utilizes one disease outcome at a time. In reality, a genetic locus can be associated with multiple disease outcomes or traits; a phenomenon called pleiotropy. The overarching goal of this 3-year project is the development of new prediction methods that can use the pleiotropy effects from genome-wide genetic loci to improve the genetic risk prediction of cardiovascular diseases, psychiatric diseases, gastrointestinal diseases, neurological disorders, and cancers. The algorithms and software resulting from the project developments will be publicly released. Successful completion of the project will result in better disease risk stratification of individuals in the general population, which can lead to the improvement of disease diagnosis and treatments in the clinic.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18478 | A deep ensemble encoder network method for improved polygenic risk score prediction | Okan Bilge Ozdemir (+3) | 2026 | BioData Mining |
| 18632 | Investigate the Effects of Preeclampsia Severity, Race and Fetal Sex on Subsequent Maternal Complications Using EHR Data From Over 100,000 Pregnancies | Xiaotong Yang (+10) | 2026 | iMetaMed |
Enabling scientific discoveries that improve human health