The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon
The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon
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DOI：
10.1101/2021.09.14.460263
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发表时间：
2021-09
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影响因子：
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通讯作者：
C. Fraslin;J. Yáñez;D. Robledo;R. Houston
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作者：
C. Fraslin;J. Yáñez;D. Robledo;R. Houston
基因组选择改善生产性状的潜力已在许多水产养殖物种中得到广泛证明。大西洋鲑鱼育种计划通常包括兄弟姐妹测试计划，其中不能在选择候选者身上测量的特征是在候选者的兄弟姐妹身上测量的（例如抗病特征）。虽然对近亲进行年度检测是有效的，但由于基因分型和表型分型成本高，成本昂贵。因此，对远亲育种价值的准确预测可以显著降低基因组选择的成本。本研究的目的是(i)评估降低训练群体和验证群体之间的基因组关系对两个关键目标性状的基因组预测准确性的影响；体重和对海虱的抵抗力；（ii）评估遗传关系与SNP密度的相互作用，SNP密度是基因分型成本的主要决定因素。表型和基因型数据来自大西洋鲑鱼商业繁殖种群的两年班。一年内获得的基因组预测的准确性与结合两年内获得的海虱数量（0.49 - 0.48）和体重（0.63 - 0.61）的数据相似，但在跨年组进行预测时，预测准确性接近于零。系统地降低一年类内训练种群和验证种群之间的相关性导致基因组预测的准确性降低；当训练种群和验证种群被设置为不包含基因组关系>.3的亲属时，海虱数量的准确性从0.48下降到0.27，体重的准确性从0.63下降到0.29。训练和验证人群之间较低的相关性也往往导致高度偏倚的预测。SNP密度下降与训练种群和验证种群之间的相关性之间没有明显的相互作用。这些结果证实了在鲑鱼育种计划中训练和选择候选种群之间的遗传关系的重要性，并表明使用现有方法进行跨代预测将严重损害基因组选择的有效性。
The potential of genomic selection to improve production traits has been widely demonstrated in many aquaculture species. Atlantic salmon breeding programmes typically consist of sibling testing schemes, where traits that cannot be measured on the selection candidates are measured on the candidates’ siblings (such as disease resistance traits). While annual testing on close relatives is effective, it is expensive due to high genotyping and phenotyping costs. Therefore, accurate prediction of breeding values in distant relatives could significantly reduce the cost of genomic selection. The aims of this study were (i) to evaluate the impact of decreasing the genomic relationship between the training and validation populations on the accuracy of genomic prediction for two key target traits; body weight and resistance to sea lice; and (ii) to assess the interaction of genetic relationship with SNP density, a major determinant of genotyping costs. Phenotype and genotype data from two year classes of a commercial breeding population of Atlantic salmon were used. The accuracy of genomic predictions obtained within a year class was similar to that obtained combining the data from the two year classes for sea lice count (0.49 - 0.48) and body weight (0.63 - 0.61), but prediction accuracy was close to zero when the prediction was performed across year groups. Systematically reducing the relatedness between the training and validation populations within a year class resulted in decreasing accuracy of genomic prediction; when the training and validation populations were set up to contain no relatives with genomic relationships >0.3, the accuracies fell from 0.48 to 0.27 for sea lice count and from 0.63 to 0.29 for body weight. Lower relatedness between training and validation populations also tended to result in highly biased predictions. No clear interaction between decreasing SNP density and relatedness between training and validation population was found. These results confirm the importance of genetic relationships between training and selection candidate populations in salmon breeding programmes, and suggests that prediction across generations using existing approaches would severely compromise the efficacy of genomic selection.