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Analysis pipeline for the epistasis search - statistical versus biological filtering
Xiangqing Sun1, Qing Lu2, Shubhabrata Mukherjee3
1Department of Epidemiology and Biostatistics, Case Western Reserve University Cleveland, OH, USA.
Frontiers in Genetics
|May 13, 2014
Summary
Gene-gene interactions are crucial for complex traits, but often overlooked. Filtering methods can speed up the detection of these epistasis effects, improving genetic association studies.
Area of Science:
- Genetics
- Bioinformatics
Background:
- Gene-gene interactions (epistasis) contribute to complex traits and may explain missing heritability.
- Genome-wide detection of epistasis is computationally intensive.
Purpose of the Study:
- To summarize existing filtering approaches for detecting epistasis.
- To differentiate filtering strategies based on the goals of epistasis detection.
Main Methods:
- Statistical filtering: Quality control using single marker statistics to refine the search space.
- Biological filtering: Targeting pathways, integrating databases (pathways, gene ontology, protein-protein interactions), and functional single-nucleotide polymorphisms.
Main Results:
- Filtering pipelines enhance computational speed by reducing the number of tests.
- Statistical and biological filtering can improve the power of interaction association studies.
Conclusions:
- Filtering strategies are essential for efficient genome-wide epistasis detection.
- While improving power, filtering may increase the risk of missing significant findings.
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