Mol Breeding (2014) 34:1229–1243 DOI 10.1007/s11032-014-0112-5
Association mapping of grain hardness, polyphenol oxidase, total phenolics, amylose content, and b-glucan in US barley breeding germplasm Mohsen Mohammadi • Jeffrey B. Endelman • Sindhu Nair • Shiaoman Chao • Stephen S. Jones • Gary J. Muehlbauer • Steven E. Ullrich • Byung-Kee Baik • Mitchell L. Wise • Kevin P. Smith
Received: 24 October 2013 / Accepted: 8 May 2014 / Published online: 16 May 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract A renewed interest in breeding barley specifically for food end-uses is being driven by increased consumer interest in healthier foods. We conducted association mapping on physicochemical properties of barley that play a role in food quality and processing including grain hardness, polyphenol oxidase activity, total phenolics, amylose content, and bglucan. We used 3,069 elite two-row and six-row spring barley breeding lines from eight US breeding programs and 2,041 SNP markers for association mapping. Marker–trait associations were identified using a mixed model that incorporated population structure and kinship. We detected two previously identified QTL for grain hardness on chromosome 2H in the telomeric region of 5H along with two novel regions on 4H and 6H. For amylose content, we Electronic supplementary material The online version of this article (doi:10.1007/s11032-014-0112-5) contains supplementary material, which is available to authorized users. M. Mohammadi G. J. Muehlbauer K. P. Smith (&) Department of Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN 55108-6026, USA e-mail:
[email protected] J. B. Endelman Department of Horticulture, University of Wisconsin, Madison, WI 53706, USA S. Nair S. S. Jones S. E. Ullrich Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
detected marker–trait associations on 7H from 0.63 to 30 cM. We detected four regions on chromosomes 1H, 2H, 3H, and 4H associated with polyphenol oxidase activity. The chromosome 2H region co-localized with the two previously mapped polyphenol oxidase genes PPO1 and PPO2, and the regions on chromosomes 1H, 3H, and 4H QTL were novel. For total phenolics, we identified three significant regions on 3H, 4H, and 5H. Two regions on 2H and 7H were associated with b-glucan. Both previously identified and novel QTL are segregating in elite US breeding germplasm. Only three of the 24 SNPs that were associated with traits using either the two-row or six-row mapping panel were identified in both panels. Nine SNPs were detected in the individual two-row or six-row panels that were not detected in the analysis using the complete panel and accounting for population structure. The distribution of favorable alleles at these loci that underpin food quality across the breeding S. Chao Biosciences Research Lab, USDA-ARS, Fargo, ND 58105, USA B.-K. Baik Soft Wheat Quality Lab, USDA-ARS, Wooster, OH 44691, USA M. L. Wise Cereal Crops Research, USDA-ARS, Madison, WI 53726, USA
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programs suggests several strategies to use markers to improve barley for food uses. Keywords Association genetics Food quality QTL Barley
Introduction Barley (Hordeum vulgare L.) is predominantly used in the animal feed and malting industries in North America and Europe, whereas it has historically been a major food crop in many African, Asian, and European cultures. With the recent recognition of health-promoting benefits of barley b-glucan (McIntosh et al. 1991; Bourdon et al. 1999; Behall et al. 2004; Shimizu et al. 2008) by the US Food and Drug Administration (National Barley Foods Council, 2011 available at http://www.barleyfoods.org/BarleyFactsFDA.pdf), it is anticipated that the development of barley-based food products and diversification of the uses of barley in food will continue to increase in the future. This may include incorporation of barley flour in bread, noodles, pasta, pastries, and extruded snacks. A number of grain traits in barley are important for food processing and human health. The resistance of the kernel to deformation is known as kernel hardness. Hard kernels (harder endosperm texture) are more resistant to destruction, whereas soft kernels (softer endosperm texture) are easily damaged. Kernel hardness plays a significant role in food processing of barley grain, affecting pearling properties and flour particle size (Nair et al. 2011). With hard kernels, the loss of endosperm is minimized during dehulling and pearling; however, it requires higher milling energy compared to soft kernels (Baik and Ullrich 2008). Endosperm texture or kernel hardness is a well recognized and important end-use quality trait of wheat (Bhave and Morris 2008). The major known hardness locus complex in barley is located on the short arm of chromosome 5H (Rouve`s et al. 1996) and is comprised of multiple genes: Hinb-1, Hinb-2, Hina, and Gsp (Caldwell et al. 2004). Dark discoloration of barley-based food products, primarily due to polyphenol oxidase (PPO) activity and total phenolic compound content, negatively affects consumer acceptability and therefore limits the utilization of barley in some food formulations. Bright
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white or light yellow color is generally preferred for cereal products, and deviations from the expected appearance, such as a gray or dull color or black specks, can arise from enzymatic reactions with phenolic compounds. Enzymatic browning mainly occurs when the PPO enzyme converts phenolic compounds to oquinones, which in turn react with other phenolic compounds or amino acids to produce dark melanin pigments (Baik and Ullrich 2008). Consequently, both the amount and type of phenolic compounds present in the grain, as well as the endogenous levels of PPO activity, influence discoloration and consumer acceptance (QuindeAxtell et al. 2004). Abrasion of the grain, heat inactivation of PPO, exclusion of oxygen, and use of a reducing agent can reduce the discoloration of barley-based food products (QuindeAxtell et al. 2006). Taketa et al. (2010) mapped the PPO1 and PPO2 genes on the long arm of chromosome 2H flanked by molecular markers MGW882 and