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Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulated

机译:基于模拟数据集的猪育种计划中基因组选择的优化选择策略

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The conventional BLUP uses the phenotype and pedigree information to predict the estimated breeding values (EBV) of individuals in genetic evaluation. However, to obtain the phenotypes of interest in swine breeding program requires relatively a long time until the completion of performance testing, which produces an extra breeding cost for the culled pigs in swine industry. An alternative solution is to pre-select a predefined proportion of young replacement piglets for future performance testing through genomic selection, which could reduce the number of testing animals entering into performance testing program and hence reduce the breeding costs associated with the tests. In this study, four strategies of genomic selection applications in swine breeding program were compared through simulation to investigate the potential benefits in the different strategies in swine breeding program. For comparison purpose, the conventional BLUP selection was simulated as strategy 1 to form the benchmark basis for comparisons. Strategy 2 was an extreme case for applying genomic selection, where newborn piglets were selected directly based on their genomic enhanced breeding value (GEBV) only without further performance testing. Strategy 3 was to pre-select piglets, based on their GEBV, for entry to performance testing in early stage, and then the breeding stock were selected ultimately based on their EBVs predicted by BLUP method when the phenotypic records were available. Similar to strategy 3, strategies 4 and 5 also used the GEBVs to pre-select replacement piglets in an early stage; however, the breeding stocks in strategies 4 and 5 were selected based on the breeding values obtained using the bi-variable model and the conventional index method to combine GEBV and EBV information, respectively, when individuals had both GEBV and phenotypes available after the performance testing. Comparing these strategies, strategy 4 resulted in the highest accuracy in first three generations and achieved the best cumulative selection response in the last generation, followed by strategies 1, 3, 5, and 2. The proportion of pre-selection of boars and sows in the early stage affected the efficiency of genomic selection substantially
机译:传统的BLUP使用表型和谱系信息来预测基因评估中个体的估计育种值(EBV)。但是,要在猪育种计划中获得感兴趣的表型,需要较长的时间才能完成性能测试,这会给养猪业的淘汰猪带来额外的育种成本。另一种解决方案是通过基因组选择预先选择预定比例的幼仔代仔猪进行未来的性能测试,这可以减少进入性能测试程序的测试动物的数量,从而降低与测试相关的育种成本。在这项研究中,通过模拟比较了四种基因组选择策略在猪育种计划中的应用,以研究不同策略在猪育种计划中的潜在利益。为了进行比较,将传统的BLUP选择作为策略1进行仿真,以形成比较的基准。策略2是应用基因组选择的极端情况,新生仔猪仅根据其基因组增强育种值(GEBV)直接选择,而无需进一步的性能测试。策略3是根据仔猪的GEBV预先选择仔猪,以便早期进入性能测试,然后在有表型记录时根据BLUP方法预测的EBV最终选择种猪。与策略3相似,策略4和5也使用GEBV在早期阶段预先选择替代仔猪。但是,当个体在性能测试后同时具有GEBV和表型时,分别基于使用双变量模型和常规指数方法获得的育种值来选择策略4和5的育种种群,以结合GEBV和EBV信息。与这些策略相比,策略4在前三代中的准确性最高,而在最后一代中获得了最佳的累积选择响应,其次是策略1、3、5和2。公猪和母猪的预选比例早期大大影响了基因组选择的效率

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