| Title: | Statistical Method Development for Phenome-Wide Association Studies |
| Lead Institution: | Michigan Technological University |
| Principal investigator: | Professor Qiuying Sha |
Application 41722
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About
In this project, we will develop new statistical methods to find genes responsible for complex human diseases and apply these methods to Biobank data sets. Over the last decade, genome-wide association studies (GWAS) have been widely performed to identify genetic associations for many complex diseases. A common limitation of GWAS is that they focus on only a single disease. Emerging evidence has shown that pleiotropy, the phenomenon of one genetic variant affects multiple diseases, is widespread in complex human diseases. Therefore, joint analysis of multiple diseases within a cohort presents an attractive alternative to single disease analyses and may provide new insights into the etiology of human diseases. Therefore, in this project, we plan to develop novel statistical methods to test the association between a genetic variant with a large number of diseases jointly. We plan to work on this project in the coming three years. We will publish the statistical methods, computational tools and biological findings from the data analysis and make the research results publicly available. Discovering novel genetic variants contributing to complex diseases may aid in improving diagnosis, treatment, and prevention, and have significant public health benefits.6 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7925 | A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics | Meida Wang (+3) | 2023 | Scientific Reports |
| 8019 | Gene selection by incorporating genetic networks into case-control association studies | Xuewei Cao (+3) | 2022 | European Journal of Human Genetics |
| 7105 | Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data | Shijia Yan (+2) | 2022 | Genes |
| 8077 | HCLC-FC: A novel statistical method for phenome-wide association studies | Xiaoyu Liang (+3) | 2022 | PLOS ONE |
| 11002 | Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies | Hongjing Xie (+3) | 2023 | Genetic Epidemiology |
| 8276 | Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network | Hongjing Xie (+3) | 2023 | Bioinformatics |
Enabling scientific discoveries that improve human health