Dagnachew Binyam S, Meuwissen Theo H E
Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway.
Genet Sel Evol. 2016 Sep 20;48(1):70. doi: 10.1186/s12711-016-0249-2.
The management of genetic variation in a breeding scheme relies very much on the control of the average relationship between selected parents. Optimum contribution selection is a method that seeks the optimum way to select for genetic improvement while controlling the rate of inbreeding.
A novel iterative algorithm, Gencont2, for calculating optimum genetic contributions was developed. It was validated by comparing it with a previous program, Gencont, on three datasets that were obtained from practical breeding programs in three species (cattle, pig and sheep). The number of selection candidates was 2929, 3907 and 6875 for the pig, cattle and sheep datasets, respectively.
In most cases, both algorithms selected the same candidates and led to very similar results with respect to genetic gain for the cattle and pig datasets. In cases, where the number of animals to select varied, the contributions of the additional selected candidates ranged from 0.006 to 0.08 %. The correlations between assigned contributions were very close to 1 in all cases; however, the iterative algorithm decreased the computation time considerably by 90 to 93 % (13 to 22 times faster) compared to Gencont. For the sheep dataset, only results from the iterative algorithm are reported because Gencont could not handle a large number of selection candidates.
Thus, the new iterative algorithm provides an interesting alternative for the practical implementation of optimal contribution selection on a large scale in order to manage inbreeding and increase the sustainability of animal breeding programs.
育种方案中遗传变异的管理在很大程度上依赖于对所选亲本之间平均亲缘关系的控制。最优贡献选择是一种在控制近亲繁殖率的同时寻求遗传改良最优选择方式的方法。
开发了一种用于计算最优遗传贡献的新型迭代算法Gencont2。通过在从三个物种（牛、猪和羊）的实际育种项目中获得的三个数据集上，将其与先前的程序Gencont进行比较来进行验证。猪、牛和羊数据集的候选选择个体数分别为2929、3907和6875。
在大多数情况下，两种算法选择的候选个体相同，并且在牛和猪数据集的遗传增益方面产生了非常相似的结果。在选择动物数量不同的情况下，额外选择的候选个体的贡献范围为0.006%至0.08%。在所有情况下，分配贡献之间的相关性都非常接近1；然而，与Gencont相比，迭代算法将计算时间大幅减少了90%至93%（快13至22倍）。对于绵羊数据集，仅报告了迭代算法的结果，因为Gencont无法处理大量的候选选择个体。
因此，新的迭代算法为大规模实际实施最优贡献选择以管理近亲繁殖和提高动物育种计划的可持续性提供了一个有趣的选择。
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