Bmac0415. Thus, marker-assisted selection for lower PPO activity and phenolic compound content could be combined with other strategies (grain abrasion, heat inactivation, exclusion of oxygen, and the use of a reducing agent) to significantly reduce the polyphenol content. Despite the fact that phenolic compounds result in discoloration of food products and have negative impact on consumer preferences, it is well known that phenolic compounds are bioactive components with high health-promoting activity (Dykes and Rooney 2007). Phenolic compounds in cereal grains are mainly localized in the pericarp, which may be used in food products to increase dietary fiber levels and nutraceutical properties (Dykes and Rooney 2007). The largest group of antioxidants in cereal grains are phenolic acids which are available in the forms of salicylic, phydroxybenzoic, vanillic, protocatechuic, p-coumaric, syringic, ferulic and sinapic acids and have been identified in barley grains (Yadav et al. 2000). Two other grain characteristics that are of nutraceutical value and significantly affect processing and product quality are the amylose content of the barley starch and the b-glucan content in barley grains. Starch is primarily comprised of amylose and amylopectin. Amylose is a linear polymer of D-glucans, while amylopectin has many linked glucoside chains attached to the main polymer. Amylose, which is the less-branched component, constitutes about 15–30 % of the starch and contributes to its nutraceutical value (Hu et al. 2010). Research indicates that food products
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using high-amylose grains lower blood glucose, insulin levels, triglycerides, and cholesterol in animals and humans (Behall et al. 1989). Amylose content also influences the viscosity, swelling power, and many other textural properties of barley food products (Zheng and Sosulski 1998). Amylose content is controlled by the amylose (amo1) gene located on chromosome 1HS and the waxy gene located on chromosome 7HS (Schondelmaier et al. 1992; Rohde et al. 1988). Although an undesirable trait for brewing purposes (Ullrich et al. 1986), high b-glucan content in barley for food use is desirable due to its cholesterollowering properties through binding to serum cholesterol and limiting its absorption. This would result in lowering total plasma cholesterol levels in humans and animals and decreasing the risk of cardiovascular disease (Baik and Ullrich 2008). Preferred characteristics for food and pearling purposes are uniform size, bright yellow color, plump shape, thin hull, and medium hard kernels (Nair et al. 2011). A renewed interest in breeding barley cultivars specifically for food applications is being driven by increased consumer interest in healthier foods. This study is a part of the Barley Coordinated Agricultural Project (CAP) where a consortium of US barley breeding programs and researchers developed a collaborative infrastructure and coordinated experiments to conduct association mapping on a wide range of important traits using elite breeding germplasm. Our principal objective was to understand the genetic determinants and allelic diversity of loci involved in physicochemical properties and functional health-promoting factors in barley germplasm developed in US breeding programs. We report here on the genetic mapping of five food quality traits, including kernel hardness, total phenolics, PPO activity, amylose content, and b-glucan content in US spring barley germplasm. Characterizing the genetic architecture of these traits and distribution of favorable alleles within the US barley breeding germplasm will provide the foundation for developing breeding strategies for food barley.
Materials and methods Germplasm and data structure Our mapping panel consisted of 3,069 elite spring barley breeding lines from eight US breeding
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programs: Washington State University (WA), USDA-ARS in Aberdeen (AB), North Dakota State University two-row and six-row (N2 and N6), Montana State University (MT), University of Minnesota (MN), Busch Agricultural Resources Inc.(BA), and Utah State University (UT). The breeding programs WA, AB, N2, and MT contributed mostly two-row breeding lines, while MN, UT, and N6 contributed mostly six-row lines. The BA program contributed a mix of two-row and six-row lines. Lines were submitted over the course of 4 years, with each line characterized in 1 year. In 2006, grain samples for analysis were obtained from a single replicate nursery in Bozeman, MT, while in 2007–2009 the samples were obtained from similar nurseries in Crookston, MN. While each breeding line was represented by a single unreplicate plot, the large mapping panel allowed for substantial replication of alleles to facilitate identification of marker–trait associations (Knapp and Bridges 1990). Grain harvested from these nurseries was sent to Washington State University for food quality analysis. Total phenolics and amylose content were measured from samples collected 2006–2008 (2,304 lines), whereas polyphenol oxidase, grain hardness, and b-glucan were measured from samples collected in all years (3,069 lines). Genotyping was conducted at the USDA-ARS facility in Fargo, ND using two 1,536 SNP Golden Gate assays (Close et al. 2009). We used the consensus map developed by Mun˜oz-Amatriaı´n et al. (2011).
Food quality measurements Kernel hardness was measured using a single kernel characterization system SKCS 4100 (Perten Instruments, Springfield, IL). The SKCS system provides a direct measure of grain hardness by crushing individual kernels and using algorithms based on force– deformation profile (Gaines et al. 1996). To facilitate the vacuum picking and loading of individual kernels in the SKCS, kernels were first abraded for 80s to remove the tightly attached hull using the Tangential Abrasive Dehulling Device (TADD) (Venables Machine Works Ltd., Saskatoon, Canada). For each line, 100 kernels were evaluated using the SKCS 4100, and their values were averaged. Amylose content of the barley starch was determined using the iodine colorimetric method of Williams et al. (1970).
