Integrating Phenomic Selection Using Single-Kernel Near-Infrared Spectroscopy and Genomic Selection for Corn Breeding Improvement

Rafaela Prado Graciano, Marco Antonio Peixoto, Kristen A. Leach, Noriko Suzuki, Jeff Gustin, A. Mark Settles, Paul R. Armstrong, Marcio FR Resende
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Abstract

Phenomic Selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using non-destructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: G-BLUP and P-BLUP models, which used relationship matrices based on SNP and NIRS data, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the P-BLUP model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and effectively distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with considerably higher accuracies than GS when low marker densities were used. This study highlights the potential of NIRS both to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.
利用单核近红外光谱进行表观选择与基因组选择相结合,促进玉米育种改良
表观选择(PS)是一种经济有效的方法,用于预测复杂性状和提高育种计划中的遗传增益。其统计程序与基因组选择(GS)模型中使用的程序类似,但分子标记数据被近红外光谱(NIRS)等表观数据所取代。然而,近红外光谱技术在 PS 中的应用通常是利用破坏性取样或在田间建立选择实验后收集数据。在此,我们探索了在甜玉米育种计划中使用非破坏性单粒近红外光谱技术的 PS 应用,重点是预测未来未观察到的具有经济重要性的田间性状,包括果穗和植株性状。在多样性面板上采用了三种模型:G-BLUP和P-BLUP模型(使用基于SNP和NIRS数据的关系矩阵)以及一个组合模型。通过不同数量的 SNP 对基因组关系矩阵进行了评估。此外,在多样性面板上训练的 P-BLUP 模型被用于选择种植前发芽的双倍单倍体 (DH) 株系,并利用观测数据对预测结果进行了验证。研究结果表明,PS 具有良好的预测能力(如株高预测能力为 0.46),并能有效区分未经测试的 DH 株系的高发芽率和低发芽率。虽然 GS 的表现普遍优于 PS,但结合两种信息的模型预测能力最高,在使用低标记密度时,其准确率大大高于 GS。这项研究强调了近红外光谱的潜力,它既能在 GS 可能不可行的情况下实现遗传增益,又能在降低基因分型成本的同时保持/提高基于 SNP 信息的准确性。
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