Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height

Abstract

Key message Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height. Abstract A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.

Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

“…The index-based selection was found to be particularly useful for negatively correlated traits that are hard to simultaneously improve [ 82 ], which was the case between grain yield and GPC, yield and plant height, and maturity and GPC in both conventional and organic management systems ( Figure 1 ). In such cases, phenotypic selection indices have been used for simultaneously improving multiple negatively correlated traits in wheat, including grain yield and GPC [ 83 ], spot blotch resistance, early maturity, and short plant height [ 60 ], Helminthosporium leaf blight resistance, early maturity, grain yield, and kernel weight [ 82 ]. In oat, Dolan et al [ 84 ] reported the superiority of both the restricted index and Smith index, as compared with selection for grain yield alone, for simultaneously improving heading date, plant height, and barley yellow dwarf virus resistance.…”

“…Each index is defined as a linear combination of either the observed mean phenotypic values of the traits of interest with the trait’s economic weights predefined by breeders, or both phenotype and genomic (molecular markers) data [ 50 , 56 ]. Selection indices have been used in several species, including wheat [ 40 , 57 , 58 , 59 , 60 ]. Most breeders, however, do not routinely use selection indices, due to the need for a priori knowledge of fixed effects, variance–covariance matrices, and relative economic weights [ 55 , 61 ].…”

Wheat Omics: Advancements and Opportunities

“…Based on a novel approach, an imputation-enabled SNP array, Illumina Infinium Wheat Barley 40K SNP array version 1.0, has been designed to capture the haplotypic diversity in wheat and barley germplasm [ 24 ]. In addition to the reliable and cost-effective SNP chip arrays, NGS-based genotyping-by-sequencing (GBS) has been used to detect global variation in wheat germplasm for various applications [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”

Genomic approaches for improving grain zinc and iron content in wheat

“…In GS, a set of individuals are genotyped and phenotyped as a training population for use to develop the prediction model ( Juliana et al, 2018 ). The efficacy of GS has been tested for multiple traits in several crops, including wheat, for yield ( Juliana et al, 2018 ), leaf rust and yellow rust ( Juliana et al, 2017 ), spot blotch ( Juliana et al, 2022b ), and wheat blast ( Juliana et al, 2022a ). GS to improve grain mineral concentrations has been employed in maize ( Mageto et al, 2020 ), rice ( Rakotondramanana et al, 2022 ), and wheat ( Velu et al, 2018 ), with reasonable prediction accuracies.…”