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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
Published on: August 21, 2016
Detecting genome-wide epistases based on the clustering of relatively frequent items
Minzhu Xie1, Jing Li, Tao Jiang
1Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.
Motivation:
In genome-wide association studies (GWAS), up to millions of single nucleotide polymorphisms (SNPs) are genotyped for thousands of individuals. However, conventional single locus-based approaches are usually unable to detect gene-gene interactions underlying complex diseases. Due to the huge search space for complicated high order interactions, many existing multi-locus approaches are slow and may suffer from low detection power for GWAS.
Results:
In this article, we develop a simple, fast and effective algorithm to detect genome-wide multi-locus epistatic interactions based on the clustering of relatively frequent items. Extensive experiments on simulated data show that our algorithm is fast and more powerful in general than some recently proposed methods. On a real genome-wide case-control dataset for age-related macular degeneration (AMD), the algorithm has identified genotype combinations that are significantly enriched in the cases.
Availability:
http://www.cs.ucr.edu/~minzhux/EDCF.zip
Contact:
minzhux@cs.ucr.edu; jingli@cwru.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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