Robert Pauline, Brault Charlotte, Rincent Renaud, Segura Vincent
INRAE-Université Clermont-Auvergne, UMR1095, GDEC, Clermont-Ferrand, France.
Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette, France.
Methods Mol Biol. 2022;2467:397-420. doi: 10.1007/978-1-0716-2205-6_14.
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
最近，有人提议将分子标记转换为近红外（NIR）光谱，以推断个体之间的关系，并进一步进行表型组选择（PS），这类似于基因组选择（GS）。PS的概念与基于类似基因组组学（GLOB）的选择相似，在GLOB选择中，分子标记被内表型所取代，如代谢物或转录水平，不同的是，例如通过近红外光谱（NIRS）获得的表型组信息通常比其他组学的成本低得多。尽管NIRS已在育种中常规使用了几十年，特别是用于处理最终产品的品质性状，但将其用于预测其他感兴趣的性状并进一步进行选择还是新的做法。自从关于PS的开创性论文发表以来，有几篇出版物主张在植物育种中使用光谱采集（包括NIRS和高光谱成像）来进行PS，这可能展示了其潜力。在本章中，我们首先回顾最初提出的PS概念，并对与表型组学在育种中的应用相关的精选论文进行分类。我们还进一步综述了有关所使用技术类型、光谱预处理以及用于进行预测的统计建模的精选文献。我们讨论了可能影响PS效率的因素，并在预测能力方面将其与GS进行比较。最后，我们提出了PS在植物育种背景下未来工作和应用的几个前景。
