htSNP finder - feature selection

A major challenge for genome-wide disease association studies is the high cost of genotyping large number of single nucleotide polymorphisms (SNP). The correlations between SNPs, however, make it possible to select a parsimonious set of informative SNPs, known as “tagging” SNPs, able to capture most variation in a population. Considerable research interest has recently focused on the development of methods for finding such SNPs. In this paper we present an efficient method for finding tagging SNPs. The method does not involve computation-intensive search for SNP subsets but discards redundant SNPs using a feature selection algorithm. In contrast to most existing methods, the method presented here does not limit itself to using only correlations between SNPs in local groups. By using correlations that occur across different chromosomal regions, the method can reduce the number of globally redundant SNPs. Experimental results show that the number of tagging SNPs selected by our method is smaller than by using block-based approaches. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1246#pack_1909">FsSnp </a> : A fast method for selecting haplotype tagging SNPs (htSNPs) using feature selection. </li> </ul>

基因组广域疾病关联研究面临的一项重大挑战是大量单核苷酸多态性（SNP）分型的成本高昂。然而，SNP之间的相关性使得选择一组简约的信息性SNP成为可能，此类SNP被称为“标记”SNP，能够捕捉到人群中大部分的变异。近期，大量研究兴趣集中在开发寻找此类SNP的方法。在本研究中，我们提出了一种寻找标记SNP的高效方法。该方法不涉及对SNP子集进行计算密集型搜索，而是通过特征选择算法丢弃冗余SNP。与现有的大多数方法不同，本方法不仅限于使用局部组内SNP之间的相关性。通过利用不同染色体区域发生的相关性，该方法能够减少全局冗余SNP的数量。实验结果表明，由我们方法选择的标记SNP数量小于使用基于块的近似方法。本项目包含以下软件/数据包：

<ul>
<li><a href="https://simtk.org/frs?group_id=1246#pack_1909">FsSnp</a>：一种使用特征选择快速选择单倍型标记SNP（htSNPs）的方法。</li>
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