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

• Published in: The plant genome 2017-07, Vol.10 (2), p.1-12
• Main Authors: Lado, Bettina, Battenfield, Sarah, Guzmán, Carlos, Quincke, Martín, Singh, Ravi P., Dreisigacker, Susanne, Peña, R. Javier, Fritz, Allan, Silva, Paula, Poland, Jesse, Gutiérrez, Lucía
• Format: Article
• Language: English
• Subjects: Breeding; Crosses, Genetic; Equilibrium; Genes, Plant; Genetic crosses; Genetic diversity; Genomes; Genomics; Genotype; Linkage Disequilibrium; Mathematical models; Plant breeding; Statistical analysis; Triticum - genetics
• ISSN: 1940-3372; 1940-3372
• DOI: 10.3835/plantgenome2016.12.0128

Core Ideas Cross prediction strategies for grain yield and baking quality traits were compared. Crosses for all parent combinations were obtained via genomic prediction models. Mid‐parent selection was similar to accounting for variance when selecting yield. The variance had a larger impact in cross predictions for quality traits. 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 (VLD) or assuming linkage equilibrium (VLE). 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 (Triticum aestivum 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.