■在植物育种中,我们的目标通常是同时改善多个特征。然而,不知道每个特征的经济价值,很难决定关注哪些特征。这就是“所需增益选择指数”派上用场的地方,当经济权重不可用时,根据育种者对期望改进的优先顺序,可以在每个性状中产生最佳收益。然而,它们缺乏最大化选择响应和确定指数与净遗传价值之间相关性的能力。
■这里,我们报告了一种迭代的期望增益选择指数方法的开发,该方法优化了期望增益值的采样,以实现针对多个特征的目标或用户指定的选择响应。对于一个子集或所有研究的性状,该靶向选择响应可以是受约束的或不受约束的。
■我们使用基因组估计育种值(GEBV)对包含3,331个品系的面包小麦(Triticumaestivum)参考育种群体中的七个性状进行了测试,并在七个性状中实现了0.29至0.47的预测准确性。使用3,005个双单倍体品系验证了这些指数,所述双单倍体品系来源于选自参考群体的亲本之间的杂交。我们测试了三种用户指定的响应场景:约束等权重(INDEX1),约束产量优势权重(INDEX2),和一个不受约束的重量(INDEX3)。当约束一组特征时,我们的方法实现了对用户指定选择响应的等效响应,并且该响应比传统的无需迭代的期望增益选择指数方法的响应要好得多。有趣的是,当使用不受约束的重量时,我们的迭代方法最大化了选择响应,并将选择候选的平均GEBV向所需方向移动。
■我们的结果表明,该方法不仅是在经济权重不可用时的最佳选择,但当限制选择反应是一个不利的选择。
UNASSIGNED: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where \"desired gain selection indices\" come in handy, which can yield optimal gains in each trait based on the breeder\'s prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit.
UNASSIGNED: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits.
UNASSIGNED: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction.
UNASSIGNED: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.