Multi‐trait genomic selection can increase selection accuracy for deoxynivalenol accumulation resulting from fusarium head blight in wheat

Abstract

Multi‐trait genomic prediction (MTGP) can improve selection accuracy for economically valuable ‘primary’ traits by incorporating data on correlated secondary traits. Resistance to Fusarium head blight (FHB), a fungal disease of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), is evaluated using four genetically correlated traits: incidence (INC), severity (SEV), Fusarium damaged kernels (FDK), and deoxynivalenol content (DON). Both FDK and DON are primary traits; DON evaluation is expensive and usually requires several months for wheat breeders to get results from service laboratories performing the evaluations. We evaluated MTGP for DON using three soft red winter wheat breeding datasets: two diversity panels from the University of Illinois (IL) and Purdue University (PU) and a dataset consisting of 2019–2020 University of Illinois breeding cohorts. For DON, relative to single‐trait (ST) genomic prediction, MTGP including phenotypic data for secondary traits on both validation and training sets, resulted in 23.4 and 10.6% higher predictive abilities in IL and PU panels, respectively. The MTGP models were advantageous only when secondary traits were included in both training and validation sets. In addition, MTGP models were more accurate than ST models only when FDK was included, and once FDK was included in the model, adding additional traits hardly improved accuracy. Evaluation of MTGP models across testing cohorts indicated that MTGP could increase accuracy by more than twofold in the early stages. Overall, we show that MTGP can increase selection accuracy for resistance to DON accumulation in wheat provided FDK is evaluated on the selection candidates.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.)

“…Thus, the use of the MT-GS model is gaining popularity as a choice GS model to estimate the genetic merit of new genotypes. When comparing models, our results corroborate previous studies (Calus and Veerkamp 2011; Jia and Jannink 2012; Montesinos-López et al 2018; Lado et al 2018; Bhatta et al 2020; Gaire et al 2022) that MT-GS outperforms UNI-GS by harnessing genetic correlation between traits to improve predictive ability across traits. The proposed MT-GS aided sparse phenotyping depart from the previous reports of weak genetic correlation between traits as a limitation to the advantage of MT-GS over UNI, which was evident in the partially balanced phenotyping aided MT-GS.…”

“…In general, GS is often performed with univariate-trait (UT) models that assume genetic correlation among traits to be zero (Jia and Jannink 2012; Montesinos-López et al 2016, 2018; Bhatta et al 2020; Gaire et al 2022). However, in practice, breeders’ select for multiple traits that are genetically correlated, ranging from negative to positive correlations.…”

“…To harness the genetic correlation between traits and among genotypes to improve prediction accuracy, multi-trait (MT) models, which are the generalization of UT models, have been investigated. Several empirical studies (Calus and Veerkamp 2011; Montesinos-López et al 2018; Bhatta et al 2020; Gaire et al 2022) have reported improved prediction accuracy in different crops using MT models that allow borrowing of information between correlated traits and among genotypes compared to UT models. Prediction accuracy in MT-GS improves as correlation between traits increases (Jia and Jannink 2012; Okeke et al 2017; Montesinos-López et al 2018, 2019; Neyhart et al 2019), however, trait heritability varies and is a key limiting factor to upper bound of prediction accuracy (Manolio et al 2009; Yang et al 2015; Schopp et al 2017; Zhang et al 2019; Atanda et al 2021a).…”

“…The crucial question is how to design a MT-GS strategy that will optimize the trade-off between the limiting factors and accuracy of predicting the genetic value of the traits. Studies (Montesinos-López et al 2016, 2018, 2019; Guo et al 2014; Bhatta et al 2020; Gaire et al 2022) have highlighted the importance of each factor to prediction accuracy; however, nothing is known about their combinations on composition of traits in the training and prediction set in the context of MT-GS. Consequently, we investigated the influence of the limiting factors on composition of traits in the training and prediction sets to guide the use of MT-GS in a breeding program.…”

“…Further, in most MT-GS cross-validation studies (Montesinos-López et al 2016, 2018, 2019; Guo et al 2014; Bhatta et al 2020; Gaire et al 2022), the same set of genotypes overlap across traits for testing prediction models ( Suppl. Fig.…”