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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]

Growing crops with improved N use efficiency (NUE) could decrease fertilizer inputs and the negative environmental effects of N overuse. We measured nine agronomic and yield component traits under N-limiting and nonlimiting treatments for 250 two-row and 250 six-row barley (Hordeum vulgare L.) breeding lines. We calculated four stress indices to approximate NUE. To identify quantitative trait loci (QTLs), we conducted association mapping (AM) for these phenotypic data with 3072 single nucleotide polymorphism markers. All traits and two stress indices--the geometric mean and the stress tolerance index--exhibited significant genetic variation within the two-row and six-row panels. Of the 25 QTLs identified in the two-row panel and 31 identified in the six-row panel, only four were common to both. Four of the QTLs detected in the two-row panel and 10 detected in the six-row panel were identified under the N-limiting treatment or for a stress index, but not under the nonlimiting treatment, signaling that they may be associated with NUE. We detected a QTL for grain protein concentration (GPC) on chromosome 6H that has been mapped previously in barley and is collinear with the well-characterized Gpc-B1 locus in wheat. Groups of lines defined by marker haplotypes at this locus exhibited significant differences in GPC but not in grain yield. These results support that breeding strategies for improving NUE include crossing between two- and six-row parents with complementary alleles, phenotypic selection based on stress indices, marker-assisted selection for desirable alleles, and genomic selection to capture small-effect loci. [ABSTRACT FROM AUTHOR]

Genomic selection uses marker-based predictions to improve and accelerate the breeding process. Numerous studies have investigated the accuracy of genomic predictions; however, few studies have directly compared genomic and phenotypic selection. We compared genomic and phenotypic selection in five sets of selection candidates from a barley breeding program. In each set, about 96 breeding lines were genotyped with 1536 single nucleotide polymorphism (SNP) markers and phenotyped for yield, Fusarium head blight (FHB) severity, and deoxynivalenol (DON) concentration. A set of 168 lines and the same set of SNP markers were used to train a prediction model and predict the performance of the selection candidates using ridge regression best linear unbiased prediction. The best-performing 10% of the breeding lines in each selection candidate set were selected using both methods and revaluated in several trials. We found similar significant response to selection using genomic and phenotypic selection for FHB severity and DON concentration, and no significant response for yield using either method. For all traits, genomic selection significantly increased genetic similarity compared with the selection candidates. In addition, genomic selection, compared with phenotypic selection, resulted in an increase in the frequency of favorable alleles. Three indirect selection methods for DON concentration, (predicted FHB severity, empirical FHB severity, and predicted DON concentration) performed similarly to direct phenotypic selection for DON, but differed considerably in cost. We conclude that the use of genomic selection for yield and FHB breeding in barley should result in gains from response to selection that are similar to the gains obtained using phenotypic selection, but with a shorter breeding cycle time and lower cost. [ABSTRACT FROM AUTHOR]

one of the most important factors affecting genomic prediction accuracy appears to be training population (Tp) composition. The objective of this study was to evaluate the effect of genomic relationship on genomic prediction accuracy and determine if adding increasingly unrelated individuals to a Tp can reduce prediction accuracy. To accomplish this, a population of barley (Hordeum vulgare L.) lines from the University of Minnesota (lines denoted as MN) and North Dakota State University (lines denoted as ND) breeding programs were used for model training. predictions were validated using two independent sets of progenies derived from MN × MN crosses and ND × ND crosses. predictive ability sharply decreased with decreasing relationship between the Tp and validation population (Vp). More importantly, it was observed that adding increasingly unrelated individuals to the Tp can actually reduce predictive ability compared with smaller Tps consisting of highly related individuals only. reported results are possibly conditional on the relatively low marker density (342 single nucleotide polymorphisms [SNps]) used. Nevertheless, these findings suggest plant breeding programs desiring to use genomic selection could benefit from focusing on good phenotyping of smaller Tps closely related to the selection candidates rather than developing large and diverse Tps. [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]

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]

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]

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]

