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一种群体基因组学方法,用于揭示隐藏在简化代表性测序数据集中的拷贝数变异(CNV)及其进化意义。

A population genomics approach to uncover the CNVs, and their evolutionary significance, hidden in reduced-representation sequencing data sets.

作者信息

Tigano Anna

机构信息

Department of Molecular, Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA.

Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH, USA.

出版信息

Mol Ecol. 2020 Dec;29(24):4749-4753. doi: 10.1111/mec.15665. Epub 2020 Oct 14.

Abstract

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.

摘要

结构变异在适应和物种形成中的重要性在文献中越来越明显。在结构变异中,已知拷贝数变异(CNV)通过基因表达的变化影响表型,并可能减少不同拷贝数等位基因之间的重组。然而,对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“封面文章”中,多兰特等人(2020年)提出了一种新的经济高效的方法,用于从非模式生物的大型简化代表性测序(RRS)数据集中对拷贝数变异(CNV)进行基因分型,从而联合分析序列和结构变异。他们表明,在美国龙虾(美洲螯龙虾)中,CNV表现出强烈的种群结构以及与海面温度年变化的若干显著关联,而单核苷酸多态性(SNP)未能揭示任何种群结构或基因型 - 环境关联。他们的结果清楚地表明,像CNV这样的结构变异可能存储有关分化和适应性差异的重要信息,而这些信息无法仅从序列变异分析中获取。为了更好地理解影响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种群历史和基因组结构的各种物种的数据。多兰特等人(2020年)开发的方法现在能够以经济高效的方式获得关于CNV的关键知识,即使是在基因组资源有限的物种中。

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