Machine learning techniques for crop genomic selections, especially for single-environment
plants, are well-developed. These machine learning models, which use dense genome …

Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials
(MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the …

The phenomenon of genotype× environment (G× E) interaction in plant breeding decreases
selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction …

In genomic selection (GS), genotype× environment interaction (G× E) can be modeled by a
marker× environment interaction (M× E). The G× E may be modeled through a linear kernel …

The snpReady R package is a new instrument developed to help breeders in genomic
projects such as genomic prediction and association studies. This package offers three …

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with
the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package …

Kernel methods are flexible and easy to interpret and have been successfully used in
genomic-enabled prediction of various plant species. Kernel methods used in genomic …

Deep learning (DL) is a promising method for genomic-enabled prediction. However, the
implementation of DL is difficult because many hyperparameters (number of hidden layers …

Multi-environment trials are routinely conducted in plant breeding to select candidates for
the next selection cycle. In this study, we compare the prediction accuracy of four developed …

The genomic prediction of unobserved genetic values or future phenotypes for complex
traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly …