Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

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.

Predicting the genetic variance among progeny from a cross—prior to making said cross—would be a valuable metric for plant breeders to discriminate among possible parent combinations. The use of genomewide markers and simulated populations is one proposed method for making such predictions. Our objective was to assess the predictive ability of this method for three relevant quantitative traits within a breeding program regularly using genomewide selection. Using a training population of two‐row barley (Hordeum vulgare L.) lines, we predicted the mean (μ), genetic variance (VG), and superior progeny mean (μSP, mean of the best 10% of lines) of 330,078 possible parent combinations for Fusarium head blight (FHB) severity, heading date, and plant height. Twenty‐seven of these combinations were chosen to develop biparental populations, which were subsequently phenotyped for the same traits. We found that the predictive abilities (rMP) for μ and μSP were moderate to high (rMP = 0.46–0.69), whereas those for VG were lower (rMP = 0.01–0.48). Unsurprisingly, predictive ability was likely a function of trait heritability, as rMP estimates for heading date (the most heritable trait) were highest, and rMP estimates for FHB severity (the least heritable trait) were lowest. We observed strong negative bias when predicting VG (on average −83 to −96%), but the relative consistency of this bias across validation families indicates that it may have little impact when selecting crosses. We concluded that accurate predictions of VG and μSP are feasible, but as with any implementation of genomewide selection, reliable phenotypic data are critical.

Market changes in the malting and brewing industries have increased the demand for locally produced barley (Hordeum vulgare L.) in many regions across North America. Breeding for productive barley cultivars in diverse growing environments is complicated by genotype × environment interactions (GEIs), which can make selection for broad adaptation diicult but may be exploited to select optimal cultivars for each environment. Genomewide selection has recently become a useful tool to make eicient selections on individuals using genomewide marker data. To support the use of genomewide selection to breed locally adapted barley cultivars, the University of Minnesota barley breeding program is publicly releasing a panel of two-row barley lines, and accompanying data, called the S2MET (Spring Two-Row Multi-Environment Trial) (Reg. No. MP-2, NSL 526938 MAP). The S2MET includes 233 breeding lines grouped into a 183line training population and a 50-line validation population. The entire panel was genotyped using genotyping-bysequencing and phenotyped for 14 important traits in 44 location-year environments between 2015 and 2017. All data are freely available at the Triticeae Toolbox (https:// triticeaetoolbox.org/barley/), and we describe several ontap projects and breeding advances that are exploiting this resource. We believe this panel and dataset will be useful for answering important breeding questions related to genomewide selection and GEIs and developing locally superior barley cultivars.

Intermediate wheatgrass (Thinopyrum intermedium) is an outcrossing, cool season grass species currently undergoing direct domestication as a perennial grain crop. Though many traits are selection targets, understanding the genetic architecture of those important for local adaptation may accelerate the domestication process. Nested association mapping (NAM) has proven useful in dissecting the genetic control of agronomic traits many crop species, but its utility in primarily outcrossing, perennial species has yet to be demonstrated. Here we introduce an intermediate wheatgrass NAM developed by crossing ten phenotypically divergent donor parents to an adapted common parent in a reciprocal manner, yielding 1,168 F1 progeny from 10 families. Using genotyping by sequencing, we identified 8,003 SNP markers and developed a population-specific consensus genetic map with 3,144 markers across 21 linkage groups. Using both genomewide association mapping and linkage mapping combined across and within families, we characterize the genetic control of flowering time. In the analysis of two measures of maturity across four separate environments, we detected as many as 75 significant QTL, many of which correspond to the same regions in both analysis methods across 11 chromosomes. The results demonstrate a complex genetic control that is variable across years, locations, traits, and within families. The methods were effective at detecting previously identified QTL, as well as new QTL that align closely to the well-characterized flowering time orthologs from barley, including Ppd-H1 and Constans. Our results demonstrate the utility of the NAM for understanding the genetic control of flowering time and its potential for application to other traits of interest.

Circadian clock rhythms are shown to be intertwined with crop adaptation. To realize the adaptive value of changes in these rhythms under crop domestication and improvement, there is a need to compare the genetics of clock and yield traits.We compared circadian clock rhythmicity based on Chl leaf fluorescence and transcriptomics among wild ancestors, landraces, and breeding lines of barley under optimal and high temperatures. We conducted a genome scan to identify pleiotropic loci regulating the clock and field phenotypes. We also compared the allelic diversity in wild and cultivated barley to test for selective sweeps.We found significant loss of thermal plasticity in circadian rhythms under domestication. However, transcriptome analysis indicated that this loss was only for output genes and that temperature compensation in the core clock machinery was maintained. Drivers of the circadian clock (DOC) loci were identified via genome-wide association study. Notably, these loci also modified growth and reproductive outputs in the field. Diversity analysis indicated selective sweep in these pleiotropic DOC loci.These results indicate a selection against thermal clock plasticity under barley domestication and improvement and highlight the importance of identifying genes underlying for understanding the biochemical basis of crop adaptation to changing environments.

Plant breeding programs expend significant resources on multilocation testing to evaluate genotypes for advancement or potential cultivar release. The selection of genotype entries for these trials is typically based on previous phenotypic data or predictions; yet locations, important contributors to nongenetic variation, are often chosen in a less data-driven manner. Using agronomic and quality trait data from two long-term regional barley (Hordeum vulgare L.) nurseries, our objectives were (a) to measure the precision, repeatability, and representativeness of test locations based on multitrait data and (b) to optimize the selection of test locations for use in future trials. When considering traits individually, ideal locations could be identified simply, but a combined analysis of 11 traits indicated that very few locations were broadly favorable, and considerable tradeoffs are necessary. We developed a flexible optimization procedure to select the locations based on their precision, repeatability, and representativeness for multiple traits while simultaneously constraining the total number of locations. Optimization led to a 58-75% reduction in the number of locations, and therefore phenotyping costs, with little loss in data utility. Importantly, our approach allowed locations to be selected for phenotyping different sets of traits (e.g., either agronomic or both agronomic and malting quality), mimicking the often nested structure of trait data collection. This approach may be useful for individual plant breeding programs or collaborations wishing to increase the resource efficiency of these important regional evaluation trials.

Basic quantitative and population genetics topics are typically taught in introductory plant breeding courses and are critical for success in upper‐level study. Active learning, including simulations and games, may be useful for instruction of these concepts, which rely heavily on theory and may be more challenging for students. The statistical computing language R is now routinely used in the analysis of plant breeding experiments, but the command‐line interface of the language may be unsuitable for an introductory course. Here we describe qgshiny (quantitative genetics in shiny), an interactive application for performing simulations to understand basic theory in quantitative and population genetics. The initial version of the application includes modules on three core topics in quantitative genetics: randomly mating populations, genetic variance, and response to selection. Students can specify parameters and initiate simulations to assess their impact on responses such as allele frequency, genetic variance, and genetic gain, which together can be used to reinforce more general learning objectives. Feedback collected from students after engaging with the application suggests this tool can have a positive impact on student learning. The application is bundled in an R package, qgshiny, which is available through the Comprehensive R Archive Network (CRAN), on GitHub (https://github.com/neyhartj/qgshiny), or interactively through the shinyapps.io platform (http://neyhartj.shinyapps.io/qgshiny).