| Title: | Powerful and novel statistical methods for integrative genetic association and causal analyses |
| Lead Institution: | University of Minnesota Twin Cities |
| Principal investigator: | Professor Wei Pan |
Application 35107
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About
In spite of many successful discoveries of novel genetic associations with complex traits and common diseases, genome-wide association studies (GWAS) face two major challenges. The first is its limited statistical power even with tens to hundreds of thousands of individuals in a typical GWAS, thus missing many associated genetic variants due to small effect sizes of many genetic variants. The second is that even for those few identified genetic variants, since they often do not reside in a gene's coding region, it is difficult to interpret their function and thus biological mechanisms, hindering mechanistic understanding and thus possible therapeutic and preventive development for a disease. We propose addressing these two questions through developing and applying new and more powerful and robust analysis methods, which are to be applied to the UKBB data for both testing these methods and for new discovery, especially for Alzheimer's disease, cardiovascular traits and cancer. This is planned to be a three-year project.5 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7901 | Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data | Haoran Xue (+2) | 2023 | Journal of the American Statistical Association |
| 8000 | DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. | Ruoyu He (+5) | 2023 | Biostatistics |
| 8143 | MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits. | Katherine A Knutson (+1) | 2022 | Human Molecular Genetics |
| 8226 | Statistical inference with large-scale trait imputation | Jingchen Ren (+1) | 2023 | Statistics in Medicine |
| 7874 | Using GWAS summary data to impute traits for genotyped individuals | Jingchen Ren (+4) | 2023 | Human Genetics and Genomics Advances |
Enabling scientific discoveries that improve human health