全基因组兼容SNP间隔及其性质。

Jeremy Wang, Fernando Pardo-Manual de Villena, Kyle J Moore, Wei Wang, Qi Zhang, Leonard McMillan
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引用次数: 19

摘要

种内基因组可以被细分为具有有限多样性的块。了解这些片段的分布和结构将有助于解开许多生物学问题,包括识别与复杂疾病相关的基因,寻找特定人群的祖先起源,以及定位历史重组、基因转换和同源性的区域。我们提出了将基因组划分为没有明显重组的块的方法,从而提供了基于四配子测试的简约的相容基因组间隔集。我们的贡献是对将基因组划分为兼容区间的问题进行了彻底的分析,就其计算复杂性而言,并提供了覆盖整个数据集所需的最小区间数量的可实现的下限。一般来说,这样的最小间隔分区不是唯一的。然而,我们确定了每个可能的解决方案的共同属性。我们还定义了区间集的概念,它既达到区间下界,又使区间重叠最大化。我们演示了使用标准四配子测试的扩展来划分近交小鼠的单倍型数据以及近交杂合基因型数据的算法。这些方法允许我们的算法应用于广泛的基因组数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Genome-wide compatible SNP intervals and their properties.

Genome-wide compatible SNP intervals and their properties.

Genome-wide compatible SNP intervals and their properties.

Genome-wide compatible SNP intervals and their properties.

Intraspecific genomes can be subdivided into blocks with limited diversity. Understanding the distribution and structure of these blocks will help to unravel many biological problems including the identification of genes associated with complex diseases, finding the ancestral origins of a given population, and localizing regions of historical recombination, gene conversion, and homoplasy. We present methods for partitioning a genome into blocks for which there are no apparent recombinations, thus providing parsimonious sets of compatible genome intervals based on the four-gamete test. Our contribution is a thorough analysis of the problem of dividing a genome into compatible intervals, in terms of its computational complexity, and by providing an achievable lower-bound on the minimal number of intervals required to cover an entire data set. In general, such minimal interval partitions are not unique. However, we identify properties that are common to every possible solution. We also define the notion of an interval set that achieves the interval lower-bound, yet maximizes interval overlap. We demonstrate algorithms for partitioning both haplotype data from inbred mice as well as outbred heterozygous genotype data using extensions of the standard four-gamete test. These methods allow our algorithms to be applied to a wide range of genomic data sets.

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