A comparative analysis of genomic and phenomic predictions of growth-related traits in three-way coffee hybrids

Mbebi Alain J., Breitler Jean-Christophe, Bordeaux Mélanie, Sulpice Ronan, McHale Marcus, Hao Tong, Toniutti Lucile, Alonso Castillo Jonny, Bertrand Benoît, Nikoloski Zoran. 2022. A comparative analysis of genomic and phenomic predictions of growth-related traits in three-way coffee hybrids. G3 - Genes Genomes Genetics, 12 (9):jkac170, 14 p.

https://doi.org/10.1093/g3journal/jkac170

Article de revue ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total

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Url - autres données associées : https://github.com/alainmbebi/GP-PP

Résumé : Genomic prediction (GP) has revolutionized crop breeding despite remaining issues of transfer- ability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction (PP) models that can account, in part, for this challenge. Here, we compare and contrast GP and PP models for three growth-related traits, namely, leaf count, tree height, and trunk diameter, from two coffee three-way hybrid (H3W) populations exposed to a series of treatment-inducing environmental conditions. The models are based on seven different statistical methods built with genomic markers and chlorophyll a fluorescence (ChlF) data used as predictors. This comparative analysis demonstrates that the best performing PP models show higher predictability than the best GP models for the considered traits and environments in the vast majority of comparisons within H3W populations. In addition, we show that PP models are transferrable between conditions, but to a lower extent between populations and we conclude that ChlF data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

Mots-clés Agrovoc : Coffea, hybride, amélioration génétique, amélioration des plantes, technique de prévision, méthode statistique, variation génétique, variation phénotypique, modèle de simulation, modèle mathématique

Mots-clés libres : Genomic prediction, Phenomic prediction, 3-way coffee hybrids, Chlorophyll a fluorescence, GenPred, Shared Data Resource

Classification Agris : F30 - Génétique et amélioration des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) BREEDing Coffee for AgroForestry Systems

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