Wageningen University & Research, Animal Breeding and Genomics, P.O. Box 338, 6708 PB, Wageningen, The Netherlands.
Wageningen University & Research, Business Economics, P.O. Box 8130, 6706 KN, Wageningen, The Netherlands.
Genet Sel Evol. 2019 Sep 3;51(1):49. doi: 10.1186/s12711-019-0491-5.
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.
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.
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.
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.
育种公司可能希望在有限的预算内，从其育种计划中最大限度地提高遗传增益率。在鲑鱼育种计划中，对无法在候选对象上记录的性状进行选择候选者的全同胞性能测试。虽然对每个候选对象进行表型和基因型分析的全同胞数量的综合基因型边际增益减少，但成本却呈线性增加，这表明在这些活动之间分配预算存在最佳方案。在这里，我们研究了如何优化将固定预算分配给性能测试中表型和基因型测试个体的数量。
综合基因型的增益是选择候选者的全同胞数量的函数，这些全同胞数量包括：（1）在海虱抗性挑战测试中表型，（2）在屠宰测试中表型，（3）在挑战测试中基因型，和（4）在屠宰测试中基因型。这些活动都受到预算限制。我们使用网格搜索法，优化了活动之间的预算分配，以最大化综合基因型的增益。我们对最大综合基因型增益和最佳方案中活动之间预算的相对分配进行了敏感性分析。
最大综合基因型增益为 386 欧元/吨/代。综合基因型增益的响应面在最佳方案附近相当平坦，但在极端情况下会强烈弯曲。最大增益对预算的规模和对育种目标性状的相对重视程度敏感，但对基因组预测的准确性和表型与基因型分析的成本不太敏感。最佳方案中活动之间的预算相对分配对表型和基因型分析的成本以及对育种目标性状的相对重视程度敏感，但对基因组预测的准确性和预算的规模不太敏感。
在性能测试中，对选择候选者的全同胞进行表型和基因型分析的数量进行最佳预算分配，可以最大限度地提高综合基因型的增益。尽管优化群体大小和基因型分析努力的潜在收益可能很小，但不会增加额外的成本。
