The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.
Bibliographical noteFunding Information:
We thank Ed Schiefelbein, Guillermo Velasquez, and Karen Beaubien for technical support during population development, phenotypic data collection, and genotyping. Thanks go to Ruth Dill-Macky for supplying F. graminearum inoculum for St. Paul and to Madeline Smith and Joseph Wodarek for managing the FHB trial in Crookston, MN. We are grateful to Austin Case, Jo Heuschele, John Hill Price, Ian McNish, Becky Zhong, and Alexander Susko for assistance and encouragement. Resources from the Minnesota Supercomputing Institute were used to complete this project. This research was supported by the U.S. Wheat and Barley Scab Initiative, the Minnesota Department of Agriculture, Rahr Malting Company, the Brewers Association, the American Malting Barley Association, and USDA-NIFA Grant #2018-67011-28075.
Copyright © 2019 Neyhart et al.
- Cross selection
- Genetic correlation
- Genomewide prediction
- Genomic Prediction
- Shared Data Resources
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SNP Genotyping Data for the Barley Population in "Registration of the S2MET Barley Mapping Population for Multi-Environment Genomewide Selection"
Neyhart, J. & Smith, K. P., Data Repository for the University of Minnesota, 2019