title: An overview of SNP interactions in genome-wide association studies.
abstract: With the recent explosion in high-throughput genotyping technology, the amount and quality of single-nucleotide polymorphism (SNP) data has increased exponentially. Therefore, the identification of SNP interactions that are associated with common diseases is playing an increasing and important role in interpreting the genetic basis of disease susceptibility and in devising new diagnostic tests and treatments. However, because these data sets are large, although they typically have small sample sizes and low signal-to-noise ratios, there has been no major breakthrough despite many efforts, making this a major focus in the field of bioinformatics. In this article, we review the two main aspects of SNP interaction studies in recent years-the simulation and identification of SNP interactions-and then discuss the principles, efficiency and differences between these methods.
authors.forename: Pei
authors.initials: P
authors.lastname: Li
authors.forename: Maozu
authors.initials: M
authors.lastname: Guo
authors.forename: Chunyu
authors.initials: C
authors.lastname: Wang
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Liu
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
mesh_terms: D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D006801:Humans; D008128:Logic; D058011:Multifactor Dimensionality Reduction; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*
keywords: SNP interactions; data simulation; detection methods; genome-wide association studies