| Title: | Whole-genome sequencing studies for disease-related and imaging phenotypes using UKBB data. |
| Lead Institution: | Shanghai Jiao Tong University |
| Principal investigator: |
Application 100014
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About
Whole-genome sequencing (WGS) studies including gene-based tests and single variant tests (GWAS) have identified numerous genes and variants associated with a variety of human diseases and traits. We perform large collaborative WGS studies for various disease-related and imaging phenotypes, from multiple binary (e.g. hypertension, coronary artery disease, asthma, type 2 diabetes etc.) and quantitative traits (e.g. BMI, platelet count, cholesterol, cystatin c etc.) to phenotypes or metrics associated with imaging (e.g. diffusion MRI, MRI markers of cerebral small vessel disease etc.). We are also interested in the genetic bases of complex traits and identification of rare disease variants in non-coding area or long-range regulatory elements. Depending on the specific disease-related phenotype, we would like to use UKBB dataset to either expand our discovery studies and/or to replicate our new GWAS findings in the UKBB dataset. Moreover, we are developing computationally efficient gene-based testing approach for the biobank-scale data and perform conditional testing to prioritize causal genes and variants over proxy associations genome-wide. We will develop a variety of new methods using the UK biobank genotype, phenotype and imaging data. We plan to apply whole-genome sequencing analysis and phenome-wide approach based on UK biobank diagnostic phenotypes and magnetic resonance imaging (MRI) data. In summary, we propose a project to conduct statistical analyses and method developments for genome-wide genetic data, in order to elucidate the genetic architecture and its genetic links to complex traits and clinical outcomes. We request to the duration of our project for 36 months to get publications and accomplishments using the UK Biobank data. The duration includes the time for processing and preparing all imputation genotype, phenotypes and imaging, data applications, statistical analyses, manuscripts writing and submission for publication.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8702 | Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference | Jianle Sun (+7) | 2024 | Human Genetics |
Enabling scientific discoveries that improve human health