Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates
the breeding cycle. In this review, we discuss the history, principles, and basis of GS and …

Although long-term genetic gain has been achieved through increasing use of modern
breeding methods and technologies, the rate of genetic gain needs to be accelerated to …

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

One of the major issues in plant breeding is the occurrence of genotype× environment (GE)
interaction. Several models have been created to understand this phenomenon and explore …

One of the most widely used kernel functions in genomic-enabled prediction is the Gaussian
kernel. Selection of the bandwidth parameter for kernel regression has generally been …

Soybeans (Glycine max (L.) Merr.) are a multifunctional crop that contributes significantly to
global food security, economic development, and agricultural sustainability. Genomic …

Abstract Background Non-linear Bayesian genomic prediction models such as
BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms …

Statistical learning literature has been tremendously growing in recent years. However,
modeling complex dependence structures explicitly and interpreting model results for …

Селекция домашних животных занимает большое место в научных исследованиях
различных государств. Создание новых и улучшение уже существующих пород …

Large-scale envirotyping (environmental+ typing) or simply enviromics, is an emerging field
of data science, applied both in agronomic research and plant breeding. This omics consists …