| Title: | Scalable and Robust methods for biobank data analysis |
| Lead Institution: | University of Michigan |
| Principal investigator: | Dr Seunggeun Lee |
Application 45227
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About
Large biobanks such as UK-Biobank are important resources for health research. The detailed genetic information coupled with clinical, behavior and environmental measurements provide a great opportunity to discover new genetic variants associated with diseases, such as heart disease, cancer, and type 2 diabetes. In addition, the data will allow identifying genetic variants whose effects are modified by environmental exposures. These discoveries will enable more precise prediction of individual-level disease risk and help optimize disease screening, prevention, and treatment. However, the large size and complex structure of the Biobank data is a major problem for identifying genetic associations and gene-environmental interactions. This application proposes a methodological and computational development which is an important step towards obtaining the rich information of the biobank data. The developed method will be applied to UK-Biobank data to identify novel genetic variants associated with clinical phenotypes, which will contribute to the prevention and treatment of complex genetic diseases.7 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 10543 | Genome-wide study on 72,298 individuals in Korean biobank data for 76 traits | Kisung Nam (+2) | 2022 | Cell Genomics |
| 8159 | Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies | Yongwen Zhuang (+7) | 2022 | Bioinformatics |
| 8763 | Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction | Yongwen Zhuang (+4) | 2024 | BMC Bioinformatics |
| 8174 | Integrating external controls in case-control studies improves power for rare-variant tests | Yatong Li (+1) | 2022 | Genetic Epidemiology |
| 7973 | Investigation of genetic variants and causal biomarkers associated with brain aging | Jangho Kim (+3) | 2023 | Scientific Reports |
| 10472 | SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests | Wei Zhou (+8) | 2022 | Nature Genetics |
| 10569 | The construction of cross-population polygenic risk scores using transfer learning | Zhangchen Zhao (+4) | 2022 | American Journal of Human Genetics |
Enabling scientific discoveries that improve human health