Journal
BRIEFINGS IN FUNCTIONAL GENOMICS
Volume 14, Issue 2, Pages 143-155Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bfgp/elu036
Keywords
genome-wide association studies; SNP interactions; data simulation; detection methods
Funding
- Natural Science Foundation of China [61370010, 61271346, 61172098, 91335112]
- Natural Science Foundation of Fujian Province of China [2014J01253]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20112302110040]
- Fundamental Research Funds for the Central Universities [HIT.KISTP.201418]
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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.
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