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Detecting differential copy number variation between groups of samples
Genome Research ( IF 7 ) Pub Date : 2018-02-01 , DOI: 10.1101/gr.206938.116
Craig B. Lowe , Nicelio Sanchez-Luege , Timothy R. Howes , Shannon D. Brady , Rhea R. Daugherty , Felicity C. Jones , Michael A. Bell , David M. Kingsley

We present a method to detect copy number variants (CNVs) that are differentially present between two groups of sequenced samples. We use a finite-state transducer where the emitted read depth is conditioned on the mappability and GC-content of all reads that occur at a given base position. In this model, the read depth within a region is a mixture of binomials, which in simulations matches the read depth more closely than the often-used negative binomial distribution. The method analyzes all samples simultaneously, preserving uncertainty as to the breakpoints and magnitude of CNVs present in an individual when it identifies CNVs differentially present between the two groups. We apply this method to identify CNVs that are recurrently associated with postglacial adaptation of marine threespine stickleback (Gasterosteus aculeatus) to freshwater. We identify 6664 regions of the stickleback genome, totaling 1.7 Mbp, which show consistent copy number differences between marine and freshwater populations. These deletions and duplications affect both protein-coding genes and cis-regulatory elements, including a noncoding intronic telencephalon enhancer of DCHS1. The functions of the genes near or included within the 6664 CNVs are enriched for immunity and muscle development, as well as head and limb morphology. Although freshwater stickleback have repeatedly evolved from marine populations, we show that freshwater stickleback also act as reservoirs for ancient ancestral sequences that are highly conserved among distantly related teleosts, but largely missing from marine stickleback due to recent selective sweeps in marine populations.



中文翻译:

检测样本组之间的差异拷贝数差异

我们提出了一种检测两组测序样品之间差异存在的拷贝数变异(CNV)的方法。我们使用一个有限状态传感器,其中发出的读取深度取决于在给定基本位置发生的所有读取的可映射性和GC含量。在此模型中,区域内的读取深度是二项式的混合,在模拟中,它比通常使用的负二项式分布更紧密地匹配读取深度。该方法同时分析所有样本,当识别出两组之间存在的CNV差异时,保留了个体中CNV的断点和大小的不确定性。我们应用这种方法来识别与冰河后海域三脊刺棘回适应性循环相关的CNV(Gasterosteus aculeatus)淡水。我们确定了棘背基因组的6664个区域,总计1.7 Mbp,显示了海洋和淡水种群之间一致的拷贝数差异。这些删除和重复会影响蛋白质编码基因和顺式调控元件,包括DCHS1的非编码内含性脑神经增强子。6664 CNV附近或包含的基因功能丰富了免疫力,肌肉发育以及头和肢体形态。尽管淡水棘背动物已从海洋种群中反复演化,但我们表明,淡水棘背动物还充当远古硬骨鱼类之间高度保存的古代祖先序列的水库,但由于最近对海洋种群的选择性扫描,海洋棘背动物在很大程度上消失了。

更新日期:2018-02-02
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