Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method.

Genetic Analysis of Adult Plant Resistance to Yellow Rust and Leaf Rust in Common Spring Wheat Quaiu 3.

Identifying and utilizing rust resistance genes in wheat has been hampered by the continuous and rapid emergence of new pathogen races. A major focus of many wheat breeding programs is achieving durable adult plant resistance (APR) to yellow (stripe) rust (YR) and leaf (brown) rust (LR), caused by Puccinia striiformis and P. triticina, respectively.