For more than 10,000 years, the selection of plant and animal traits that are better tailored for human use has shaped the development of civilizations. During this period, bread wheat (Triticum aestivum) emerged as one of the world's most important crops. We used exome sequencing of a world-wide panel of almost 500 genotypes selected from across the geographical range of the wheat species complex to explore how 10,000 years of hybridization, selection, adaptation and plant breeding shaped the genetic makeup of modern bread wheats. We observed considerable genetic variations at the genic, chromosomal and subgenomic levels deciphering the likely origins of modern day wheats, the consequences of range expansion and allelic variants selected since its domestication. Our data supports a reconciled model of wheat evolution and provides novel avenues for future breeding improvement.

A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi‐environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing G × E. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for G × E. Based on our experiences in teaching statistical methods to plant breeders, for specialized G × E courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G × E analyses is two‐fold. On the one hand, we see the G × E problem as the building of predictive models for genotype‐specific reaction norms. On the other hand, the G × E problem consists in the identification of suitable variance‐covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear‐bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G × E, we show that our preference for mixed linear‐bilinear models for analyzing G × E is supported by recent trends in the types of methods for G × E analysis that are most frequently cited.

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Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME‐GP models differing in the way environment‐specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction of explicit environmental covariables. We discuss the models not only in the light of their accuracy, but also in their ability to predict the different parts of the incomplete genotype × environment interaction (G × E) table: (Gt; Et), (Gu; Et), (Gt; Eu), and (Gu; Eu), where G is genotype, E is environment, both tested (t; in one or more instances) and untested (u). Using the ‘Steptoe’ × ‘Morex’ barley (Hordeum vulgare L.) population as an example, we show the advantage of ME‐GP models that account for G × E. In addition, for our example data set, we show that for prediction in the most challenging scenario of untested environments (Eu), the use of explicit environmental information is preferable over the simpler approach of predicting from a main effects model. Besides producing the most general ME‐GP model, the use of environmental covariables naturally links with ecophysiological and crop‐growth models (CGMs) for G × E. We conclude with a list of future research topics in ME‐GP, where we see CGMs playing a central role.