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The modeling of genotype × environment interactions (GEI) is important to understand how new crops perform in different environments and management systems. Naked barley (Hordeum vulgare L.) is a type of barley where the hull threshes freely from the grain and can be used for multiple end uses, including food, malt, and animal feed. We examined the performance of a winter naked barley trial grown in organic conditions across three regions and 3 years in northern latitude regions of the United States. We recorded yield, test weight, heading date, plant height, threshability, winter survival, barley stripe rust (Puccinia striiformis f. sp. hordei Westend), barley scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. nov), and spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito & Kurib.). Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low GEI. Grain yield, test weight, scald, and winter survival had high GEI and were analyzed using the additive main effect and multiplicative interaction (AMMI) model, genotype plus GEI biplot (GGE), and Finlay–Wilkinson (FW) stability analysis. The environmental patterns explained by the AMMI and GGE models indicated that selection for high GEI traits should occur within target environments and across multiple years within the target environments. Environmental groups were separated by Oregon as the first group and Wisconsin and New York as the second group. Core Ideas: Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low genotype × environment interactions.Grain yield, test weight, scald, and winter survival had high genotype × environment interaction effects.Environmental groupings were separated into OR as the first group and NY and WI as the second group. [ABSTRACT FROM AUTHOR]

Threshability, defined here as the propensity of grains to lose their hull after harvest, is a key trait in naked barley (Hordeum vulgare L.). While threshability is a defining characteristic of naked grains and has been found to be associated with grain size and shape, its genetic architecture is poorly described. The goals of this study were to identify quantitative trait loci associated with threshability and evaluate their utility as covariates in genomic prediction models. A genome‐wide association study identified two loci on chromosomes 2H and 3H associated with threshability. The locus on chromosome 2H accounted for 9.9% of the phenotypic variance explained (PVE). The locus on chromosomes 3H accounted for 7.8% of the PVE. With effects on threshability of 0.18 and 0.29 for each marker, respectively, these markers could have a limited impact when implemented in marker‐assisted selection. Predictive ability for threshability was 0.842 using a structured genomic best linear unbiased prediction model. Incorporation of the markers with significant associations as covariates in the model did not improve predictive ability. Predictive ability was improved by the use of a multi‐trait model including grain test weight. The high predictive ability for threshability overall indicate that genomic selection would be useful in selection for threshability in naked barley. Core Ideas: Two markers were identified with significant associations to threshability.Predictive ability for threshability was 0.842 using a structured gBLUP model.Predictive ability was not increased by inclusion of identified QTL as covariates.Predictive ability was increased using a multi‐trait prediction model. [ABSTRACT FROM AUTHOR]

Predicting the performance of new plant genotypes under new environmental conditions could accelerate the development of locally adapted and climate resilient cultivars. Enabling these predictions may rely on extending the genomewide prediction framework to include environmental covariates (EC), but such models have generally been tested under limited, less breeding‐realistic contexts. Using a barley (Hordeum vulgare L.) multi‐environment dataset, our objectives were to compare multi‐environment prediction models and scenarios that target genotypes from different breeding generations, use different levels and timescales of ECs, and are applied to different agronomic and quality traits. When predicting the phenotypes of previously tested genotypes in untested environments, models that included the interaction of genomewide markers and pre‐selected in‐season ECs resulted in more accurate predictions (rMG or rMP) within (rMG = 0.56–0.94) and across (rMP = 0.63–0.87) environments; similar accuracy was achieved within (rMP = 0.46–0.89) and across (rMP = 0.87–0.95) locations when using only ECs from realistically available historical climate data. Shifting the prediction target to a distinct, untested offspring population slightly reduced model performance within environments or locations, but rMP across environments (rMP = 0.60–0.86) or locations (rMP = 0.87–0.94) remained very high. Though we achieved moderately high rMP for most traits in the challenging scenario of predicting the offspring population in holdout environments, the similarity between training and target environments, like that between populations, will be a limiting factor for enabling accurate predictions of new genotypes under new growing conditions. Core Ideas: Genomewide prediction with select environmental covariates led to accurate whole‐phenotype predictions.Historical climate data enabled a more breeding‐realistic framework for predicting phenotypes in new locations.Predictive ability was improved for both tested genotypes and untested offspring across new environments.Genetic and environmental similarity may have limited accuracy of untested offspring in holdout environments. [ABSTRACT FROM AUTHOR]

Naked barley (Hordeum vulgare L.) grain threshes freely from its hull during harvesting and cleaning. Much of the available naked barley germplasm is unadapted to U.S. barley growing regions, and few genotypes have been selected to thrive under organic systems. The goal of this research was to characterize a set of spring naked barley genotypes for agronomic traits in the northern United States under organic conditions and estimate the degree of genotype × environment interaction (GEI). To achieve this goal, a multienvironment trial was conducted over 3 yr. Experiments were grown under organic conditions where traits including grain yield, test weight, plant height, heading date, and threshability were evaluated. Contributions of environment, genotype, and GEI to the phenotypic variance were calculated. In the tested germplasm, test weight, plant height, heading date, and threshability were found to have higher variance attributed to genotypic effects compared to GEI. Grain yield variance attributed to GEI was five times greater than that of genotype alone. Genotype + GEI (GGE) and location‐grouping (LG) biplots showed two sets of environments where naked barley genotypes performed similarly. Sensitivity analysis by Finlay–Wilkinson regression found that grain yield was highly sensitive to changes in environment mean yield. These results show that selection for grain yield should be conducted within mega‐environments to leverage GEI patterns. Heading date, plant height, test weight, and threshability can be selected across mega‐environments. Core Ideas: There is five‐times greater genotype × environment interaction (GEI) than genetic variance for grain yield. Biplots define two mega‐environments in the northern United States for naked barley grain yield. Other assessed agronomic traits and threshability are more affected by genotype than GEI. [ABSTRACT FROM AUTHOR]

