Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis
Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis
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DOI：
10.1371/journal.pone.0040224
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发表时间：
2012-07-09
期刊：
影响因子：
3.7
通讯作者：
Amos, Christopher I.
中科院分区：
文献类型：
--
作者：
Ma, Jianzhong;Amos, Christopher I.
尽管在过去的几年里，随着双端测序方法的出现，在映射倒位方面取得了重大进展，但我们对人类基因组中倒位的流行和谱的理解落后于其他类型的结构变异，这主要是由于缺乏适用于大规模样本的具有成本效益的方法。我们提出了一种新的方法，基于主成分分析（PCA）的特点，使用高密度SNP基因型数据的倒位多态性。我们的方法适用于非复发性倒位，其中倒位杂合子中的倒位和非倒位片段之间的重组由于不平衡配子的丢失而被抑制。在这样的倒位区域内，产生了类似于群体亚结构的效应：两个不同取向的倒位纯合子和它们的1：1混合物的不同“群体”，即倒位杂合子。这种子结构可以很容易地通过在反转区域中局部执行PCA来检测。使用模拟，我们证明了所提出的方法可以用来检测和基因型反转多态性使用非定相基因型数据。我们将我们的方法应用于III期HapMap数据，并推断已知倒位多态性在8p23.1和17q21.31的倒位基因型。这些倒位基因型进行了验证，通过与文献结果进行比较，并检查孟德尔的一致性，使用家庭数据时可用。基于PCA方法，我们还使用HapMap数据对倒位进行了初步的全基因组扫描，得到了2040个候选倒位，其中169个与先前报道的倒位重叠。我们的方法可以很容易地应用于丰富的SNP数据，并有望在开发人类基因组倒位图谱和探索倒位与疾病易感性之间的关联方面发挥重要作用。
Despite the significant advances made over the last few years in mapping inversions with the advent of paired-end sequencing approaches, our understanding of the prevalence and spectrum of inversions in the human genome has lagged behind other types of structural variants, mainly due to the lack of a cost-efficient method applicable to large-scale samples. We propose a novel method based on principal components analysis (PCA) to characterize inversion polymorphisms using high-density SNP genotype data. Our method applies to non-recurrent inversions for which recombination between the inverted and non-inverted segments in inversion heterozygotes is suppressed due to the loss of unbalanced gametes. Inside such an inversion region, an effect similar to population substructure is thus created: two distinct "populations" of inversion homozygotes of different orientations and their 1:1 admixture, namely the inversion heterozygotes. This kind of substructure can be readily detected by performing PCA locally in the inversion regions. Using simulations, we demonstrated that the proposed method can be used to detect and genotype inversion polymorphisms using unphased genotype data. We applied our method to the phase III HapMap data and inferred the inversion genotypes of known inversion polymorphisms at 8p23.1 and 17q21.31. These inversion genotypes were validated by comparing with literature results and by checking Mendelian consistency using the family data whenever available. Based on the PCA-approach, we also performed a preliminary genome-wide scan for inversions using the HapMap data, which resulted in 2040 candidate inversions, 169 of which overlapped with previously reported inversions. Our method can be readily applied to the abundant SNP data, and is expected to play an important role in developing human genome maps of inversions and exploring associations between inversions and susceptibility of diseases.