Phenomic Selection: A new and efficient alternative to genomic selection. - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Book Sections Year : 2022

Phenomic Selection: A new and efficient alternative to genomic selection.


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.

Dates and versions

hal-03654962 , version 1 (29-04-2022)



Pauline Robert, Charlotte Brault, Renaud Rincent, Vincent Segura. Phenomic Selection: A new and efficient alternative to genomic selection.. Complex Trait Prediction, 2467, Springer US, pp.397-420, 2022, Methods in Molecular Biology, 978-1-0716-2205-6. ⟨10.1007/978-1-0716-2205-6_14⟩. ⟨hal-03654962⟩
142 View
0 Download



Gmail Mastodon Facebook X LinkedIn More