| Title: | Statistical methods development for genetic basis of complex traits and predictions |
| Lead Institution: | University of Calgary |
| Principal investigator: |
Application 100532
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About
The effectiveness of precision medicine relies on healthcare plans that can be tailored to an individual's traits. To plan the most helpful course of treatment of action, an individual's traits need to be accurately predicted from their unique combination of genetic material. Two recent technological advancements are bringing this predictive pipeline into the realm of clinical possibility. The first is the increased availability of linked genetic and clinical information. Bringing these variables together facilitates cross-disciplinary insight, where the predictions of one can inform the other. The second is the application of machine learning to healthcare problems, leveraging next-generation computing techniques to make full use of high-dimensional biological data. However, there is a gap between theoretical predictive performance and the complex reality of patient outcomes. In this project, we will study the integration of genomics with other data such as imaging files and health outcomes to augment experimental design. By pre-identifying associations of biological significance, our tool will characterize the genetic basis of complex disease. The ultimate goal is to predict various medically important traits, including risk of diseases, responses to potential medication, etc.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9144 | A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders | Jingni He (+12) | 2023 | American Journal of Human Genetics |
Enabling scientific discoveries that improve human health