The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …

To develop more productive and resilient crops that are capable of feeding 10 billion people
by 2050, we must accelerate the rate of genetic improvement in plant breeding programs …

Multi-trait and multi-environment data are common in animal and plant breeding programs.
However, what is lacking are more powerful statistical models that can exploit the correlation …

Abstract Key Message Multi-trait genomic prediction models are useful to allocate available
resources in breeding programs by targeted phenotyping of correlated traits when predicting …

Plant breeders regularly evaluate multiple traits across multiple environments, which opens
an avenue for using multiple traits in genomic prediction models. We assessed the potential …

Genomic prediction is a promising approach for accelerating the genetic gain of complex
traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic …

When information on multiple genotypes evaluated in multiple environments is recorded, a
multi-environment single trait model for assessing genotype× environment interaction (G× E) …

Advances in high-throughput sequencing technologies have reduced the cost of genotyping
dramatically and led to genomic prediction being widely used in animal and plant breeding …

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not
rare in animal and plant breeding programs. However, there is a lack of statistical models …

The prediction accuracies of genomic selection depend on several factors, including the
genetic architecture of target traits, the number of traits considered at a given time, and the …