In plant breeding, the goal of genomewide selection is to predict the merit of unobserved individuals, particularly those in the next breeding generation. Predictions of these individuals in unobserved or future environments would be of additional use to a breeder. For many of the complex traits targeted in breeding, this may require management of genotype × environment interactions by, for example, using data from homogeneous groups of environments. Our objectives were to assess the accuracy of genomewide predictions in unobserved environments both within and between breeding generations; we aimed to compare training sets that included data from all possible environments with those that included data from (a) decreasingly similar environments or (b) discrete clusters of similar environments. A 183-line spring barley (Hordeum vulgare L.) training population and 50-line offspring test population were phenotyped in 29 location–year environments for grain yield, heading date, and plant height. Environmental similarities were measured using phenotypic data, geographic distance, or environmental covariables. When using training data from more, but decreasingly similar environments, prediction accuracy increased, but marginal gains declined; in some cases, accuracy declined with additional data. Clusters of environments informed by phenotypes (i.e., phenotypic correlations or multiplicative models) typically improved prediction accuracy within a generation, but not between generations (offspring population). Our study suggests that, as an alternative to using data from all available environments, informative subsets may be advantageous for genomewide predictions within a single breeding generation, but not between generations.
Bibliographical noteFunding Information:
We thank the many collaborators of this project that helped conduct trials and collect phenotype data, including Daniel Sweeney, Mark Sorrells, Christian Kapp, Kenneth D. Kephart, Liz Elmore, Jamie Sherman, Eric J. Stockinger, Scott Fisk, Patrick Hayes, Sintayehu Daba, Mohsen Mohammadi, Nia Hughes, Lisa Trumble, Lewis Lukens, Pablo González Barrios, Aaron Mills, Chad Sellmer, Richard Horsley, Martin Hochhalter, Jean Goudet, Heather Darby, Carl Duley, Chris Evans, and Gongshe Hu. We also thank Ed Schiefelbein, Guillermo Velasquez, and Karen Beaubien for technical support. Resources from the Minnesota Supercomputing Institute were used to complete this project. This research received support from the U.S. Wheat and Barley Scab Initiative, the Minnesota Department of Agriculture, Rahr Malting Company, the Brewers Association, the American Malting Barley Association, USDA‐NIFA Grants 2018‐67011‐28075 and 2018‐67013‐27620.