Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

Authors

Kassa Semagn, University of Alberta
Muhammad Iqbal, University of Alberta
Diego Jarquin, University of Florida
Harpinder Randhawa, Lethbridge Research and Development Centre
Reem Aboukhaddour, Lethbridge Research and Development Centre
Reka Howard, University of Nebraska-LincolnFollow
Izabela Ciechanowska, University of Alberta
Momna Farzand, University of Alberta
Raman Dhariwal, Lethbridge Research and Development Centre
Colin W. Hiebert, Morden Research and Development Centre
Amidou N’Diaye, University of Saskatchewan
Curtis Pozniak, University of Saskatchewan
Dean Spaner, University of Alberta

Document Type

Article

Date of this Version

6-30-2022

Citation

Semagn, K.; Iqbal, M.; Jarquin, D.; Randhawa, H.; Aboukhaddour, R.; Howard, R.; Ciechanowska, I.; Farzand, M.; Dhariwal, R.; Hiebert, C.W.; et al. Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction. Plants 2022, 11, 1736. https://doi.org/ 10.3390/plants11131736

Comments

Open access.

Abstract

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from 􀀀0.11 to 􀀀0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.