Tambaqui or cachama ( Colossoma macropomum ) is one of the most important neotropical freshwater fish used for aquaculture in South America, and its production is concentrated at low latitudes (close to the Equator, 0°), where the water temperature is warm. Therefore, understanding how selection shapes genetic variations and structure in farmed populations is of paramount importance in evolutionary biology. High‐throughput sequencing to generate genome‐wide data for fish species allows for elucidating the genomic basis of adaptation to local or farmed conditions and uncovering genes that control the phenotypes of interest. The present study aimed to detect genomic selection signatures and analyze the genetic variability in farmed populations of tambaqui in South America using single‐nucleotide polymorphism (SNP) markers obtained with double‐digest restriction site‐associated DNA sequencing. Initially, 199 samples of tambaqui farmed populations from different locations (located in Brazil, Colombia, and Peru), a wild population (Amazon River, Brazil), and the base population of a breeding program (Aquaculture Center, CAUNESP, Jaboticabal, SP, Brazil) were genotyped. Observed and expected heterozygosity was 0.231–0.350 and 0.288–0.360, respectively. Significant genetic differentiation was observed using global F ST analyses of SNP loci ( F ST = 0.064, p < 0.050). Farmed populations from Colombia and Peru that differentiated from the Brazilian populations formed distinct groups. Several regions, particularly those harboring the genes of significance to aquaculture, were identified to be under positive selection, suggesting local adaptation to stress under different farming conditions and management practices. Studies aimed at improving the knowledge of genomics of tambaqui farmed populations are essential for aquaculture to gain deeper insights into the evolutionary history of these fish and provide resources for the establishment of breeding programs.
The gray brocket deer, Mazama gouazoubiraG. Fischer, 1814, occurs in South America and presents an extensive degree of morphological and genetic variability. Previous phylogenetic research showed that the genus Mazama is polyphyletic and imposed the designation of a different genus-group name for M. gouazoubira. We aimed to review and clarify the taxonomy of M. gouazoubira through the proposal of updating the nomenclature for this taxon and by the characterization of specimens collected close to the original type locality (topotypes). The topotypes were characterized by morphological (general characterization and morphometry), cytogenetic (conventional staining, Ag-NOR, G- and C-banding, and fluorescence in situ hybridization), and phylogenetic (mitogenomes) approaches. We revealed chromosome homologies between cattle and M. gouazoubira using an entire set of cattle whole chromosome painting probes and propose an updated G-band idiogram for the species. The morphometric analysis did not discriminate the individuals of M. gouazoubira, including the topotypes, from other small brocket deer species. However, the phylogenetic analysis, based on a Bayesian inference tree of the mitogenomes, confirmed the polyphyly of the genus Mazama and supported the need to change the gray brocket deer genus-group name. Based on our revision, we validated the genus SubuloSmith, 1827, and fixed a type species for the genus. In the absence of the holotype, we denominated a neotype described by the collection of a male topotype in Paraguay. The nomenclature rearrangement presented here is a starting point that will assist in the taxonomic resolution of Neotropical deer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.