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International audience; Key message Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G x E). Phenomic selection is supposed to be efficient for modelling the G x E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G x E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G x E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G x E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G x E.

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.

International audience; In plant breeding, the selection of the best individuals is mainly based on phenotyping records. Because phenotyping is costly and time consuming, predictive tools such as Genomic selection (GS) have been developed in order to select among unphenotyped candidates. GS allows predicting the target traits for the selection candidates using the phenotypes of a training set and genotypic information collected on the training set and the selection candidates. Despite a good potential of the method to assist breeders in their selection choices, the cost of the genotyping still remains expensive, as GS requires to genotype each year the new selection candidates. In 2018, Rincent et al. developed a new, low cost, and high throughput method to predict the target trait of unobserved selection candidates. This method called phenomic selection (PS) is similar to GS, but genotyping is replaced by near infrared spectroscopy (NIRS). NIRS has the main advantage of being affordable, and already routinely applied on the selection candidates for many species such as wheat. GS has been well studied for twenty years, and many factors influencing its predictive ability are well understood. In PS, little is known about the factors influencing the predictive abilities, and about its performance relative to GS. We conducted the analyses on several datasets, corresponding to breeding lines drawn from the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments or at different steps of the breeding program. Contrary to genotypic data, near infrared spectra are indeed influenced by both the genotype and the environment. Thus, a selection candidate can be characterised by a multitude of spectra measured in different environments. The statistical model used was a simple H-BLUP model, reaching prediction ability from 0.26 to 0.62.Our results showed that the environment in which the NIR spectra was collected had an impor-tant impact on predictive ability and this impact was specific to the trait considered. Among all the models tested, combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. We finally tested a model which gathered NIRS and molecular marker effects. This model, GH-BLUP, was the best model of all, regardless of the trait or dataset, with prediction abilities reaching 0.31 to 0.73. In this study we showed that PS could be a great support tool for breeders to improve wheat breeding programs and could efficiently replace or complement GS..