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To measure total phenolics, PPO activity, and amylose content, dehulled barley kernels were ground using a Cyclone sample mill (UDY Corporation, Fort Collins, CO) fitted with a 0.5-mm screen. Total phenolics were determined using a modified method of Bendelow (1977) and QuindeAxtell et al. (2004). Whole barley flour (0.10–0.12 g) was extracted with 30 % (v/v) dimethylformamide (2 mL) for 45 min by shaking at room temperature and later centrifuged at 1,000 g for 10 min. Supernatant (1 mL) was mixed with 1 mL of 15 % (w/v) ammonium hydroxide, 0.5 mL 2 % (w/v) potassium ferricyanide [K3Fe(CN)6], and 0.5 mL (w/v) 2 % 4-aminoantipyrine. Absorbance was read at 505 nm after 30 min incubation at room temperature and compared with a gallic acid standard; results are reported in % gallic acid equivalents. PPO activity was determined using a modified protocol of American Association of Cereal Chemists International (AACC) Approved Method 22-85.01 (2010). Whole barley flour (0.20–0.22 g) was reacted with 1.5 ml L-dihydroxyphenylalanine (L-DOPA) for 1 h by rotating on a Labquake rotator at room temperature, followed by centrifugation at 5,000g for 10 min. Absorbance was read at 475 nm with L-DOPA as the blank. For measurement of b-glucan content of barley, we used two methods that both employed calcofluor fluorescence (Wood and Weisz 1984). The b-glucan content for breeding lines grown in 2006 and 2007 was determined by flow injection analysis, while a plate reader to measure fluorescence was used for lines grown in 2008 and 2009. Approximately 1–3 grams of dry, de-hulled grain was ground in a Retsch ZM-1 centrifugal mill grinder (Haan, Germany) to pass a 0.5-mm sieve. Duplicate aliquots were carefully weighed and the values recorded (approximately 50 mg) and placed in a 50-mL polyethylene screwcap centrifuge tube with a small stir bar and 10.0 mL MilliQ de-ionized H2O. The tube was swirled to ensure sample suspension, capped and put into a boiling water bath atop a heater/stir plate and heated for 60 min with constant stirring. The samples were then placed under cold running water for 10-15 min; then, to each tube, 10 mL of 0.075 M sulfuric acid was added, and they were returned to the boiling water bath for an additional 12 min with constant stirring. In the FIA analysis method, 1.4 ml aliquots were transferred to microfuge tubes (Eppendorf, Fremont CA.) and centrifuged at 8800 g for 12 min, and then, 0.5 mL of
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the supernatant transferred into glass test tubes with 2.5 mL of 0.2 M glycine buffer (pH 10.0) for analysis. A 40 lL aliquot of the test solution was injected into a stream of 0.2 M glycine buffer, pH 10.0; this stream was merged into a stream of 25 mg/L calcofluor (Fluorescent Brightener 28, Sigma, St. Louis, MO.). The fluorescence of the resulting product was measured in a Shimadzu RF-535 flow-through spectrofluorometer (Columbia, MD.) with excitation at 350 nm and emission detection at 420 nm. Peak areas were integrated using a Shimadzu R3A integrator and compared to a standard curve established using known concentrations of b-glucan (Megazyme, Wicklow, Ireland). In the plate reader analysis method, aliquots (100 lL) from the ground barley sulfuric acid solutions were diluted with 100 lL of a glycine solution (0.2 M, pH 10.0). Ten microliters aliquots of these diluted samples were placed in wells of a flat clearbottom black 96-well plate (Corning Costar 96 well, Corning, NY) with 90 lL of a glycine solution (0.2 M, pH 10.0) and 100 lL of a 25 mg/L calcofluor solution in 0.2 M glycine (pH 10.0) containing Triton X (100 lL of a 10 % Triton X solution in H2O per 100 mL of calcofluor solution). Each sample was replicated in triplicate in the plate and the plate mixed for 15 s with an Eppendorf MixMate (Fremont, CA) before being read with a TECAN Genios plate reader (Mannedorf, Switzerland) equipped with filters for excitation at 360 nm and emission at 420 nm. A standard curve was developed using Megazyme bglucan standards. The b-glucan content determined with this method has been shown to be essentially the same as those determined by FIA (Schmitt and Wise 2009). Association mapping We performed association mapping for (1) the entire set of lines across years, (2) all six-row lines and all two-row lines across years, and (3) for individual years. After eliminating monomorphic and unmapped markers, and those with more than 10 % missing data, 2,401 remained for estimating both kinship and the level of population structure. Because missing genotype data comprised only 0.3 % of the entire dataset, we used the population mean to impute the untyped genotypes. Rutkoski et al. (2013) reported that when the fraction of missing genotypes is less than 20 %
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missing data, imputation by using population mean is sufficient. For association mapping, we considered a minimum number of 30 individuals for accurate phenotype estimation of the rare variants. For each association panel, we used the minimum number of rare variants (n = 30) to determine an appropriate minor allele frequency to filter out markers for analysis. For instance, polyphenol oxidase was measured on 3,069 lines in our across-year analysis. For this dataset, we used a minor allele frequency of MAF = 1 % (30/3,069). Individual year datasets consisted of nearly 730 individuals for which we used a MAF filter of 4 % (*30/730). We used the same principle for the six-row and two-row association mapping panels. We used P ? K model to account for population structure with the kinship effect (K) plus the first two principal components (P) of the genotype matrix (Endelman 2011). Our association genetics model for individual years included markers and kinship (the first and the second principal components) as fixed effects and polygenic effect of line and the error variance as random effects. In the multi-year analyses (i.e., the entire set, all six-row lines, and all two-row lines), we also included the year as a fixed effect in the association model. The sets of breeding lines from each year of the Barley CAP study comprise a representative sample of the diversity within each breeding program. We do not expect substantial genetic differences from year to year in each program; therefore, inclusion of a fixed effect for year in the association analysis should remove any possible year effects. Association mapping was conducted in R 3.0.1 (R Development Core Team 2013) using the function GWAS in package rrBLUP (Endelman 2011), which is based on the algorithm of Kang et al. (2010). QTL mapping and association analysis is used to identify the effects of loci or alleles on traits of interest. Therefore, the key concern in association genetics is to replicate alleles in studies dealing with only one replication of genotypes (Knapp and Bridges 1990). Statistical significance of QTL was assessed at a false discovery rate (FDR) of 5 %, using the R package q value (Storey and Tibshirani, 2003). Population structure was assessed using the 2,041 markers that survived quality control measures by using the first and the second principal components. Linkage disequilibrium in a window of up to 50 cM was measured by using TASSEL 3.0 (Bradbury et al. 2007).
1233 Table 1 Descriptive statistics of the combined dataset for the five food quality traits is shown Traits
Min
Max
Median
Mean
SD
Grain hardness (SKCS)
26
95.9
64.4
64.1
11.2
b-Glucan (%)
2.4
9.2
4.8
4.9
0.8
Polyphenol oxidase (Abs. at 475)
0.05
1.98
0.31
0.39
0.23
Amylose content (%)
0
Total phenolics (% gallic acid equivalents)
0.13
29.2 0.47
23.2 0.25
22.9 0.27
3.12 0.05
SD standard deviation
SNP annotation For markers that showed significant associations, we conducted a search of genomic sequences from the Morex Barley BACs version 2 searchable database available at http://www.harvest-web.org/ and identified candidate genes that are in close proximity with the SNP markers.
