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首页> 外文期刊>Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation >Linkage Disequilibrium Grouping of Single Nucleotide Polymorphisms (SNPs) Reflecting Haplotype Phylogeny for Efficient Selection of Tag SNPs
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Linkage Disequilibrium Grouping of Single Nucleotide Polymorphisms (SNPs) Reflecting Haplotype Phylogeny for Efficient Selection of Tag SNPs

机译:连锁不平衡分组的单核苷酸多态性(SNPs)反映单体型系统发育的标签SNPs的有效选择。

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Single nucleotide polymorphisms (SNPs) have been proposed to be grouped into haplotype blocks harboring a limited number of haplotypes. Within each block, the portion of haplotypes is expected to be tagged by a selected subset of SNPs; however, none of the proposed selection algorithms have been definitive. To address this issue, we developed a tag SNP selection algorithm based on grouping of SNPs by the linkage disequilibrium (LD) coefficient r 2 and examined five genes in three ethnic populations—the Japanese, African Americans, and Caucasians. Additionally, we investigated ethnic diversity by characterizing 979 SNPs distributed throughout the genome. Our algorithm could spare 60% of SNPs required for genotyping and limit the imprecision in allele-frequency estimation of nontag SNPs to 2% on average. We discovered the presence of a mosaic pattern of LD plots within a conventionally inferred haplotype block. This emerged because multiple groups of SNPs with strong intragroup LD were mingled in their physical positions. The pattern of LD plots showed some similarity, but the details of tag SNPs were not entirely concordant among three populations. Consequently, our algorithm utilizing LD grouping allows selection of a more faithful set of tag SNPs than do previous algorithms utilizing haplotype blocks.
机译:已经提出将单核苷酸多态性(SNP)分组为具有有限数量的单倍型的单倍型嵌段。在每个区块内,单倍型的一部分有望被选定的SNP子集标记;然而,所提出的选择算法均未确定。为了解决此问题,我们开发了一种基于SNP分组的标签SNP选择算法,该算法通过连锁不平衡(LD)系数r 2进行了检查,并检查了三个民族(日本人,非裔美国人和高加索人)中的五个基因。此外,我们通过表征分布在整个基因组中的979个SNP来研究种族多样性。我们的算法可以节省60%的基因分型所需的SNP,并将非标签SNP等位基因频率估计的不精确度平均降低到2%。我们发现在常规推断的单倍型模块内LD图的马赛克图案的存在。之所以出现这种情况,是因为多组具有较强组内LD的SNP混杂在它们的身体位置上。 LD图的模式显示出一些相似性,但是标签SNP的细节在三个人群中并不完全一致。因此,与以前利用单倍型模块的算法相比,我们利用LD分组的算法允许选择更忠实的标签SNP集。

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