Premium
A population genomics approach to uncover the CNVs, and their evolutionary significance, hidden in reduced‐representation sequencing data sets
Author(s) -
Tigano Anna
Publication year - 2020
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.15665
Subject(s) - biology , copy number variation , population genomics , genomics , structural variation , population , genetics , evolutionary biology , adaptation (eye) , genome , computational biology , gene , demography , neuroscience , sociology
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.