| Title: | Knockoff-Based Statistics for the Identification of Putative Causal Genes in Genetic Studies |
| Lead Institution: | Shanghai Jiao Tong University |
| Principal investigator: |
Application 349052
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
Whole-genome sequencing 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 GWAS and gene-based test studies for various disease-related and survival 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 time-to-event phenotypes. 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. In the KnockoffSurvival approach, we will perform genome-wide association analysis of survival phenotypes (e.g. ischaemic heart disease, breast cancer, glaucoma, Alzheimer's disease, etc.) and to construct knockoff statistics to control false-positive errors and to identify common and rare variants associated with disease onset time and status. We will develop a variety of new methods using genotype, phenotype and EHR data from the UK Biobank. In short, we propose a project for statistical analysis and method development of genome-wide genetic data to elucidate genetic structure and its genetic associations with complex traits and clinical outcomes. We expect the project to last for 36 months in order to gain publications and achievements using UK Biobank data. The duration includes time for processing and preparation of all genotypes and phenotypes data, data application, statistical analysis, manuscript writing and submission for publication.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20051 | Cox-MK: a model-X knockoff framework for genome-wide survival association analysis | Lin Hu (+8) | 2026 | Genetics |
Enabling scientific discoveries that improve human health