| Title: | Development of statistical methods for detection of genetic associations, explanation of underlying biological mechanisms, and integrative analysis of genomics and brain imaging data. |
| Lead Institution: | Southwestern University of Finance and Economics |
| Principal investigator: |
Application 154421
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About
We aim to develop a set of statistical tools that can increase the power of identifying genetic variants associated with diseases and health-related outcomes, uncover the underlying biological mechanisms of SNP-disease relationship, and gain insights into brain image genomics. Many diseases that have a substantial burden on human health are complex traits influenced by a large number of genetic factors. The SNPs identified by genome-wide association studies often can only explain a proportion of the heritability. The first goal of our project is to develop powerful statistical tools to uncover SNPs associated with various complex traits, which will help us have better understandings about the genetic architecture in complex traits. For example, we have developed association tests to discover rare and low-frequency genetic variations that are likely to make considerable contributions to the "missing heritability". After identifying the associated SNPs, it is also of great interest to uncover the biological mechanisms and pathways to explain the relationship between the SNPs and complex traits. Mediation analysis offer a powerful tool to investigate the intermediate mechanism through which a SNP exerts its influence on the trait. Molecular-level variables, such as gene expression or methylation, could mediate the genetic effect on the trait. Our second goal is to develop robust and scalable statistical methods for mediation analysis to improve the understanding of the genetic foundations of disease susceptibility. Brain image genomics has become an emerging research field that can help us gain new insights into the phenotypic characteristics and the genetic and molecular mechanisms of the brain. The large scale and complexity of the brain image data sets pose new computational and statistical challenges for the analysis. The third goal of our project is to develop statistical tools for integrated analysis of brain imaging and genomics data. These tools will enhance our understanding of the neuropsychiatric etiology and neurodevelopment. Our three main goals will broadly help us gain insights into the genetic etiology of complex diseases and brain. This information can assist health professionals in advancing treatment and prevention strategies for various diseases. We expect the scope of the work we have outlined to take around three years.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 19166 | A Powerful Transformation of Quantitative Responses for Biobank-Scale Association Studies | Yaowu Liu (+1) | 2025 | Journal of the American Statistical Association |
Enabling scientific discoveries that improve human health