Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population

Abstract

Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Photosynthetic Efficiency in Flag Leaves and Ears of Winter Wheat during Fusarium Head Blight Infection

“…Furthermore, fungicide treatment was shown to be more effective at reducing FHB severity and decreasing the accumulation of the main Fusarium mycotoxin deoxynivalenol (DON) in moderately resistant, compared to highly FHB susceptible varieties [10]. However, selecting for such genotypes is hampered by the complexity of FHB resistance traits [11,12]. The mechanisms governing more efficient defense against FHB are not fully uncovered [13].…”

Mapping and validation of Fusarium wilt race 2 resistance QTL from Citrullus amarus line USVL246-FR2

“…Marker validation studies with a C. lanatus population are required to demonstrate the effectiveness of resistance introgression into a cultivated watermelon background using MAS or MABS. Also, validation of markers developed for respective QTL in an independent population aid in construction of an effective model for genomic selection (Jannink et al 2010 ; Poland and Rutkoski 2016 ; Rutkoski et al 2014 ; Zhang et al 2021 ). Although the FonR2 KASP markers were checked for introgression utility by genomic comparison of the wild ( C. amarus ) and cultivated ( C. lanatus ) genomes, only a few ( N = 16) of the markers developed were polymorphic in the segregating interspecific population.…”

Identification of Fusarium head blight sources of resistance and associated QTLs in historical and modern Canadian spring wheat

“…Of the five types of FHB resistance mechanisms described in the literature ( Schroeder and Christensen, 1963 ; Mesterházy, 1995 ), Type I (resistance to initial infection) and Type II (resistance to the spread of infection in the spike) are the two most widely studied forms of resistance ( Hales et al., 2020 ). For that reason, the major focuses in the global FHB research involve the identification of new sources of Type I and Type II resistance, mapping of quantitative trait loci (QTLs), and developing FHB resistance germplasm by pyramiding multiple resistance alleles using conventional and/or modern breeding methods ( Steiner et al., 2017 ; Buerstmayr et al., 2020 ; Mesterhazy, 2020 ; Zhang et al., 2021 ).…”