Results and discussion Phenotypic distributions of food quality traits We evaluated the phenotypic distribution of five food quality traits in two-row and six-row spring barley breeding lines from eight US spring breeding programs in three (total phenolics and amylose content) or 4 years (polyphenol oxidase, grain hardness, and bglucan) (Fig. S1). The distribution for PPO activity (in absorbance units) was right-skewed, with range 0.05–1.98 and mean 0.39 (SD 0.23). Likewise, total phenolic demonstrated a right-skewed bimodal distribution, with range 0.13–0.47 and mean 0.27 (SD 0.05). QuindeAxtell et al. (2004) reported a mean of 0.15 % for 10 proanthocyanidin-containing varieties grown in Pullman, WA. The SKCS values across 4 years of the study showed averaged 64.05 and ranged from 26.0 to 95.9 (Table 1). The range and average observed for SKCS hardness index in our study was comparable with the range (30.1–91.2) and the average (61.8) reported by Nair et al. (2010) using a subset of the same germplasm. This range was similar to the range of 35–89 reported by Fox et al. (2007) for a collection
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0.04
6 5 Grain hardness
0.02
3
0.00
1
2
0 AB BA MN MT N2 N6 UT WA
10 8 -glucan
-0.04
-0.02
PC2
4
6 4
-0.02
-0.01
0.00
0.01
0.02
PC1
0 35 30
Polyphenol oxidase
Fig. 1 Population structure visualized by scatter plot of the first (PC1) and the second principle components (PC2). Six-row and two-row breeding lines are represented by open and closed circles, respectively. Each breeding program is represented by a different color. Open and closed circles with the same colors represent the two-row and six-row germplasm contributed by UT and AB breeding programs. (Color figure online)
2
25 25 15 10 5 0 40
Amylose content
of Australian breeding lines and commercial varieties. For amylose content, the three-year data averaged 22.9 % (SD 3.12), with range 0–29.2 (Table 1). b-Glucan content ranged from 2.4 to 9.2 % and averaged 4.9 % (SD 0.77 %) across all the 4 years in our study (Table 1).
30 20
Population structure 10
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0 Total phenolics
We characterized population structure using PCA and found that the first and second principal components accounted for 31.4 and 5.3 % of the total variance, respectively, whereas the remaining principal components each accounted for less than 3.6 % of the variation. The first principal component discriminated between six-row and two-row breeding lines (Fig. 1). The breeding lines with negative values for the first principal component are predominantly six-row (MN, N6, and some of the UT and BA lines), while those with positive values are predominantly two-row (MT, WA, N2, and some of the UT and AB lines). The second principal component mostly discriminated breeding lines among the different two-row programs. The population structure described here is largely consistent with previous characterization of
8 6 4 2 0
1
2
3
4
5
6
7
Chromosome
Fig. 2 Manhattan plots of genome-wide association mapping of grain hardness, b-glucan, polyphenol oxidase, amylose content, and total phenolic content using P ? K model. The horizontal line is the 5 % FDR cutoff
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population structure in the barley CAP breeding lines (Hamblin et al. 2010; Wang et al. 2012; Zhou et al. 2012). To further investigate the effect of this population structure on association mapping, we conducted the analysis within the major subpopulations as described below. The genome-wide average linkage disequilibrium for adjacent markers in the entire sample was 0.4 and stayed well above 0.15 for markers 10 cM apart (Fig. S2). This is similar to other estimates of elite barley germplasm and suggests that this marker density is sufficient to conduct genomewide association analyses (Hamblin et al. 2010; Massman et al. 2011).
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Fig. 3 Comparative analysis of significant number of SNPs detected in the entire sample, the two-row panel, and the six-row panel. The data are the count of SNPs with -logP [ 4 detected only in individual association mapping panels (e.g., detected only in the entire sample but not in six-row nor in two-row), any two-way combination of panels (e.g., detected in two-row and in six-row), or shared among all panels
Association mapping Marker–trait associations for the five traits detected in the multi-year analysis using the P ? K model are presented in Fig. 2. The detection threshold corresponds to a false discovery rate of 0.05 (Storey and Tibshirani, 2003). The marker significance levels (-log P) based on the P ? K model for each trait are given in Supplemental Table S1. The results of the six-row and the two-row barley association panels and the individual CAP year analysis are also reported in Supplemental Table S1, and Manhattan plots comparing the multi-year analysis, six-row and two-row panels, and individual years are given in the Supplemental Fig. S3. In general, the analyses of individual years support our multi-year analysis. In 19 out of 29 cases, when we detected a signal in the multi-year analysis, it was also detected in at least one of the individual year analyses. Since the primary population structure in our germplasm was explained by row type (Fig. 1), we analyzed two-row and six-row panels separately as well as combined in the entire set. Only three out of 29 markers detected in this study were detected in both the two-row and six-row mapping panels (Fig. 3). In many cases, this could be explained by the number of rare variant dropping below our cutoff in either the two-row or six-row panel. Nevertheless, this highlights the fact that segregation for traits important for food quality is primarily occurring at different loci for the two-row and six-row elite breeding germplasm in the USA. Combining two-row and six-row panels should increase the number of QTL detected due to increased sample size. Of the 20 markers detected using the entire panel, 15 were detected in either the