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. Core Ideas: Test locations in regional barley breeding nurseries were evaluated for multiple traits.No single location was ever superior for genotypic evaluation of many traits.Optimization identified a reduced set of locations with little loss in phenotypic data utility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the current study, 248 two‐rowed and another 253 six‐rowed spring barley (Hordeum vulgare L.) breeding lines from several US barley breeding programs were evaluated in multiple locations in Egypt to select for potentially adapted lines. The introduced plant materials were planted in two locations during three growing seasons (2015, 2016, and 2017). The focus of the first growing season was to increase available seeds and collect preliminary observations. During the second and third growing seasons, the advanced plant materials and local check cultivars were planted in an incomplete block design in two replicates within three locations. Grain yield, leaf rust, number of days to flowering, and plant height were measured. Based on grain yield production, the top 94 lines from the two‐rowed and 100 from the six‐rowed barley lines were selected and advanced for further evaluation in 2017. The introduced materials contained several lines that consistently outperformed the check cultivars. Therefore, these lines could be used as parents to improve barley production and enhance genetic diversity or be directly released as cultivars after appropriate testing in Egypt. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous studies have shown that there is considerable population structure in cultivated barley (Hordeum vu/gare L.), with the strongest structure corresponding to differences in row number and growth habit. U.S. barley breeding programs include six-row and two-row types and winter and spring types in all combinations. To facilitate mapping of complex traits in breeding germplasm, 1816 barley lines from 10 U.S. breeding programs were scored with 1536 single nucleotide polymorphism (SNP) genotyping assays. The number of SNPs segregating within breeding programs varied from 854 to 1398. Model-based analysis of population structure showed the expected clustering by row type and growth habit; however, there was additional structure, some of which corresponded to the breeding programs. The model that fit the data best had seven populations: three two-row spring, two six-row spring, and two six-row winter. Average linkage disequilibrium (LD) within populations decayed over a distance of 20 to 30 cM, but some populations showed long-range LD suggestive of admixture. Genetic distance (allele-sharing) between populations varied from 0.11 (six-row spring vs. six-row spring) to 0.45 (two-row spring vs. six-row spring). Analyses of pairwise LD revealed that the phase of allelic associations was not well correlated between populations, particularly when their allele-sharing distance was >0.2. These results suggest that pooling divergent barley populations for purposes of association mapping may be inadvisable. [ABSTRACT FROM AUTHOR]

Kernel color and grain protein concentration (GPC) are two of the most important attributes of barley (Hordeum vulgate L.) grain intended for use in malting and brewing. Grain with protein levels that are too low or high or that have dark kernel color, which may result from the disease complex of kernel discoloration (KD), is unacceptable for the malting and brewing industries. The purpose of this study was to use molecular markers to map quantitative trait loci (QTLs) for KD and GPC in two recombinant inbred line (RIL) populations. One population was created by means of the high GPC and KD resistant cultivar Chevron. The other population was created from a KD resistant cultivar MNBrite that was derived from Chevron through eight generations of crossing and selection for bright kernels. The traits KD and GPC were evaluated in four and three environments, respectively, using the Chevron population and six environments using the MNBrite population. GPC and KD score were negatively correlated in both populations. A revised genetic map for the Chevron/M69 population, with an additional 45 simple sequence repeat (SSR) markers, was used to identify nine QTLs associated with KD on chromosomes 1(7H), 2(2H), 4(4H), 6(6H), and 7(5H), including two major QTLs on chromosome 6(6H). A single QTL for GPC identified in the Chevron mapping population, accounting for over 55% of the phenotypic variation, was located on chromosome 6(6H) and was coincident with one of the two major QTLs for KD. In this region, the Chevron allele increased GPC and decreased KD score. In the MNBrite mapping population, the same region of chromosome 6(6H) was mapped with SSR markers, and QTL analysis verified that this region was associated with both KD and GPC, indicating that MNBrite inherited this region of chromosome 6(6H) from Chevron. The results of this study suggest that if GPC and KD are controlled by tightly linked genes, then it should be possible to use SSR markers to identify recombinants in this region of chromosome 6(6H) and break the linkage to allow selection for KD resistance without high GPC. Alternatively, if GPC and KD are conditioned by the same gene, then it should be possible to select for KD resistance from Chevron by means of SSR markers and to use phenotypic selection to introgress other genes to reduce GPC to acceptable levels. [ABSTRACT FROM AUTHOR]