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Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects

机译:水产养殖物种抗病性遗传评价的方法:挑战和未来前景

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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.
机译:对影响水产养殖物种的特定疾病的抗性通常表现出中等至高的遗传力,因此遗传改良的潜力很大。然而,在挑战性测试下基于存活率的抗病性遗传评估仍然面临一些挑战,这可能会使抗病性的记录和统计分析变得复杂,例如,易感性和直至死亡的时间可能是抗药性的不同方面,否则存活率可能不足抵抗。因此,对于某些疾病,除了生存率以外,更高级的统计模型和/或补充指标将是一个优势。此外,通常将被测个体排除为选择候选者,因为它们可能是潜在的疾病携带者,因此构成健康风险。在经典选择下,这种限制降低了选择的准确性和强度,因为关于同胞选择候选者如何偏离家庭方式的知之甚少。因此,允许基于家庭的选择的选择方法在基于疾病挑战测试的选择中以改善疾病抵抗力特别重要。这样的方法的例子是对可在选择候选者上测量的相关性状进行间接选择,在已鉴定的数量性状基因座上进行选择和基因组选择。将来,基因组信息有可能大大改善针对抗病性状的选择性育种,因为可以以可承受的成本大规模获取该信息。

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