Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment

Abstract

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017-2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from -0.03 to 0.74 for AC and -0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.

Keywords: GWAS; anther extrusion; floral traits; genome-wide prediction; hybrid wheat.

FIGURE 1

Boxplots of best linear unbiased estimates of anther count (AC) expressed in percentage and visual score (VS) across the three environments (El Batan 2017, Obregon 2018 and El Batan 2019). VS data were collected from only two environments (2018 Obregon and 2019 El Batan). Visual score scale ranges from 0 (0% extruded anthers) to 10 (100% extruded anthers), assessed visually during flowering. Visual score data were not collected in 2017.

FIGURE 2

Results from the genome-wide association scan: (A) Three-dimensional scatterplot showing the relationship between the first three principal components (PC1, PC2, and PC3) from molecular marker data. (B) A pairwise linkage disequilibrium (LD) decay plot with pairwise marker LD in a sliding window of 100 adjacent markers in the y-axis and genetic distance in centimorgans from the genetic map by Wang et al. (2014) in the x-axis. The red line indicates a moving average of r² values for 10 adjacent markers. (C) A Manhattan plot showing −log₁₀(P) values of marker trait association (MTA) for anther count (AC) in El Batan (2017) across the genome. The dotted line represents the Bonferroni significance threshold for MTA. (D) A quantile-quantile (QQ) plot showing the distribution of expected vs actual −log10(P) values of GWAS using AC from El Batan (2017). (E) A Manhattan plot showing −log₁₀(P) values of MTA for anther count (AC) in El Batan (2019) across the genome. (F) A QQ plot for GWAS using AC data from El Batan (2019). (G) A Manhattan plot showing −log₁₀(P) values of MTA for visual score (VS) in El Batan (2019) across the genome. (H) A QQ plot for GWAS using VS data from El Batan (2019).