Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan

Abstract

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

“…Among the prediction models used, GBLUP and BayesB were the most effective, while EN was the least. The results in this study supported previous genomic prediction studies, where GBLUP and similar models predicted the disease resistance more accurately than other models ( Avni et al, 2017 ; Juliana et al, 2017 ; Tehseen et al, 2021 ).…”

“…The reason for low prediction accuracies can be attributed to smaller population sizes and unrelated genotypes. Recently, the prediction accuracy for stripe rust resistance in wheat landraces from Afghanistan was observed to be between 0.33 to 0.38 ( Tehseen et al, 2021 ). Among the prediction models used, GBLUP and BayesB were the most effective, while EN was the least.…”

“…The application of genomic prediction depends on the population size, marker density, model performance, heritability of the trait, training population size, and breeding population relatedness ( Daetwyler et al, 2008 ; Bassi et al, 2016 ). In wheat, genomic prediction studies have been reported to predict rust resistance in diverse wheat landraces ( Daetwyler et al, 2014 ; Crossa et al, 2016 ), landraces from Afghanistan ( Tehseen et al, 2021 ), tetraploid wheat ( Azizinia et al, 2020 ), and improved wheat germplasm ( Ornella et al, 2012 ; Rutkoski et al, 2014 ; Bassi et al, 2016 ; Juliana et al, 2017 ).…”

Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

“…The resistance QTL identified in this study will be further evaluated for their use in marker-assisted selection and as covariates in genomic prediction models for stripe rust resistance in the breeding program. The breeding population demonstrated highly heritable, quantitative resistance to stripe rust and low population structure, indicating that genomic prediction of stripe rust resistance can be successfully applied in this population (Crossa et al 2017 ; Juliana et al 2017 ; Muleta et al 2017 ; Tehseen et al 2021 ).…”

QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population

“…Most breeding programs in the world rely on two types of genetic resistance based on major and minor genes ( Chen et al, 2014 ). Genetic resistance due to major genes is termed as a seedling and/or all-stage resistance and is often race-specific and based on the gene for gene hypothesis and is effective throughout a plant’s life ( Burdon et al, 2014 ; Tehseen et al, 2021 ). However, such resistance in commercial wheat cultivars is often short-lived and is overcome by new races of stripe rust pathogens virulent on the major resistance gene ( Boyd 2005 ; Ellis et al, 2014 ; Hulbert and Pumphrey 2014 ), whereas the minor gene resistance is often not expressed until in the later stages of plant life and is commonly referred to as horizontal or adult plant stage resistance ( Steele et al, 2001 ; Boyd 2005 ).…”