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Approximation Algorithms for the Selection of Robust Tag SNPs

机译:鲁棒标签单核苷酸多态性选择的近似算法

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摘要

Recent studies have shown that the chromosomal recombination only takes places at some narrow hotspots. Within the chromosomal region between these hotspots (called haplotype block), little or even no recombination occurs, and a small subset of SNPs (called tag SNPs) is sufficient to capture the haplotype pattern of the block. In reality, the tag SNPs may be genotyped as missing data, and we may fail to distinguish two distinct haplotypes due to the ambiguity caused by missing data. In this paper, we formulate this problem as finding a set of SNPs (called robust tag SNPs) which is able to tolerate missing data. To find robust tag SNPs, we propose two greedy and one LP-relaxation algorithms which give solutions of (m + 1) ln (K(K-1))/2, ln((m + 1) (K(K-1))/2), and O(m ln K) approximation respectively, where m is the number of SNPs allowed for missing data and K is the number of patterns in the block.
机译:最近的研究表明,染色体重组仅发生在一些狭窄的热点上。在这些热点之间的染色体区域内(称为单倍型模块),很少或什至没有重组发生,一小部分SNP(称为标签SNP)足以捕获该模块的单倍型模式。实际上,标签SNP可能会被基因型识别为缺失数据,并且由于缺失数据会导致模棱两可,因此我们可能无法区分两种不同的单倍型。在本文中,我们将此问题表述为找到一组能够容忍丢失数据的SNP(称为鲁棒标签SNP)。为了找到鲁棒的标签SNP,我们提出了两种贪心和一种LP松弛算法,它们给出了(m +1)ln(K(K-1))/ 2,ln((m +1)(K(K-1 ))/ 2)和O(m ln K)近似值,其中m是允许丢失数据的SNP数量,K是块中模式的数量。

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