| Title: | Identifying genetic and environmental factors underpinning complex trait variation in humans |
| Lead Institution: | Westlake University |
| Principal investigator: | Professor Jian Yang |
Application 66982
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About
Most human traits (including diseases) are influenced by many genetic and environmental factors. Population-based genetic studies have been proven valuable for identifying genetic factors responsible for the traits and for predicting an individual's risk of developing a disease. However, a widening knowledge gap exists between cutting-edge genetics research and current clinical practices. Besides the genetic factors, an increasing number of lifestyle and environmental factors that can trigger and exacerbate a condition have been documented. Thus, it is essential to collect genetic data and health records from a very large cohort (e.g., hundreds of thousands to millions of individuals) to identify the genetic and environmental factors and to yield clinically actionable disease predictors. With the access to the UK Biobank data, we aim 1) to quantify the overall contribution of all the genetic factors to each of the traits; 2) to characterize individual genetic factors associated with the traits and to understand the biology underpinning the associations; 3) to identify environmental/lifestyle factors that are causally linked to diseases and to predict disease risks of currently healthy individuals by integrating both genetic and environmental/lifestyle factors. The duration of this project will be three years, from 2020.11 to 2023.11. The findings of this project are crucial to deepen the understanding of the disease etiology and prevention, are of great importance for public health, and are also in concordance with the UK Biobank's stated purpose. The combination of new data and new methods will take us into an era of personalized and precision treatments based on an individual's gene, environment, and lifestyle, which has the potential to have a significant impact on health care.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11570 | Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes | Yang Wu (+5) | 2023 | Cell Genomics |
| 10137 | Unravelling the complex causal effects of substance use behaviours on common diseases | Angli Xue (+6) | 2024 | Communications Medicine |
Enabling scientific discoveries that improve human health