| Title: | A diseases prediction model based on common variants (polygenic risk score) and rare variants (variant weighted gene burden scores) in whole exome sequencing data |
| Lead Institution: | Guangzhou KingMed Transformative Medicine Institute Co. Ltd. |
| Principal investigator: |
Application 107825
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About
Our research aims to develop an advanced disease prediction model for complex diseases by integrating both common and rare genetic variants identified through whole-exome sequencing (WES) data. One of the primary objectives of our study is to evaluate the heritability of complex diseases using WES data, shedding light on the underlying genetic factors contributing to disease susceptibility. Additionally, we strive to enhance the prediction accuracy of existing models based solely on polygenic risk scores (PRS). By incorporating rare variants into the model, we hope to capture the additional sources of genetic variation that are often overlooked when focusing solely on common variants. Previous large-scale population studies have shown that PRS, which consider the effects of common genetic variants, can effectively identify individuals at high risk for complex diseases. However, it is important to note that the overall prediction accuracy of PRS models varies, with most falling within the AUC range of 0.6-0.75. This discrepancy may arise from the fact that most current PRS methods solely rely on common variants and do not fully consider the impact of rare variants on disease susceptibility. Despite their lower frequency, rare variants can possess a substantial effect size and contribute significantly to the development of common diseases, even in individuals with low PRS. By integrating both common and rare variants into our prediction model, we aim to provide a more comprehensive understanding of genetic predisposition. Our proposed research will leverage the extensive whole-exome sequencing data available from the UK Biobank to identify genetic susceptibility loci associated with complex diseases and develop a prediction model based on WES data. This model will hold great potential to advance our knowledge of genetic risk factors, empowering medical professionals to make informed decisions regarding personalized prevention strategies, clinical interventions, and disease management. By considering both common and rare variants, we hope to gain deeper insights into disease risk prediction and contribute to the broader field of personalized medicine. Ultimately, our research endeavors to reduce disease burden, enhance public health outcomes, and drive evidence-based healthcare interventions tailored to individual genetic profiles. project duration: 36 months.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11715 | Common and rare variants in genetic susceptibility analysis of mature B-cell neoplasm subtypes by whole exome sequencing | Junwei Lin (+5) | 2024 | Leukemia |
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