NDLI: A Novel Method to Select Informative SNPs and Their Application in Genetic Association Studies

Content Provider	ACM Digital Library
Author	Zhu, Wen Liao, Bo Li, Xiong Cao, Zhi
Abstract	The association studies between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes have recently received great attention. However, these studies are limited by the cost of genotyping all SNPs. Therefore, it is essential to find a small subset of tag SNPs representing the rest of the SNPs. The presence of linkage disequilibrium between tag SNPs and the disease variant (genotyped or not), may allow fine mapping study. In this paper, we combine a nearest-means classifier (NMC) and ant colony algorithm to select tags. Results show that our method (ACO/NMC) can get a similar prediction accuracy with method BPSO/SVM and is better than BPSO/STAMPA for small data sets. For large data sets, although the prediction accuracy of our method is lower than BPSO/SVM, ACO/ NMC can reach a high accuracy ({>}99 percent) in a relatively short time. when the number of tags increases, the time complexity of NMC is nearly linear growth. To find out that the ability of tags to locate disease locus, we simulate a case-control study and use two-locus haplotype analysis to quantitatively assess the power. The result showed that 20 percent of all SNPs selected by NMC have about 10 percent higher power than random tags, on average.
Starting Page	1529
Ending Page	1534
Page Count	6
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2012.70
Volume Number	9
Issue Number	5
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-09-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Diseases,Accuracy,Support vector machines,Bioinformatics,Genomics,Prediction algorithms,tag selection.,Haplotypes,single nucleotide polymorphism,informative SNP
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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