Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean prediction accuracy of machine learning genomic prediction models increased up to 7% over the well-established Factor Analytic Multiplicative Mixed Model among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, prediction accuracy, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the feature engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling.

Fernandes IK ，Vieira CC ，Dias KOG ，Fernandes SB ... -  《-》

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.

Dias KODG ，Gezan SA ，Guimarães CT ，Nazarian A ，da Costa E Silva L ，Parentoni SN ，de Oliveira Guimarães PE ，de Oliveira Anoni C ，Pádua JMV ，de Oliveira Pinto M ，Noda RW ，Ribeiro CAG ，de Magalhães JV ，Garcia AAF ，de Souza JC ，Guimarães LJM ，Pastina MM ... -  《-》

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.

Acosta-Pech R ，Crossa J ，de Los Campos G ，Teyssèdre S ，Claustres B ，Pérez-Elizalde S ，Pérez-Rodríguez P ... -  《-》

Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.).

Trachsel S ，Dhliwayo T ，Gonzalez Perez L ，Mendoza Lugo JA ，Trachsel M ... -  《PLoS One》

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.

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 × E). Phenomic selection is supposed to be efficient for modelling the G × 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 × 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 × 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 × 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 × E.

Robert P ，Goudemand E ，Auzanneau J ，Oury FX ，Rolland B ，Heumez E ，Bouchet S ，Caillebotte A ，Mary-Huard T ，Le Gouis J ，Rincent R ... -  《-》