| Title: | Detecting pleiotropic effects through integration of omics data |
| Lead Institution: | Yale University |
| Principal investigator: | Dr Andrew DeWan |
Application 32285
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
Using omics data, we will attempt to identify shared genetic variants that play a role in a number of common traits and diseases that have a high public health significance that include: asthma, obesity, type 2 diabetes, blood pressure and blood lipid profiles. We will accomplish these goals by using the UK Biobank data and a second dataset with gene expression and genome sequence data on a small set of subjects. We will use statistical methods to detect shared genetic effects and perform biological validation in order to bring about a better understanding of the role shared genetics plays in complex disease. The co-occurrence of common traits and diseases pose a critical public health challenge; this co-occurrence may be explained, in part, by genetic loci shared between these traits and diseases. Identifying shared genetic loci for pairs of traits/diseases will advance our understanding of the mechanistic links between them. These loci have the potential to serve as targets for a single intervention that simultaneously treats both diseases. We will implement previously developed statistical methods and extend existing methods to analyze imputed and rare genetic variants to identify variants associated with two of the diseases/traits listed in 1a. All methods will be implemented in software developed by one of the investigators on this proposal, Dr. Suzanne Leal, which uses parallel processing to make it feasible to analyze hundreds of thousands of samples efficiently and quickly. We plan to analyze the full cohort of approximately 500,000 subjects with genotype and phenotype data.5 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 10056 | A mediation analysis framework based on variance component to remove genetic confounding effect | Zihan Dong (+2) | 2024 | Journal of Human Genetics |
| 12004 | LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information | Zihan Dong (+4) | 2024 | Briefings in Bioinformatics |
| 7942 | Rare-variant association analysis reveals known and new age-related hearing loss genes | Diana M. Cornejo-Sanchez (+11) | 2023 | European Journal of Human Genetics |
| 11115 | The genetic contribution of the X chromosome in age-related hearing loss | Elnaz Naderi (+6) | 2023 | Frontiers in Genetics |
| 10509 | Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population | Andrew T. DeWan (+8) | 2023 | Frontiers in Genetics |
Enabling scientific discoveries that improve human health