Post-GWAS analysis through integration of genome functional annotations
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Post-GWAS analysis through integration of genome functional annotations In-Person
Presenters: Qiongshi Lu, Department of Biostatistics in Yale School of Public Health, and Ryan Powles, Center for Statistical Genomics and Proteomics
Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. We present a statistical framework to produce high-resolution, single tissue annotations through integration of diverse epigenomic and transcriptomic data. In addition, we discuss several applications of genome functional annotations in post-GWAS analysis including GWAS signal prioritization, genetic risk prediction, and estimating annotation-stratified genetic correlation.
- Date:
- Wednesday, March 15, 2017
- Time:
- 1:00pm - 2:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Location:
- C-103, SHM, 333 Cedar St, New Haven, 06510
- Campus:
- Medical School
- Categories:
- Bioinformatics
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