Malt barley (Hordeum vulgare L.) is an economically important crop worldwide. Barley malting quality is difficult to breed for, as it is a combination of complex traits that are time consuming and expensive to phenotype. This makes marker-assisted selection--and thus quantitative trait locus (QTL) mapping--particularly appealing. We used the Oregon State University and University of Minnesota facultative and winter six-rowed barley advanced breeding lines to assemble a genomewide association studies (GWAS) panel, named the FAC-WIN6, to map malting quality QTL within our breeding programs. Winter and facultative malt barleys can be sown in fall, which makes them more sustainable in terms of soil erosion and input efficiency. The FAC-WIN6 projects are part of the Triticeae Coordinated Agricultural Project, which aims to develop sustainable barley and wheat (Triticum aestivum L.) germplasm that is robust to climate change. The FAC-WIN6 has 300 lines, with genotypic data for 5812 polymorphic single- nucleotide polymorphism markers. We conducted a 2-yr, single-location GWAS experiment for eight malting quality traits: plump grain, grain protein, and six modification traits. We report 20 malting quality QTL across seven traits. Eight of the QTL appear to be novel. For each QTL, we provide an effect estimate, a representative marker, and a candidate gene. Our results are useful for marker-assisted selection, as well as for strengthening the confidence on several QTL mapped in other barley germplasm. [ABSTRACT FROM AUTHOR]

The fungal disease barley stripe rust (BSR, causal agent Puccinia striiformis f. sp. hordei) can greatly reduce yield and quality of malt barley (Hordeum vulgare L.), a valuable crop in the distilling and brewing industries. Yield losses of up to 70% have been reported. Resistant cultivars are the most sustainable defense against BSR, especially those cultivars with pyramids of quantitative disease resistance (QDR) genes. The Oregon State University (OSU) barley breeding program has selected for BSR resistance for decades and has released cultivars with BSR resistance. We wanted to determine the number and effect of the loci underlying that resistance, as well as whether it was conferred by QDR or race-specific qualitative loci. To test these, we used BSR resistance quantitative trait loci (QTL) mapping via genomewide association studies (GWAS) in adult-plant field trials (3 yr, one location) and race-specific seedling greenhouse trials (five races). The FAC-WIN6, a GWAS panel composed of 300 lines that represent the OSU and University of Minnesota facultative and winter six-rowed malt barley breeding programs, was used in genotyping and phenotyping. The FAC-WIN6 was developed as part of the USDA-ARS Triticeae Coordinated Agricultural Project but was not developed specifically for BSR resistance. In total, we detected three race-specific QTL in greenhouse trials (one of which was also significant in field trials) and 14 QTL significant in field trials only with 2.3 to 20.7% additive reduction in percentage disease severity. At least five QTL were considered novel. These results indicated that the OSU breeding program had both QDR and race-specific loci for BSR resistance. We suspected that its BSR resistance distributions in the FAC-WIN6 might align with the major population structure patterns in ways that would be difficult to account for with a GWAS model alone. Therefore, based on post hoc diagnostics, we report a qualitative "usability" level for each QTL. [ABSTRACT FROM AUTHOR]

Silflower (Silphium integrifolium Michx.) has been identified as a candidate for domestication as a perennial oilseed crop. In order to successfully domesticate silflower it will be necessary to identify traits that should be targeted for selection. We have conducted the first multilocation trial of replicated silflower genotypes at six sites across four states. By observing architectural, phenological, and yield traits in a diversity panel of silflower breeding material, we were able to estimate the heritability of these traits, as well as patterns of correlation across years and environments. We found that this population expressed heritable variation for many, but not all, traits of interest, and that targeted introgression of diversity may be beneficial for some traits. In addition, we used regression analysis to identify several traits, particularly height, number of stalks per plant, and stalk diameter as important contributors to determining levels of yield and levels of lodging. These results will help to guide future silflower domestication efforts and have already shaped the direction of our breeding program. Core Ideas: Silflower is being domesticated as a perennial oilseed crop to provide ecological benefits. It is unclear which traits are highest priority for improvement. A diversity panel was evaluated at six sites for several architectural, phenological, and yield traits. Estimates of heritability, variation between locations and year, and trait correlations will inform selection. [ABSTRACT FROM AUTHOR]

