| Title: | New statistical methods and analytical strategies to exploit complexities in complex trait genetics. |
| Lead Institution: | Clemson University |
| Principal investigator: |
Application 129216
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About
The aim of this 36-month-long project is to provide new methods for the genetic analysis of complex human traits and disease. We call a trait or disease "complex" when it is affected by both genetic and non-genetic factors. Some examples of complex traits are blood pressure and Alzheimer's disease. Being able to predict complex traits and disease risk from genetic factors using computational/statistical methods is one of the main goals of precision medicine. This will allow early screening of individuals to identify those at risk of developing any complex disease, and take appropriate actions. However, for most complex traits and diseases, prediction accuracy achieved by currently available statistical methods is low. In this project, we tackle this problem by exploiting biological complexity and the vast amount of information available in modern genetic and phenotypic data sets. For example, many traits share some of their genetic component. Therefore, considering these traits jointly in genetic analyses has the potential to improve discoveries and prediction accuracy by the borrowing of information. In addition, genetic factors may not act independently of each other to affect a particular trait - in other words, they may interact. However, the majority of currently available methods ignore these complexities. Thus, we plan to fill this gap by developing new statistical methods and analytical strategies that can make use of this information to increase their performance. The successful completion of this project will provide new methods that will be publicly available to the scientific community and the results of their application to a variety of human complex traits and diseases within the UK Biobank. These new knowledge and methodology will also result in improved prediction accuracy for such traits and will be a significant advance towards effective personalized medicine.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14557 | Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes | Deborah Kunkel (+3) | 2025 | PLOS Genetics |
Enabling scientific discoveries that improve human health