BACKGROUND Breeding companies may want to maximize the rate of genetic gain from their breeding program within a limited budget. In salmon breeding programs, full-sibs of selection candidates are subjected to performance tests for traits that cannot be recorded on selection candidates. While marginal gains in the aggregate genotype from phenotyping and genotyping more full-sibs per candidate decrease, costs increase linearly, which suggests that there is an optimum in the allocation of the budget among these activities. Here, we studied how allocation of the fixed budget to numbers of phenotyped and genotyped test individuals in performance tests can be optimized. METHODS Gain in the aggregate genotype was a function of the numbers of full-sibs of selection candidates that were (1) phenotyped in a challenge test for sea lice resistance (2) phenotyped in a slaughter test (3) genotyped in the challenge test, and (4) genotyped in the slaughter test. Each of these activities was subject to budget constraints. Using a grid search, we optimized allocation of the budget among activities to maximize gain in the aggregate genotype. We performed sensitivity analyses on the maximum gain in the aggregate genotype and on the relative allocation of the budget among activities at the optimum. RESULTS Maximum gain in the aggregate genotype was €386/ton per generation. The response surface for gain in the aggregate genotype was rather flat around the optimum, but it curved strongly near the extremes. Maximum gain was sensitive to the size of the budget and the relative emphasis on breeding goal traits, but less sensitive to the accuracy of genomic prediction and costs of phenotyping and genotyping. The relative allocation of budget among activities at the optimum was sensitive to costs of phenotyping and genotyping and the relative emphasis on breeding goal traits, but was less sensitive to the accuracy of genomic prediction and the size of the budget. CONCLUSIONS There is an optimum allocation of budget to the numbers of full-sibs of selection candidates that are phenotyped and genotyped in performance tests that maximizes gain in the aggregate genotype. Although potential gains from optimizing group sizes and genotyping effort may be small, they come at no extra cost.

中文翻译：

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大西洋鲑鱼育种计划中全同胞测试组规模和基因分型工作的经济优化。

背景技术育种公司可能希望在有限的预算内最大化其育种计划的遗传增益率。在鲑鱼育种计划中，选择候选者的全同胞要接受无法在选择候选者上记录的性状的性能测试。虽然表型分析和每个候选者更多全同胞基因分型带来的总基因型边际收益减少，但成本线性增加，这表明这些活动之间的预算分配存在最佳状态。在这里，我们研究了如何优化性能测试中固定预算对表型和基因型测试个体数量的分配。方法 总基因型的增益是选择候选者的全同胞数量的函数，这些候选者（1）在海虱抗性挑战测试中表现型（2）在屠宰测试中表现型（3）在挑战测试中表现型， (4)在屠宰试验中进行基因分型。每项活动都受到预算限制。使用网格搜索，我们优化了活动之间的预算分配，以最大限度地提高总基因型的收益。我们对总基因型的最大收益以及最佳活动之间预算的相对分配进行了敏感性分析。结果 总基因型的最大增益为每代 386 欧元/吨。总基因型增益的响应面在最佳值附近相当平坦，但在极值附近强烈弯曲。最大增益对预算的大小和对育种目标性状的相对重视很敏感，但对基因组预测的准确性以及表型和基因分型的成本不太敏感。最佳活动之间预算的相对分配对表型和基因分型的成本以及对育种目标性状的相对重视敏感，但对基因组预测的准确性和预算的大小不太敏感。结论 对于在性能测试中进行表型和基因分型的选择候选者的全同胞数量，存在最佳的预算分配，使总基因型收益最大化。尽管优化群体规模和基因分型工作的潜在收益可能很小，但不需要额外成本。