| Title: | develop statistical genetic methods with hidden subgroups |
| Lead Institution: | University of California, Irvine |
| Principal investigator: | Professor Wei Li |
Application 78898
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About
In this study, we will develop novel methods to identify who (which subgroup of people) have a higher risk of diseases (e.g., breast cancer) than others. Previously, scientists have shown that some humans are born with variations in their DNA (known as "risk variants") that increase their lifetime risk of some diseases (e.g., breast cancer). Using these risk variants, scientists defined the polygenic risk score (PRS) to predict if a person will have a high risk to have a certain disease (e.g., breast cancer). However, previous studies do not consider the difference between people. In this study, we will develop novel methods, which will take into account population differences, to obtain more accurate disease risk predictions. We will mainly focus on brain diseases and breast cancer first. And if promising, our methods will be extended to other human diseases. The whole project is expected to last for about three years. We believe that our study will make significant contributions to disease (e.g., breast cancer and brain diseases) risk prediction. From a public health perspective, if we can identify the subgroup of people who have high disease risk, then we can provide appropriate care for them.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9961 | A genome-wide spectrum of tandem repeat expansions in 338,963 humans | Ya Cui (+6) | 2024 | Cell |
Enabling scientific discoveries that improve human health