| Title: | Developing statistical methods to improve fine-mapping |
| Lead Institution: | University of Hong Kong |
| Principal investigator: |
Application 140822
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About
Genome-wide association studies (GWAS) have discovered tens of thousands of genetic variants associated with many complex traits and diseases. In the post-GWAS era, extracting causal variants and genes from disease-associated genetic variants, and further understanding the molecular mechanisms of complex traits and human diseases, is an important part of current research in genomics. Also, owing to the development of single-cell genomic technologies, it becomes possible to combine the single-cell data with GWAS results to identify loci or genes contributing to the diseases at different cell types and states. We aim to improve statistical fine-mapping methods to identify causal genetic variants associated with complex traits and diseases more accurately and efficiently. In particular, our proposed novel fine-mapping methods should enable the cross-population fine-mapping, and allow the interpretation of causal variants at sing-cell resolution. The project will last for three years. The statistical methods and software will provide solid and efficient tools for the scientific community to conduct fine-mapping analysis. Our methods will offer insights for understanding disease etiology and further to enable development of new therapeutics.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12944 | TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference | Anqi Wang (+2) | 2024 | Bioinformatics |
Enabling scientific discoveries that improve human health