Validation of cross-progeny variance genomic prediction using simulations and experimental data in winter elite bread...

From simulations and experimental data, the quality of cross progeny variance genomic predictions may be high, but depends on trait architecture and necessitates sufficient number of progenies. Genomic predictions are used to select genitors and crosses in plant breeding. The usefulness criterion (UC) is a cross-selection criterion that necessitates the estimation of parental mean (PM) and progeny standard deviation (SD). This study evaluates the parameters that affect the predictive ability of UC and its two components using simulations. Predictive ability increased with heritability and progeny size and decreased with QTL number, most notably for SD. Comparing scenarios where marker effects were known or estimated using prediction models, SD was strongly impacted by the quality of marker effect estimates. We proposed a new algebraic formula for SD estimation that takes into account the uncertainty of the estimation of marker effects. It improved predictions when the number of QTL was superior to 300, especially when heritability was low. We also compared estimated and observed UC using experimental data for heading date, plant height, grain protein content and yield. PM and UC estimates were significantly correlated for all traits (PM: 0.38, 0.63, 0.51 and 0.91; UC: 0.45, 0.52, 0.54 and 0.74; for yield, grain protein content, plant height and heading date, respectively), while SD was correlated only for heading date and plant height (0.64 and 0.49, respectively). According to simulations, SD estimations in the field would necessitate large progenies. This pioneering study experimentally validates genomic prediction of UC but the predictive ability depends on trait architecture and precision of marker effect estimates. We advise the breeders to adjust progeny size to realize the SD potential of a cross.