A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars

Abstract

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

Exploring genomic regions involved in bread wheat resistance to leaf rust at seedling/adult stages by using GWAS analysis

“…Generally speaking, the GP accuracy depends on several factors, including the LD levels, genetic diversity in populations, genomic selection methodology, and the genetic architecture of the trait [ 42 ]. In the recent study, we found that GBLUP appeared as better than RR-BLUP and BRR, reflecting that GBLUP is a potent model for implementing genomic selection in wheat accessions.…”

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

“…Overall, obtaining the best outcomes from the breeding value-based methods depend on the genetic architecture of trait, genetic variation, etc. [ 18 ].…”

“…Research shows that gBLUP and rrBLUP are similar models. One of the advantages of gBLUP over rrBLUP is the reduction of the dimensions of the mixed equations to the number of people in the reference population, the calculation of accuracy and error predicting corrective values ​​as commonly used in pedigree methods and combining The information of genotyped and non-genotyped individuals was mentioned simultaneously in the mixed equations [ 18 ].…”

“…Of our observations, the highest prediction accuracy was achieved via the gBLUP model. Shabannejad et al [ 18 ] evaluated classic approaches for exploiting GP accuracy by BRR, gBLUP, rrBLUP models in normal and drought environments in wheat cultivars and landraces. They identified the highest GP accuracies via the gBLUP and BRR method.…”

“…Genomic prediction utilizes all genetic markers within a model to train a prediction model, which is consisted of all genetic impacts. The model is applied to a validation set for estimating its accuracy [ 18 ]. Several studies have demonstrated high or moderate GP accuracy for quantitative characteristics in barley ( Hordeum vulgare L.) [ 19 ], maize ( Zea mays L.) [ 20 ], rice ( Oryza sativa L.) [ 21 ], oat ( Avena sativa L.) [ 22 ] and wheat ( Triticum aestivum L.) [ 17 ].…”