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KNOWLEDGE DRIVEN BINNING AND PHEWAS ANALYSIS IN MARSHFIELD PERSONALIZED MEDICINE RESEARCH PROJECT USING BIOBIN
Anna O Basile1, John R Wallace, Peggy Peissig
1Department of Biochemistry, Microbiology and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 19, 2016
Summary
BioBin, a tool for rare variant analysis, was enhanced with statistical tests to create Bin-KAT. This new method demonstrates superior power in identifying gene associations with complex diseases compared to previous approaches.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Next-generation sequencing enables rare variant discovery, but analysis remains challenging.
- Existing methods for rare variant association studies often lack statistical power.
- BioBin utilizes a biologically informed binning strategy for variant aggregation.
Purpose of the Study:
- To enhance the BioBin framework by integrating statistical rare variant analysis methods.
- To introduce a unified tool for collapsing and statistically testing rare variants.
- To evaluate the performance of the enhanced tool, termed Bin-KAT.
Main Methods:
- BioBin was expanded to incorporate statistical tests, including SKAT (Sequence Kernel Association Test).
- Extensive simulation studies were conducted on gene-coding regions to compare Bin-KAT with BioBin-regression.
- Variant weighting methods, such as Madsen-Browning, were evaluated.
Main Results:
- Bin-KAT demonstrated greater statistical power than BioBin-regression across all simulated conditions.
- Bin-KAT outperformed traditional burden tests, even in scenarios where variants influence the phenotype in the same direction.
- Application to pharmacogenes and electronic health record data identified associations with complex phenotypes.
Conclusions:
- Bin-KAT offers a powerful, unified approach for rare variant analysis.
- The tool effectively increases statistical power for detecting associations between genes and complex phenotypes.
- Bin-KAT is valuable for identifying genes with low-frequency variants relevant to disease.