Cen Wu, Shaoyu Li and Yuehua Cui Pages 566 - 573 ( 8 )
The availability of high-density single nucleotide polymorphisms (SNPs) data has made the human genetic association studies possible to identify common and rare variants underlying complex diseases in a genome-wide scale. A handful of novel genetic variants have been identified, which gives much hope and prospects for the future of genetic association studies. In this process, statistical and computational methods play key roles, among which information-based association tests have gained large popularity. This paper is intended to give a comprehensive review of the current literature in genetic association analysis casted in the framework of information theory. We focus our review on the following topics: (1) information theoretic approaches in genetic linkage and association studies; (2) entropy-based strategies for optimal SNP subset selection; and (3) the usage of theoretic information criteria in gene clustering and gene regulatory network construction.
Conditional entropy, Entropy, Gene-centric analysis, Haplotype analysis, Mutual information, Epistasis, Synergistic effect, Single nucleotide polymorphism
Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, A432 Wells Hall, East Lansing, MI 48824.