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A double classification tree search algorithm for index SNP selection

Peisen Zhang1*, Huitao Sheng2 and Ryuhei Uehara3

Author Affiliations

1 Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA

2 Columbia Genome Center, Columbia University, New York, NY 10032, USA

3 Natural Science Faculty, Komazawa University, Setagaya, Tokyo, Japan

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BMC Bioinformatics 2004, 5:89  doi:10.1186/1471-2105-5-89

Published: 6 July 2004



In population-based studies, it is generally recognized that single nucleotide polymorphism (SNP) markers are not independent. Rather, they are carried by haplotypes, groups of SNPs that tend to be coinherited. It is thus possible to choose a much smaller number of SNPs to use as indices for identifying haplotypes or haplotype blocks in genetic association studies. We refer to these characteristic SNPs as index SNPs. In order to reduce costs and work, a minimum number of index SNPs that can distinguish all SNP and haplotype patterns should be chosen. Unfortunately, this is an NP-complete problem, requiring brute force algorithms that are not feasible for large data sets.


We have developed a double classification tree search algorithm to generate index SNPs that can distinguish all SNP and haplotype patterns. This algorithm runs very rapidly and generates very good, though not necessarily minimum, sets of index SNPs, as is to be expected for such NP-complete problems.


A new algorithm for index SNP selection has been developed. A webserver for index SNP selection is available at webcite