Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs

Abstract

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses.

Validating Genomewide Predictions of Genetic Variance in a Contemporary Breeding Program

“…A distinctly triangular pattern was observed when comparing m and ĝ V (Fig. This pattern, based on the idea that lines with similarly extreme phenotypes will likely share alleles at most QTL influencing a trait, was predicted in theory (Zhong and Jannink, 2007) and observed in subsequent studies (Bernardo, 2014;Mohammadi et al, 2015;Lado et al, 2017). Among crosses with a common parent, this relationship was approximately linear, as exemplified in Fig.…”

“…The predictive ability for family mean was moderate to high for all traits (r MP = 0.46-0.62). This is not unexpected, since any error associated with the predicted marker effect will more strongly influence ĝ V than m (Zhong and Jannink, 2007;Lado et al, 2017). For all traits, the predictive ability for genetic variance was always lower than that for m, ranging from 0.01 (FHB severity, not significant) to 0.48 (plant height).…”

“…Similar evidence was found for quality traits in wheat (Triticum aestivum L.), where low genetic variance may be desirable to restrict the trait value within industry specifications (Lado et al, 2017). In barley (Hordeum vulgare L.), initial predictions suggest that both grain yield and deoxynivalenol (a mycotoxin produced by the fungal pathogen Fusarium graminearum Schwabe) content could be improved by selecting crosses with favorable superior progeny mean, calculated using predictions of the mean and genetic variance of a cross .…”

“…First, the additive genetic variance in a biparental family is expected to increase with successive generations of inbreeding (Bernardo, 2010). In contrast, the covariance between loci may be more important for complex traits, such as FHB severity, that are influenced by many loci of small effect distributed more diffusely throughout the genome (Lado et al, 2017). It is possible that incomplete inbreeding led to deviations from the final additive genetic variance in families, reducing the predictive ability.…”

A connected half‐sib family training population for genomic prediction in barley

“…A better option for increasing PA with CV A or CV W would be selection of parents that maximize within‐family genetic variance for all traits under selection. Predicting genetic variance of a cross has historically been a difficult prediction problem, but recent studies have described several methods to improve prediction of progeny variance based on genome‐wide molecular markers (Lehermeier, Teyssèdre, & Schön, 2017; Mohammadi, Tiede, & Smith, 2015; Osthushenrich et al., 2018) although variable results have been reported (Adeyemo & Bernardo, 2019; Lado et al., 2017; Neyhart & Smith, 2019). These methods require estimation of marker effects to predict progeny variance, meaning potential parents must be genotyped and phenotyped in a sufficiently large field experiment to accurately estimate marker effects.…”