Mathematics Home / Research Seminars: Probability and Statistics
Time & Location: All talks are on Wednesdays in Dinwiddie Hall 103 at 2:00 PM unless otherwise noted.
Organizer: Scott McKinley
April 26
Title: STAARpipeline: an all-in-one rare-variant analysis tool for biobank-scale whole-genome sequencing data
Zilin Li | Indiana University, Biostatistics
Abstract: Large-scale whole-genome sequencing (WGS) studies have enabled the analysis of rare variant associations with complex human diseases and traits. Variant set analysis is a powerful approach to studying rare variant associations. However, existing methods have limited ability to define the variant set in the genome, especially for the noncoding genome. We propose a computationally efficient and robust rare variant association-detection framework, STAARpipeline, to automatically annotate a WGS study and perform flexible rare variant association analysis, including gene-centric analysis and fixed-window and dynamic-window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline groups coding and noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, in addition to fixed-size sliding window analysis, STAARpipeline provides a data-adaptive-size dynamic window analysis. All these variant sets could be automatically defined and selected in STAARpipeline. STAARpipeline also provides analytical follow-up of dissecting association signals independent of known variants via conditional analysis. We applied the STAARpipeline to analyze the total cholesterol in 30,138 samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. All analyses scale well in computation time and memory. We discover several potentially new significant associations with lipids. In summary, STAARpipeline is a powerful and resource-efficient tool for association analysis of biobank-scale WGS studies.
Time: 2:00
Location: Dinwiddie Hall 103
May 3
Last Day Of Class