Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population's phenotype for genotype by environment effect had a positive impact on GS model's predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.

中文翻译：

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基因组选择在双单倍体冬小麦育种程序中的实际应用。

作物改良是一项长期的，昂贵的机构努力。使用单核苷酸多态性（SNP）信息估算基因组育种值的基因组选择（GS）已被证明可通过加速动物育种程序中的育种过程来有效提高遗传增益。至于作物改良，除少数例外，GS的适用性仍在算法性能评估中。在这项研究中，我们研究了使用硬红冬小麦（Triticum aestivum L.）双单倍体群体在谷类开发中与GS适用性相关的因素。连续两年对GS的性能进行了评估，以预测谷物产量。通常，半参数复制内核希尔伯特空间预测算法的性能优于参数基因组最佳线性无偏预测。对于参数和半参数算法，在年内交叉验证中，可预测性都存在明显的偏差，这表明进行更可靠的预测需要跨年验证。通过环境效应调整训练人群的基因型表型对GS模型的预测能力具有积极影响。可能由于标记冗余，处于绝对成对相关系数阈值0.4的SNP的选定子集产生了可比较的结果，并减轻了考虑整个SNP集的计算负担。最后，在正在进行的育种和选择工作中，本研究提供了基于品系选择与GS结果的偏差的置信度度量，支持GS在小麦品种开发中的实施。