Phenomic Selection: A New and Efficient Alternative to Genomic Selection.

Abstract

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.

Plant phenotyping relevance

植物育種におけるNIR・ハイパースペクトル取得と統計モデルによる表現型予測を中心に扱う方法論レビューであり、フェノミック選抜の技術・前処理・予測モデルを体系的に評価している。

Code and data availability

This is a review/protocol chapter on phenomic selection. It contains no public phenotype datasets, spectral data, author analysis code, or trained models specific to this paper; all referenced datasets and methods belong to cited prior works, and no availability statements or repository URLs for the authors' own data或码

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