Predicting genetic variances of biparental populations has been a long-standing goal for plant breeders. The ability to discriminate among crosses with similarly predicted high means but different levels of genetic variance (VG) should improve the effectiveness of breeding. We developed a procedure that uses established progeny simulation and genomic prediction strategies to predict the population mean (μ) and VG, the mean of the desired 10% of the progeny (superior progeny mean [μsp]), and correlated responses of multiple traits for biparental populations. The proposed procedure, PopVar, is herein demonstrated using a training population (TP) composed of 383 breeding lines that have been genotyped and phenotyped for yield and deoxynivalenol (DON). Marker effects estimated from the TP were used to calculate genotypic estimated breeding values (GEBVs) of 200 simulated recombinant inbred lines (RILs) per cross. Values of μ, VG, and μsp were then calculated directly from the RIL GEBVs. We found that μ explained 82 and 88% of variation in μsp for yield and DON, respectively, and adding VG to the regression model increased those respective R2 values to 99.5 and 99.6%. The results of correlated response revealed that although yield and DON are unfavorably correlated, the correlation was near zero or slightly negative in some simulated crosses, indicating the potential to increase yield while decreasing DON. This work extends the current benefits of genomic selection to include the ability to design crosses that maximize genetic variance with more favorable correlations among traits. PopVar is available as an R package that researchers and breeders are encouraged to use for empirical evaluation of the methodology. [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]

Kernel shape and ß-glucan content are important attributes for oat (Avena sativa L.) millers. The identification of genomic regions controlling these traits, as well as their effect on phenotype, will enable rapid gain fromselection. The objectives of this study were (a) to identify the genomic regions associated with kernel shape in an oat panel adapted to subtropical environments and (b) to clarify the relationship between kernel shape and ß-glucan content in oat. An elite oat panel comprising 406 inbred lines was assessed in Londrina and Eldorado do Sul, Southern Brazil. Genome-wide association analyses were performed for each environment separately and also in a multi-environment analysis. Phenotypic and genetic correlation coefficients were estimated between kernel shape traits and ß-glucan content. Genomic regions influencing kernel length were identified onMrg06, Mrg21, and Mrg24. Kernel width and kernel thickness were genetically correlated and shared a quantitative trait locus, located on Mrg13 of the oat consensus map. Phenotypic and genetic correlations were found between kernel shape traits and ß-glucan content, indicating a negative relationship between kernel width or kernel thickness and ß-glucan content. Oat breeding programs that aim to maintain minimum levels of ß-glucan for nutrition and minimum kernel widths for milling will need to screen for both traits; otherwise, indirect selection will lead to unfavorable results. However, the negative correlation was not too strong (-0.4), suggesting that improvement for both traits can be made simultaneously. [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]

Silphium perfoliatum L. (cup plant, silphie) and S. integrifolium Michx. (rosinweed, silflower) are in the same subfamily and tribe as sunflower (Helianthus annuus L.). Silphium perfoliatum has been grown in many countries as a forage or bioenergy crop with forage quality approaching that of alfalfa (Medicago sativa L.) and biomass yield close to maize (Zea mays L.) in some environments. Silphium integrifolium has large seeds with taste and oil quality similar to traditional oilseed sunflower. Silphium species are all long-lived, diploid perennials. Crops from this genus could improve the yield stability, soil, and biodiversity of agricultural landscapes because, in their wild state, they are deep rooted and support a wide diversity of pollinators. In contrast with premodern domestication, de novo domestication should be intentional and scientific. We have the luxury and obligation at this moment in history to expand the domestication ideotype from food and energy production to include (i) crop-driven ecosystem services important for sustainability, (ii) genetic diversity to enable breeding progress for centuries, (iii) natural adaptations and microbiome associations conferring resource use effciency and stress tolerance, and (iv) improving domestication theory itself by monitoring genetic and ecophysiological changes from predomestication baselines. Achieving these goals rapidly will require the use of next-generation sequencing for marker development and an international, interdisciplinary team committed to collaboration and strategic planning. [ABSTRACT FROM AUTHOR]

