| Title: | Understanding the genetic regulatory mechanism underpinning complex trait variations in humans |
| Lead Institution: | West China Hospital of Sichuan University |
| Principal investigator: |
Application 151441
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
Human common diseases, are affected by many genetic and environmental factors. GWAS have been instrumental in pinpointing genetic variations that cause phenotypic variations in complex traits and can be useful for predicting disease risk for individuals. Apart from the genetic variations, molecular phenotypes (e.g., DNA methylations) and environmental factors that are thought to play an important role in developing a disease. To fully understand these genetic, molecular and environmental contributions, it's crucial to analyse data from a large cohort with various of genotypic and phenotypic data measurement available. This data can then be used to develop actionable medical insights. With advancement in sequencing technologies and collaborative research, the UK Biobank has provided a comprehensive database of both genetic and non-genetic information. In this project, we aim to understand the relationship between causal variants and GWAS hits in diverse population and detect the plausible causal variants and molecular phenotypes underlying these GWAS associations. We will develop novel statistical models to analyze genomic data for ~500,000 individuals with ~3,000 traits included in the UK Biobank. We will develop novel fine-mapping method to improve the performance on the identification of true causal variants and apply it to the ~3000 traits. Moreover, we will identify variants with pleiotropic effect between complex traits and heterogeneity effect between gender and ancestry groups to reveal the underlying shared or independent genetic regulatory mechanisms. Furthermore, we will identify environmental factors and molecular phenotypes causally associated with a trait and investigate the use of these factors for phenotype prediction. This is crucial to the understanding of the disease etiology and prevention, of great importance for public health, and also in concordance with the UKB's stated purpose. The combination of new data and new methods will take us into an era of personalized and precision treatments based on individual's gene, environment, and lifestyle, which has the potential to have a significant impact on health care.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17686 | Multi-ancestry GWAS of age-related hearing loss identifies 140 loci and key cellular mechanisms | Lulu Shi (+7) | 2026 | Nature Communications |
Enabling scientific discoveries that improve human health