| Abstract: | Thousands of genome-wide association studies (GWAS) have been conducted over the past decade to identify the genetic risk variants underlying such complex phenotypes as human height, diabetes, and psychiatric disorders. Most existing methods for GWAS data analysis require individual-level genotype data as their input. However, it is often not easy to get access to individual-level data, due to many practical issues, such as privacy protection and disagreement on data-sharing among multiple research groups. In this talk, we introduce SSE, a Summary-Statistics-based approach to exploring the genetic architecture of human complex phenotypes, i.e., estimating heritability, co-heritability and effect sizes in GWAS data analysis. This is achieved by Bayesian analysis with the standard random-effect prior and a summary-statistics-based likelihood function. We have implemented a parallel Gibbs sampling strategy, which allows us to handle genome-wide-scale datasets. Our analysis results suggest that summary-statistics-based analysis can achieve comparable performance to individual-level data analysis, greatly facilitate big genomic data analysis in practice. |
| Date: | 23 September 2016 |
| Time: | 11:00am - 12:15pm |
| Speaker: |
Prof Can YANG Hong Kong Baptist University |
| Venue: | Room 7-207, 7/F, Academic 3 |
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