Review
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Potential of Genomic Technologies to Improve Disease Resistance in Molluscan Aquaculture
Version 1
: Received: 14 October 2020 / Approved: 15 October 2020 / Online: 15 October 2020 (16:06:27 CEST)
How to cite: Potts, R.; Gutierrez, A.; Penaloza, C.; Regan, T.; Bean, T.; Houston, R. Potential of Genomic Technologies to Improve Disease Resistance in Molluscan Aquaculture. Preprints 2020, 2020100331 Potts, R.; Gutierrez, A.; Penaloza, C.; Regan, T.; Bean, T.; Houston, R. Potential of Genomic Technologies to Improve Disease Resistance in Molluscan Aquaculture. Preprints 2020, 2020100331
Abstract
Molluscan aquaculture is a major contributor to global seafood production, but is hampered by infectious disease outbreaks which can cause serious economic losses. Selective breeding has been widely used to improve disease resistance in major agricultural and aquaculture species, and has clear potential in molluscs, albeit its commercial application remains at a formative stage. Advances in genomic technologies, especially development of cost-efficient genomic selection, have potential to accelerate genetic improvement. However, tailored approaches are required due to the distinctive reproductive and lifecycle characteristics of molluscan species. Transgenesis and genome editing, in particular CRISPR/Cas systems, have been successfully trialled in molluscs, and may further understanding and improvement of genetic resistance to disease through targeted changes to the host genome. Whole organism genome editing is achievable on a much greater scale compared to other farmed species, making genome-wide CRISPR screening approaches plausible. This review discusses the current state and future potential of selective breeding, genomic tools, and genome editing approaches to understand and improve host resistance to infectious disease in molluscs.
Keywords
Mollusc; selective breeding; gene editing; disease resistance; transgenesis; CRISPR/Cas9
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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