Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects
Resistance against specific diseases affecting aquaculture species often show moderate to high heritabilities, and there is thus a large potential for genetic improvement. However, genetic evaluation of disease resistance based on survival under challenge testing still face some challenges that may...
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Published in | Aquaculture research Vol. 42; no. s1; pp. 103 - 114 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.02.2011
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Subjects | |
Online Access | Get full text |
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Summary: | Resistance against specific diseases affecting aquaculture species often show moderate to high heritabilities, and there is thus a large potential for genetic improvement. However, genetic evaluation of disease resistance based on survival under challenge testing still face some challenges that may complicate both recording and statistical analysis of disease resistance, e.g., susceptibility and time until death may be different aspects of resistance, or survival may be an inadequate measure of resistance. Hence, for some diseases, more advanced statistical modelling and/or supplementing indicators besides survival would be an advantage. Furthermore, tested individuals are usually excluded as selection candidates as they might be potential disease carriers, and thus pose a health risk. Under classical selection, this restriction reduces both accuracy and intensity of selection, as little or nothing is known about how sib selection candidates deviate from the family means. Thus, selection methods allowing within-family selection are of particular importance in selection for improved disease resistance based on disease challenge testing. Examples of such methods are indirect selection on correlated traits measurable on selection candidates, selection on identified quantitative trait loci and genomic selection. In the future, genomic information has the potential to substantially improve selective breeding for disease resistance traits, given that this information can be acquired on a massive scale and at an affordable cost. |
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Bibliography: | http://dx.doi.org/10.1111/j.1365-2109.2010.02669.x ArticleID:ARE2669 istex:D0CB6BD275F1B47342D322D3BA8E615A662117F3 ark:/67375/WNG-BVCVVGZQ-N ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1355-557X 1365-2109 |
DOI: | 10.1111/j.1365-2109.2010.02669.x |