The importance of structural variation in adaptation and speciation is becoming increasingly evident in the literature. Among SVs, copy number variants (CNVs) are known to affect phenotypes through changes in gene expression and can potentially reduce recombination between alleles with different copy numbers. However, little is known about their abundance, distribution and frequency in natural populations. In a “From the Cover” article in this issue of Molecular Ecology, Dorant et al. (2020) present a new cost‐effective approach to genotype copy number variants (CNVs) from large reduced‐representation sequencing (RRS) data sets in nonmodel organisms, and thus to analyse sequence and structural variation jointly. They show that in American lobsters (Homarus americanus), CNVs exhibit strong population structure and several significant associations with annual variance in sea surface temperature, while SNPs fail to uncover any population structure or genotype–environment associations. Their results clearly illustrate that structural variants like CNVs can potentially store important information on differentiation and adaptive differences that cannot be retrieved from the analysis of sequence variation alone. To better understand the factors affecting the evolution of CNVs and their role in adaptation and speciation, we need to compare and synthesize data from a wide variety of species with different demographic histories and genome structure. The approach developed by Dorant et al. (2020) now allows to gain crucial knowledge on CNVs in a cost‐effective way, even in species with limited genomic resources.

中文翻译：

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人口基因组学方法揭示隐藏在简化表示测序数据集中的CNV及其进化意义

在适应和物种形成中结构变异的重要性在文献中变得越来越明显。在SV中，已知拷贝数变异（CNV）通过基因表达的变化影响表型，并可能减少具有不同拷贝数的等位基因之间的重组。但是，关于它们在自然种群中的丰度，分布和频率知之甚少。在本期《分子生态学》的一篇“从封面开始”文章中，Dorant等人。（2020）提出了一种新的经济有效的方法来从非模型生物中的大型简化代表测序（RRS）数据集获得基因型拷贝数变异（CNV），从而共同分析序列和结构变异。他们指出，美国龙虾（龙虾美洲），CNVs表现出强大的种群结构，并且与海表温度的年变化存在数个显着的联系，而SNP却无法揭示任何种群结构或基因型与环境的联系。他们的结果清楚地表明，结构变异体（例如CNV）可以潜在地存储有关分化和适应性差异的重要信息，而这些信息不能仅从序列变异分析中获得。为了更好地了解影响CNV进化的因素及其在适应和物种形成中的作用，我们需要比较和综合来自具有不同人口统计历史和基因组结构的多种物种的数据。Dorant等人开发的方法。（2020）现在允许以具有成本效益的方式获得有关CNV的关键知识，即使在基因组资源有限的物种中也是如此。