Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat

Fusarium head blight (FHB) is a devastating disease of wheat, causing yield losses, reduced grain quality, and mycotoxin contamination. Breeding can mitigate the severity of FHB epidemics, especially with genomics-assisted methods. The mechanisms underlying resistance to FHB in wheat have been extensively studied, including phenological traits and genome-wide markers associated with FHB severity. Here, we aimed to improve genomic prediction for FHB resistance across breeding programs by incorporating FHB-correlated traits and FHB-associated loci as model covariates. We combined phenotypic data on FHB severity, anthesis date, and plant height with genome-wide marker data from five Central European winter wheat breeding programs for genome-wide association studies (GWAS) and genomic prediction. Within all populations, FHB was correlated with anthesis date and/or plant height, and a marker linked to the semi-dwarfing locus Rht-D1 was detected with GWAS for FHB. Including the Rht-D1 marker, anthesis date, and/or plant height as covariates in genomic prediction modeling improved prediction accuracy not only within populations but also in cross-population scenarios.

Molecular Investigations to Improve Fusarium Head Blight Resistance in Wheat: An Update Focusing on Multi-Omics Approaches

Fusarium head blight (FHB) is mainly caused by Fusarium graminearum (Fg) and is a very widespread disease throughout the world, leading to severe damage to wheat with losses in both grain yield and quality. FHB also leads to mycotoxin contamination in the infected grains, being toxic to humans and animals. In spite of the continuous advancements to elucidate more and more aspects of FHB host resistance, to date, our knowledge about the molecular mechanisms underlying wheat defense response to this pathogen is not comprehensive, most likely due to the complex wheat-Fg interaction. Recently, due to climate changes, such as high temperature and heavy rainfall, FHB has become more frequent and severe worldwide, making it even more urgent to completely understand wheat defense mechanisms. In this review, after a brief description of the first wheat immune response to Fg, we discuss, for each FHB resistance type, from Type I to Type V resistances, the main molecular mechanisms involved, the major quantitative trait loci (QTLs) and candidate genes found. The focus is on multi-omics research helping discover crucial molecular pathways for each resistance type. Finally, according to the emerging examined studies and results, a wheat response model to Fg attack, showing the major interactions in the different FHB resistance types, is proposed. The aim is to establish a useful reference point for the researchers in the field interested to adopt an interdisciplinary omics approach.

Diallel Analysis of Wheat Resistance to Fusarium Head Blight and Mycotoxin Accumulation under Conditions of Artificial Inoculation and Natural Infection

Breeding resistant wheat cultivars to Fusarium head blight (FHB), caused by Fusarium spp., is the best method for controlling the disease. The aim of this study was to estimate general combining ability (GCA) and specific combining ability (SCA) for FHB resistance in a set of eight genetically diverse winter wheat cultivars to identify potential donors of FHB resistance for crossing. FHB resistance of parents and F1 crosses produced by the half diallel scheme was evaluated under the conditions of artificial inoculation with F. graminearum and natural infection. Four FHB related traits were assessed: visual rating index (VRI), Fusarium damaged kernels (FDK), and deoxynivalenol and zearalenone content in the harvested grain samples. Significant GCA effects for FHB resistance were observed for the parental cultivars with high FHB resistance for all studied FHB resistance related traits. The significant SCA and mid-parent heterosis effects for FHB resistance were rare under both artificial inoculation and natural infection conditions and involved crosses between parents with low FHB resistance. A significant negative correlation between grain yield under natural conditions and VRI (r = -0.43) and FDK (r = -0.47) under conditions of artificial inoculation was observed in the set of the studied F1 crosses. Some crosses showed high yield and high FHB resistance, indicating that breeding of FHB resistant genotypes could be performed without yield penalty. These crosses involved resistant cultivars with significant GCA effects for FHB resistance indicating that that they could be used as good donors of FHB resistance.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.