two-row or six-row panel. In contrast, of the 29 markers detected in all three panels, only nine were not detected in the combined panel. However, for all of these nine markers, either another marker in the same chromosome position was significant or had a moderate signal (-log P [ 1.3 corresponding to a p value \0.05; Table S1). Thus, combing the two-row and sixrow panels was either neutral or beneficial to detecting QTL in most cases. The one possible exception is for traits correlated to row type (see discussion of grain hardness and Vrs1 below). A discussion of the genetic analysis for each trait is given below based on the multi-year analysis presented in Fig. 2, Fig. S3, and Table S1. Grain hardness We observed highly significant marker–trait associations in the multi-year analysis for grain hardness on chromosomes 4H, 6H, and 7H (Fig. 2). The signal observed on 6H (12_10811) was observed in the sixrow panel (-log P = 9.3) and weakly (-log P = 3.7) in the two-row panel. 12_10811 is located at 49.67 cM and resides in a region previously identified for grain protein content QGpc.DiMo-6H (Oziel et al., 1996; See et al. 2002). In a previous study, it was shown that protein concentration was positively correlated with grain hardness in wheat (Nelson et al. 2006). In more genetically relevant spring barley germplasm, chromosome 6H was shown to contain a QTL for grain protein content, which resides on barley chromosome 6H near ABG458 and HVM74 (See et al. 2002). We extracted the relative locations of the SNP markers and pre-SNP historical markers from the supplementary
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tables 1 and 8 provided by Mun˜oz-Amatriaı´n et al. (2011). The markers ABG458 and HVM74 define a genomic region from 43 to 59 cM on chromosome 6H, spanning the location of SNP 12_10811. The marker 12_20227 located at 8.45 cM on chromosome 7H showed the second strongest association with grain hardness (Fig. 2). This marker had a –log P value of 4.13 in the two-row germplasm panel, but was omitted from association mapping analysis of six-row germplasm because of its insufficient minor allele frequency. The marker 11_10738 residing at 19.45 cM on chromosome 4H showed the third strongest association with grain hardness (Fig. 2). This marker was detected with moderate significance in 2006 and 2007 and was weakly significant in 2008 (Table S1). In previous work, the vrs1 allele (Vrs1.a3-1) on 2H was associated with softer endosperm texture in barley (Turuspekov et al. 2008b). The Vrs1 locus is responsible for the two-row or six-row head phenotype in barley (Leonard 1942). The dominant Vrs1 and the recessive vrs1 alleles control two-row and six-row head phenotypes, respectively (Komatsuda et al. 2007). In our study using the complete panel, we observed a subthreshold association near Vrs1 for SNP 11_20340 (-log P 3.98). This marker was not significant in the six-row panels and was below the MAF threshold in the two-row panel. Since the population structure in our mapping panel was primarily explained by row-type (Fig. 1) and previous work suggests a correlation between population structure and the phenotype of grain hardness, it is possible that accounting for the row-type subpopulation structure in our model reduced the power to detect this region. The strongest signal at this locus was observed in 2009 (-log P of 3.99). The SNP marker 11_20340 (MAF = 0.45) was highly correlated with row type. 95.4 % of the individuals with allele G for 11_20340 at this locus were two-row lines, and 98.7 % of the individuals with allele T were six-row germplasm. The mean difference between genotypes GG and TT for this SNP was about five SKCS. In contrast to the observation made by Turuspekov et al. (2008a), the harder endosperm in our germplasm was associated with the marker for the six-row allele. Our observation was in agreement with the data reported by Nair et al. (2010). They reported average SKCS hardness index values of 61.3 and 65.3 for two-row and six-row germplasm, respectively.
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Surprisingly, we did not observe significant associations with grain hardness in the multi-year analysis near the Ha locus on 5H (Caldwell et al. 2004). This region spans SNP markers 11_20226, 12_30979, 12_30977, and 12_30001. The marker 12_30977 is contained on BAC 0641K21 located on the telomeric region of 5H. This BAC includes the genomic sequences of the gene for grain softness protein (Gsp-1), hordoindoline-A (HINA), and chalcone synthase. The Marker 12_30001 is contained on BAC 0222J19 which contains a H-ATPase gene. The gene content (i.e., Gsp-1, HINA, chalcone synthase, and HATPase) in the genomic sequence of Ha locus in barley is also supported by Caldwell et al. (2004). However, Charles et al. (2009) indicated that the chalcone synthase gene on this region is only present in barley and not in the synteneous and collinear genomic regions of any other grass species. Individual year analyses revealed a significant association (-log P of 4.53) for 12_30977 in 2006 and weaker signals in subsequent years in this region (Fig. S4). The minor allele frequencies for markers in this region ([0.35) should not have limited the power to detect associations suggesting that the effect of alleles segregating in this region is small. While variation at the Ha locus can influence barley texture (Beecher et al. 2002b; Turuspekov et al. 2008a), many other QTL have been identified (Fox et al. 2007; Walker et al. 2011), and the inheritance appears to be more complex in barley compared to wheat. Fox et al. (2007) analyzed the grain hardness variation in a relatively small size (N = 94) doubled haploid bi-parental mapping population generated by crossing a French malting barley ‘Patty’ and an Australian malting variety ‘Tallon’ and identified markers significantly associated with grain hardness on chromosomes 2H, 3H, 5H, 6H, and 7H. Three of these regions corresponded to QTL that we identified. The marker we identified on 5H (12_30977) at 4.15 cM mapped near bxPb-5389 located at 3.2 cM on 5H. Our QTLs located on 6H and 2H, however, were about 20–30 cM apart from the genomic regions previously reported (XP12M49P224 at 120 cM on 2H and bPb-3230 at 63 cM on 6H). In another study of grain hardness in a cross of ‘Arapiles’ with ‘Franklin,’ QTL were identified on chromosomes 1H, 2H, 3H, 6H, and 7H (Walker et al. 2011). None of these QTLs were identified in our study.
