Novel approaches for detecting and analyzing stuctural variants in personal genomes

基因组结构变异（SVs），如拷贝数变异或大平衡反转，代表了一种主要的遗传变异形式，其对人类基因组的影响类似于单核苷酸多态性（snp）。然而，与snp相比，我们对SVs的了解有限。迄今为止，已公布的调查的分辨率不足以绘制sv的起点和终点（即断点），从而妨碍了sv的详细分析。在此，我们提出以前所未有的细节研究SVs：我们将开发计算方法来识别SVs并分析其在基因组中的范围；[2]矿山公布的数据集，揭示SV从头形成过程；和[3]应用了测量SV新生形成速率和推断自然选择影响的方法。具体地说，首先我们将系统地收集关于具有已知断点的sv的信息，并在库中标准化它们的描述。针对文库扫描短DNA测序读数将能够准确检测SV，从而为目前缺乏详细SV分析的“个人基因组”增加重要价值。其次，我们将通过设计计算方法来推断每个SV位点可能的祖先状态（通过与灵长类基因组的比较）和可能的因果突变机制（通过断点分析），开发一个解开SV形成的框架。第三，我们将通过实验估计SV的形成率和计算SV的发生频率，以评估影响SV在基因组中丰度和分布的因素。

Genomic structural variants (SVs), such as copy-number variants or large balanced inversions, represent a major form of genetic variation with an impact on the human genome that is similar to single nucleotide polymorphisms (SNPs). However, compared to SNPs, our understanding of SVs is limited. The resolution of published surveys has thus far been insufficient for mapping the start- and end-points (i.e., breakpoints) of SVs, hampering detailed analyses of SVs. Here we propose to study SVs in unprecedented detail: we will [1] develop computational approaches to identify SVs and analyze their extent in the genome; [2] mine published datasets to unravel SV de novo formation processes; and [3] apply approaches to measure SV de novo formation rates and to infer the influence of natural selection. Specifically, first we will systematically collect information on SVs with known breakpoints and standardize their description in a library. Scanning short DNA sequencing reads against the library will enable accurate SV-detection and thus add significant value to “personal genomes” that presently lack detailed SV analyses. Second, we will develop a framework for untangling SV formation by devising computational approaches for inferring the likely ancestral state (by comparison with primate genomes) and the likely causal mutational mechanism (by breakpoint analysis) at each SV locus. Third, we will estimate SV formation rates experimentally and SVoccurrence frequencies computationally to assess the factors contributing to the abundance and distribution of SVs in the genome.