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A large set of microsatellites for the highly invasive earthworm Amynthas corticis predicted from low coverage genomes

Cunha, Luis, Thornber, Alice, Kille, Peter, Morgan, John and Novo Rodriguez, Marta 2017. A large set of microsatellites for the highly invasive earthworm Amynthas corticis predicted from low coverage genomes. Applied Soil Ecology 119 , pp. 152-155. 10.1016/j.apsoil.2017.05.029

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Abstract

Invasive species can significantly affect local biodiversity and create important challenges for conservation. They usually present an outstanding plasticity that permits the adaptation to the new environments. Understanding their genetic background is fundamental to better comprehend invasion dynamics and elaborate proper management plans as well to infer population and evolutionary patterns. Here, we present a reasonable set of tools for the study of a highly invasive earthworm, the megascolecid Amynthas corticis. We designed in silico a large set of primers targeting microsatellite regions (ca. 9400) from two low coverage genomes presented here. This study provides 154 high quality primer pairs targeting polymorphic repeats conserved in two Amynthas corticis mitochondrial lineages. From this dataset, a set of primer pairs (15) was validated by polymerase chain reaction with 86% consistent amplification, confirming the accuracy of the in silico prediction. Nine of the primer pairs tested were selected for population genetics and presented polymorphism in the studied populations, thus showing promising potential for future studies of this global invasive species. The nuclear markers used in this study appear to recapitulate and complement the mitochondrial relationships found in a previous study. Interestingly, all genotyped individuals showed at least one triploid locus profile among the tested loci, which may be evidence of polyploidy associated to their life history, in particular to asexual reproduction by parthenogenesis.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Publisher: Elsevier
ISSN: 0929-1393
Funders: NERC
Date of First Compliant Deposit: 16 June 2017
Date of Acceptance: 24 May 2017
Last Modified: 01 Aug 2018 12:57
URI: http://orca.cf.ac.uk/id/eprint/101404

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