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In wheat, kernel hardness is the result of interaction between lipid-binding proteins rich in tryptophan and cysteine residues (Giroux and Morris 1997) known as puroindolines and lipids, where the tryptophan domains of puroindolines interact with lipids located on the surface of starch granules (Bhave and Morris 2008). Functional complementation tests of puroindolines have proven that they affect grain hardness in wheat because expression of the dominant wild-type allele of Pinb introduced a soft endosperm to a hard wheat cultivar (Beecher et al. 2002a). It has been also shown that the kernels of wheat chromosomal additions lines generated through addition of the 5H chromosomes from Hordeum vulgare, Hordeum vulgare ssp. spontaneum, or Hordeum chilense are significantly softer than those of the corresponding wheat parents (Yanaka et al. 2011). We detected a signal just below the significance threshold for grain hardness on 1H at 12_11011 (-log P 3.95). This marker was highly significant (-log P 6.4) in 2009 and reasonably strong (-log P 3.36) in 2007 (Fig. S4 and Table S1). Berger et al. (2013) studied winter barley germplasm that was a component of the Barley CAP and did not identify any QTLs controlling grain hardness. Amylose content We identified several markers associated with amylose content on chromosome 7H (Fig. 2). The short arm of chromosome 7H included seven markers, i.e., 12_30472, 12_20201, 11_20242, 11_11495, 11_20755, 11_20722, and 12_30702 spanning from 0.63 to 27.09 cM (Table S1). 12_20201 at 2.13 cM, 11_11495 at 6.79 cM, and 11_20755 at 10.57 cM were the first (-log P 39.52), the second (-log P 9.49), and the third (-log P 7.32) strongest signals detected in this region, respectively. The SNP 11_20755 is located within 3 cM of SNP 12_30902 (POPA3_0902) which was derived from the promoter sequence of the granule-bound starch synthase (GBSS) gene (Rohde et al. 1988). Our genotype dataset did not contain the SNP 12_30902 to assess its effect on amylose content. We detected another marker (12_30301) significantly associated with amylose content and located at 94.34 cM on the long arm of chromosome 7H (Table S1). Association mapping using the individual year data showed a strong signal at the SNP 11_11495 (-log P = 11.17) in 2008 and no significant associations in 2006 and 2007. There were 23
1237
lines in the 2008 dataset mostly from the WA program that were distinctly low in amylose (AC \ 10 %) and likely accounted for the significant association detected in the 2008 analysis (see Supplemental Fig. 1). The rowtype-specific analyses have shown that these amylose content QTLs are specific to only two-row germplasm. In a separate analysis on the germplasm from Virginia, which are all six-row, no QTL was detected for amylose content (Berger et al. 2013). Bi-parental QTL mapping using 142 recombinant inbred lines (RILs) from a cross between the varieties Azhul (waxy) with high BG and Falcon (non-waxy) have shown that beta-glucan levels ranged from 3.68 % in Falcon to 8.60 % in Azhul, and amylose levels ranged from 7.84 % in Azhul to 22.43 % in Falcon, indicating a clear inverse relationship between the two traits (Islamovic et al. 2013). In contrast, when we eliminated the genotypes with the extreme low amylose content, we did not observe any positive or negative relationship between amylose content and beta-glucan in over 2,200 contemporary barley breeding lines. Islamovic et al. (2013) identified major QTLs contributing to high BG on chromosomes 4H, 5H, and 7H and QTLs associated with amylose on chromosomes 1H, 5H and 7H. The genomic region we identified for amylose content on the short arm of chromosome 7H (from 0.63 to 27.09 cM) mapped in the same region as the previously reported QTL on 7H in the Azhul 9 Falcon mapping population by Islamovic et al. (2013). However, the QTL associated with 12_30301 and located at 94.34 cM on chromosome 7H which is specific to the two-row germplasm was not previously reported. This marker was not included for association mapping of the six-row germplasm due to low minor allele frequency. Polyphenol oxidase We identified one very large effect (-log P = 32.85) QTL for PPO at 124.98 cM on 2H (11_21184; Fig. 2). This corresponds to the region containing PPO1 and PPO2 mapped by Taketa et al. (2010) and Berger et al. (2013) based on common markers found in the map published by Szucs et al. (2009). Just about 5 cM distal to this large effect QTL associated with 11_21184, we identified a rare variant at 12_21396 (129.30 cM) which showed highly significant association with PPO on chromosome 2H (Table S1). In addition, we detected associations in four other
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1238
regions on chromosomes 1H (11_20550 at 90.31 cM), 3H (12_31298 at 40.99 cM and 11_11502 at 67.86 cM), and 4H (11_11199 at 23.96 cM) that have not previously been associated with polyphenol oxidase activity. These four associations were only detected in the complete panel. Marker 11_11066 on chromosome 4H at 134.6 cM was only detected in the two-row panel. Total phenolics For total phenolic compounds, we detected ten regions with –log P values greater than the 5 % FDR threshold (Fig. 2). The four most significant regions are on 3H, 4H, 5H, and 7H. The region on 3H is associated with markers 11_11258 and 11_11502. The second region is a 10-cM region spanned by markers 11_21481, 11_20363, 11_20472, 11_20412, 11_11207, and 11_11207 on 4H with 11_20363 at 62.81 cM being the most significant (-log P 8.3) marker in this region. The third region is marked by 11_21480 on 5H (Fig. 2 and Table S1), which showed a –log P of 4.9 in the entire sample and 2.2 and 3.5 in six-row and two-row panels, respectively. The QTL at 11_10256 at 79.08 cM on chromosome 7H was found associated with total phenolic in the entire sample and the tworow panel but not in the six-row panel (Table S1). All of the markers mentioned, except 7H, detected significant associations in only the complete panel suggesting that combining two-row and six-row lines resulted in increased power. Individual year analyses revealed a highly similar signal profile in 2006, whereas the signals were weaker in 2007 and 2008 (Fig. S4). b-Glucan content For b-glucan content, we detected seven regions on chromosomes 2H, 3H, 4H, 5H, and 7H (Fig. 2). The most significant region is located on the short arm of chromosome 7H at 12_20201 (2.13 cM) and 12_20227 (8.45 cM) and was also detected with the two-row panel. We detected 12_31211 (83.42 cM) also on chromosome 7H which is located in a region previously associated with malt b-glucan (QBgnm.StMo-7H.2; Han et al., 1995). Other associations we identified included 11_10326 at 4.72 cM on 2H, 11_20866 at 63.94 cM on 3H, 12_31156 at 56.22 cM on 4H, and 12_20297 at 63.33 cM on 5H. In
123
Mol Breeding (2014) 34:1229–1243
our six-row association panel, we identified the SNP 12_30102 at 172.92 cM on 2H significantly associated with b-glucan content. This SNP was not identified in two-row germplasm nor in the entire sample. Berger et al. (2013) did not identify any significant QTL controlling b-glucan content in the Virginia germplasm. The genomic sequence search for 12_30589 (less than 2 cM apart from 12_31211 for which we identified significant association with b-glucan content) returned the BACs 0428N06 containing the glucan endo-1,3-b-glucosidase 14 gene. In the starch and sucrose metabolism pathway, glucan endo-1,3-bglucosidase is responsible for successive hydrolysis of b-D-glucose units from the non-reducing ends of (1-[3)-b-D-glucans.