Crop improvement relies on the ability to utilize novel genetic variation. Six-rowed malting barley (Hordeum vulgare L. subsp. vulgare) developed in the US Midwest has an especially narrow genetic base. Therefore, developing strategies to widen the genetic base in barley is important for continued crop improvement. A wild barley [Hordeum vulgare L. subsp. spontaneum (K. Koch) Thell.] advanced backcross-nested association mapping (AB-NAM) population was developed to assess wild-barley-derived alleles in an elite spring six-rowed malting barley background. The 796 BC2F4:6 lines in the AB-NAM were derived from 25 wild barley accessions backcrossed to the cultivar Rasmusson. Because the AB-NAM design minimizes the deleterious and unadapted traits of the wild barley parents, the population was screened in five field environments for the agronomic traits: days to heading, height, productive tiller number (PTN), test weight, and yield. The number of loci identified ranged from two (PTN, the trait with the lowest heritability) to 19 (height). Domestication-related genes were identified for each trait, particularly the photoperiod gene Ppd-H1 for days to heading, height, and PTN and the nonbrittle Btr1/Btr2 loci for test weight and yield. Only a single variant showed beneficial variation for yield from a wild barley allele, but positive effects were identified for the other traits examined, including positive variants for test weight on chromosomes 1H and 3H. The AB-NAM population is a novel resource to map agronomically important traits associated with wild barley alleles. [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]

Assignment or exclusion of an individual to specific populations or cultivars based on molecular genetic markers provides an attractive approach for varietal identification at the individual level in cross-pollinated plant species. The objectives of this study were (i) to explore the molecular diversity and relationships between Australasian perennial ryegrass (Lolium perenne L.) populations; (ii) to investigate accuracy of assignment of individuals to different types of populations including ecotypic, nonrestricted- and restricted-based cultivars; and (iii) to determine the effect of variable number of SSR loci and different statistical analysis methods on assignment accuracy. Eight forage perennial ryegrass populations comprising 48 individual plants per population were genotyped with 29 simple sequence repeat (SSR) marker loci. The number of alleles per locus ranged from 3.72 to 6.76. The mean observed heterozygosity varied from 0.419 to 0.538. Various genetic distance estimates and clustering methods obtained results consistent with breeding history. Genetic variation among (8.7%) and within populations (91.3%) was significant. Accuracy of individual assignment differed by population type, and it was higher (>90%) for restricted-based cultivars than for ecotypic and nonrestricted-based populations. These results indicate that SSR marker profiles can be effectively used to assign individuals for outbreeding populations such as perennial ryegrass. The approach used in this study may be useful for ryegrass germplasm management issues such as cultivar identification at the individual level. [ABSTRACT FROM AUTHOR]

The symbiotic association between perennial ryegrass (Lolium perenne L.) and the fungal endophyte Neotyphodium lolii is associated with host-specific adaptations, particularly in response to abiotic and biotic stresses. Knowledge of the origin of the symbiosis and the contribution of endophyte genotype to host phenotypic variation is currently limited. Simple sequence repeat (SSR) markers were used to assess endophyte genetic diversity in a globally distributed collection of perennial ryegrass accessions. Consistent in planta detection was achieved with 18 of 22 SSR markers (primer pairs). Endophytes representing as many as four different taxa were detected in 42 accessions from 20 different countries, N. lolii being predominant. A total of 33 unique N. lolii genotypes were discriminated, of which 29 clustered into three major groups with limited within-group variation. The three major N. lolii groups were associated with distinct perennial ryegrass chloroplast haplotypes. The alkaloid profiles of accessions were apparently associated with the presence of specific N. lolii genotypes. Genotypic analysis provides a powerful method for genetic dissection of the grass-endophyte interaction and prediction of phenotypic variation based on genotypic variation. [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]