PeerJ (Sep 2021)

Genetic diversity across the mitochondrial genome of eastern oysters (Crassostrea virginica) in the northern Gulf of Mexico

  • Chani R. Rue,
  • Jason D. Selwyn,
  • Patricia M. Cockett,
  • Bryan Gillis,
  • Lauren Gurski,
  • Philip Jose,
  • Brandi L. Kutil,
  • Sharon F. Magnuson,
  • Luz Ángela López de Mesa,
  • R Deborah Overath,
  • Delbert Lee Smee,
  • Christopher E. Bird

DOI
https://doi.org/10.7717/peerj.12205
Journal volume & issue
Vol. 9
p. e12205

Abstract

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The eastern oyster, Crassostrea virginica, is divided into four populations along the western North Atlantic, however, the only published mitochondrial genome sequence was assembled using one individual in Delaware. This study aimed to (1) assemble C. virginica mitochondrial genomes from Texas with pooled restriction-site-associated DNA sequencing (ezRAD), (2) evaluate the validity of the mitochondrial genome assemblies including comparison with Sanger sequencing data, and (3) evaluate genetic differentiation both between the Delaware and Texas genomes, as well as among three bays in Texas. The pooled-genome-assembled-genomes (PAGs) from Texas exhibited several characteristics indicating that they were valid, including elevated nucleotide diversity in non-coding and the third position of codons, placement as the sister haplotype of the genome from Delaware in a phylogenetic reconstruction of Crassostrea mitochondrial genomes, and a lack of genetic structure in the ND4 gene among the three Texas bays as was found with Sanger amplicons in samples from the same bays several years prior. In the comparison between the Delaware and Texas genome, 27 of 38 coding regions exhibited variability between the two populations, which were differentiated by 273 mutations, versus 1–13 mutations among the Texas samples. Using the full PAGs, there was no additional evidence for population structure among the three Texas bays. While population genetics is rapidly moving towards larger high-density datasets, studies of mitochondrial DNA (and genomes) can be particularly useful for comparing historic data prior to the modern era of genomics. As such, being able to reliably compile mitochondrial genomes from genomic data can improve the ability to compare results across studies.

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