Discussion This analysis of a comprehensive sample of US spring barley breeding germplasm provides substantial insight into the genetic architecture of important food barley traits. Because we used contemporary six-row and two-row breeding material, the results point to loci that are segregating within elite germplasm, making them more useful for breeding. To further characterize the segregation at key loci, we determined the frequency of important markers in each of the breeding programs (Table 2). In addition, we determined which alleles were associated with high and low values for each trait to help characterize the distribution of favorable alleles across the breeding programs. Looking at allele frequencies across programs, we can see wide variation from cases where an allele is completely fixed within a program to cases where allele frequencies are near 0.5. There are also numerous cases where the patterns of allele frequencies are similar among programs. A striking example is the case of the MN and N6 programs. These two programs are the most closely related as they serve a similar geographic region, focus on six-row malting barley, and have periodically exchanged germplasm throughout their breeding histories. We also observed differences in allele frequency pattern among the various traits. For PPO, most of the markers are skewed toward one allele in each of the programs. The notable exception is UT. Interestingly, the MN and N6 programs are heavily skewed toward the low PPO alleles, while the other programs are primarily skewed toward the high PPO
Polyphenol oxidase
Beta-glucan
Grain hardness
Traits
3H
2H
11_21184
11_11502
1H
11_20550
7H
12_31211
6H
12_10811
2H
5H
12_30977
11_10326
4H
Chr
11_10738
SNP
67.86
124.29
0.31
83.42
4.72
49.67
4.15
19.45
Position
4.09
32.85
4.46
4.53
4.42
5.33
3.09
4.16
-logP
2 13
H N
High PPO allele
A
H N
C
Low PPO allele
N
0
1 0
299
83
0
H G
0
C
High PPO allele
2
T
High PPO allele Low PPO allele
381
117
A
Low BG allele
0 251
N G
0
H High BG allele
326 57
0
N C T
0
H
High BG allele Low BG allele
280
G
Low GH allele
103
0
N A
0
H High GH allele
371
C
Low GH allele
0
N 12
H T
0
T
High GH allele
351
C
High GH allele
32
MT2 383
Low GH allele
Total individuals
0
1 0
342
40
0
0
24
359
0
6
126
251
0
1
343 39
1
0
382
0
0
1
358
24
2
4
347
30
WA2 383
5
6 1
319
48
1
0
1
372
0
2
351
21
1
9
185 179
4
0
97
273
0
5
337
32
1
6
270
97
N22 374
16
1 0
329
51
0
0
139
242
5
4
198
174
0
1
282 98
1
0
311
69
0
0
362
19
1
3
255
122
AB2 381
227
2 32
263
85
18
3
122
239
86
4
167
125
11
2
285 84
2
0
335
45
0
0
366
16
21
4
175
182
UT2,6 382
0
0 0
355
24
0
0
147
232
1
1
212
165
0
0
283 96
0
0
379
0
1
0
353
25
0
1
222
156
BA2,6 379
0
0 1
383
0
0
0
384
0
0
0
9
375
1
6
226 151
0
0
384
0
0
0
360
24
0
1
11
372
MN6 384
0
0 2
379
3
1
1
379
3
3
5
84
292
0
8
172 204
3
0
360
21
3
0
341
40
1
0
1
382
N66 384
Table 2 Distribution of individuals homozygous in SNP alleles and individuals with heterozygous genotype state (H) or missing data (N) across breeding programs is shown
Mol Breeding (2014) 34:1229–1243 1239
123
123 3H
4H
5H
11_20363
11_21480
7H
11_20755
11_11502
7H
Chr
11_11495
SNP
78.19
62.81
67.86
10.57
6.79
Position
4.94
8.34
7.72
7.32
9.49
-logP 383 0 0
C H N
Low PPO allele
52 0 0
A H N
Low TP allele
331
G
0
N High TP allele
0
H
1 382
A G
0
N High TP allele
0
H
Low TP allele
0 383
0
N A C
2
H
High TP allele Low TP allele
128
A
Low AC allele
0
N 253
H G
0
A
High AC allele
9
G
High AC allele Low AC allele
374
MT2 383
Total individuals
0
7
180
196
9
0
340
34
0
0
0 383
1
4
201
177
0
1
54
328
0
0
383
WA2 383
1
3
19
351
16
0
356
2
1
1
5 367
1
1
6
366
1
1
6
366
1
1
367
N22 374
0
5
92
284
8
2
249
122
2
1
16 362
2
4
148
227
0
2
75
304
2
1
362
AB2 381
14
3
109
256
64
2
245
71
23
4
227 128
35
0
199
148
10
4
78
290
23
4
128
UT2,6 382
1
1
99
278
3
0
236
140
0
0
0 379
0
0
161
218
0
0
13
366
0
0
379
BA2,6 379
1
0
282
101
2
0
15
367
1
0
0 383
1
0
1
382
1
0
0
383
1
0
383
MN6 384
0
2
86
296
1
5
72
306
1
1
0 382
3
0
4
377
1
1
0
382
1
1
382
N66 384
Breeding programs include MT (Montana State University), WA (Washington State University), N2 (North Dakota State University two-row), AB (USDA program in Aberdeen, ID), UT (Utah State University), BA (Busch Agricultural Resources Inc.), MN (University of Minnesota), and N6 (North Dakota State University six-row). B and A alleles are equivalent to -1 and ?1 allele in the T3 database. Subscripts on the first line of the table represent two-row (2), six-row (6), or mixed (2, 6) composition of germplasm in each breeding program
Total phenolics
Amylose content
Traits
Table 2 continued
1240 Mol Breeding (2014) 34:1229–1243
Mol Breeding (2014) 34:1229–1243
alleles. The trait b-glucan has a very different pattern with most markers for most programs more evenly distributed between the high and low alleles. The differences in allele distribution across these loci within different breeding programs have important implications for a national or coordinated effort to breed barley for food use. The frequency of the favorable allele within a breeding program will inform the breeder of the need for selection within the breeding population or whether introgression of favorable alleles from outside the breeding program is necessary. For example, the low allele for the major PPO QTL, as well as the other loci, is nearly fixed in the MN and N6 programs. If the current level of PPO activity is not low enough in this germplasm, then breeders will need to look to exotic sources to reduce PPO activity further. In contrast, most of the other programs are skewed toward the high PPO allele at SNP marker 11_20550. Thus, they could use markerassisted selection to increase the frequency of the low allele in their programs. Programs that are fixed for the unfavorable allele at a particular locus could look to a neighboring program to introduce that allele into their germplasm. For example, the MN or BA programs could look to N6 to introgress the high allele for grain hardness using SNP marker 12_10811. To deliver the potential health benefits and diversity of food products that could be provided with barley, a substantial breeding effort will be needed to improve key traits. There has been relatively little attention to food-related traits in spring barley with the exception of the WA program. This study shows that there is substantial allelic variation for important loci that determine food quality in US barley germplasm. Using markers to increase the frequency of favorable alleles is becoming increasingly less expensive and could substantially reduce the overall cost of improving barley for food uses. Acknowledgments We gratefully acknowledge the technical support of Tracy Harris for phenotyping the barley lines. We also thank the breeders who contributed two-row spring lines for this research: D. Obert, formerly USDA-ARS, Aberdeen, ID; T. Blake, Montana State University; and R. Horsley, North Dakota State University. T. Blake and K. Smith grew the grain samples for phenotyping. This research was supported by USDACSREES-NRI Grant No. 2006-55606-16722 and USDA-NIFA Grant No. 2009-85606-05701 and 2011-68002-30029, the Washington State University Agricultural Research Center, and the Washington State University Center for Sustaining Agriculture and Natural Resources.
1241
References Baik BK, Ullrich SE (2008) Barley for food: characteristics, improvement, and renewed interest. J Cereal Sci 48: 233–242 Beecher B, Bettge A, Smidansky E, Giroux MJ (2002a) Expression of wildtype pinB sequence in transgenic wheat complements a hard phenotype. Theor Appl Gen 105: 870–877 Beecher B, Bowman J, Martin JM, Bettge AD, Morris CF, Blake TK, Giroux MJ (2002b) Hordoindolines are associated with a major endosperm-texture QTL in barley (Hordeum vulgare). Genome 45:584–591 Behall KM, Scholfield DJ, Yuhauiak I, Canary J (1989) Diets containing high amylose vs amylopectin starch: effects on metabolic variables in human subjects. Am J Clin Nutr 49:337–344 Behall KM, Scholfield DJ, Hallfrisch J (2004) Diets containing barley significantly reduce lipids in mildly hypercholesterolemic men and women. Am J Clin Nutr 80:1185–1193 Bendelow VM (1977) Automated procedure for the estimation of total polyphenol content in beer, wort, malt, and barley. J Am Soc Brew Chem 35:150–152 Berger GL, Liu S, Hall MD, Brooks WS, Chao S, Muehlbauer GJ, Baik BK, Steffenson B, Griffey CA (2013) Marker-trait associations in Virginia Tech winter barley identified using genome-wide mapping. Theor Appl Genet 126:693–710 Bhave M, Morris CF (2008) Molecular genetics of puroindolines and related genes: allelic diversity in wheat and other grasses. Plant Mol Biol 66:205–219 Bourdon I, Yokoyama W, Davis P, Hudson C, Backus R, Richter D, Knuckles B, Schneeman BO (1999) Postprandial lipid, glucose, insulin, and cholecystokinin responses in men fed barley pasta enriched with beta-glucan. Am J Clin Nutr 69:55–63 Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635 Caldwell KS, Langridge P, Powell W (2004) Comparative sequence analysis of the region harboring the hardness locus in barley and its colinear region in rice. Plant Physiol 136:1–14 Charles M, Tang H, Belcram H, Paterson A, Gornicki P, Chalhoub B (2009) Sixty million years in evolution of soft grain trait in grasses: emergence of the softness locus in the common ancestor of Pooideae and Ehrhartoideae, after their divergence from Panicoideae. Mol Biol Evol 26:1651–1661 Close TJ, Bhat PR, Lonardi S, Wu Y, Rostoks N, Ramsay L, Druka A, Stein N, Svensson JT, Wanamaker S, Bozdag S, Roose ML, Moscou MJ, Chao S, Varshney RK, Sz} ucs P, Sato K, Hayes PM, Matthews DE, Kleinhofs A, Muehlbauer GJ, DeYoung J, Marshall DF, Madishetty K, Fenton RD, Condamine P, Graner A, Waugh R (2009) Development and implementation of high-throughput SNP genotyping in barley. BMC Genom 10:582 Dykes L, Rooney LW (2007) Phenolic compounds in cereal grains and their health benefits. Cereal Foods World 52:105–111
123
1242 Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255 Fox GP, Osborne B, Bowman J, Kelly A, Cakir M, Poulsen D, Inkerman A, Henry R (2007) Measurement of genetic and environmental variation in barley (Hordeumvulgare) grain hardness. J Cereal Sci 46:82–92 Gaines CS, Finney PF, Fleege LM, Andrews LC (1996) Predicting a hardness measurement using the single-kernel characterization system. Cereal Chem 73:278–283 Giroux MJ, Morris CF (1997) A glycine to serine change in puroindoline b is associated with wheat grain hardness and low level of starch-surface friabilin. Theor Appl Genet 95:857–864 Hamblin MT, Close TJ, Bhat PR, Chao S, Kling JG, Abraham KJ, Blake T, Brooks WS, Cooper B, Griffey CA, Hayes PM, Hole DJ, Horsley RD, Obert DE, Smith KP, Ullrich SE, Muehlbauer GJ, Jannink JL (2010) Population structure and linkage disequilibrium in U.S. barley germplasm: implications for association mapping. Crop Sci 50: 556–566 Han F, Ullrich SE, Chirat S, Menteur S, Jestin L, Sarrafi A, Hayes PM, Jones BL, Blake TK, Wesenberg DM, Kleinhofs A, Kilian A (1995) Mapping of b-glucan content and b-glucanase activity loci in barley grain and malt. Theor Appl Genet 91:921–927 Hu G, Charlotte B, Chunyan Y (2010) Efficient measurement of amylose content in cereal grains. J Cereal Sci 51:35–40 Islamovic E, Obert DE, Oliver RE, Harrison SA, Ibrahim A, Marshall JM, Miclaus KJ, Hu G, Jackson EW (2013) Genetic dissection of grain beta-glucan and amylose content in barley (Hordeum vulgare L.). Mol Breed 31:15–25 Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354 Knapp SJ, Bridges WC (1990) Using molecular markers to estimate quantitative trait locus parameters: power and genetic variances for unreplicated and replicated progeny. Genetics 126:769–777 Komatsuda T, Pourkheirandish M, He C, Azhaguvel P, Kanamori H, Perovic D, Stein N, Graner A, Wicker T, Tagiri A, Lundqvist U, Fujimura T, Matsuoka M, Matsumoto T, Yano M (2007) Six rowed barley originated from a mutation in a homeodomain leucine zipper I-class homeobox gene. Proc Natl Acad Sci USA 104:1424–1429 Leonard W (1942) Inheritance of fertility in the lateral spikelets of barley. Genetics 27:299–316 Massman J, Cooper B, Horsley R, Neate S, Dill-Macky R, Chao S, Dong Y, Schwarz P, Muehlbauer GJ, Smith KP (2011) Genome-wide association mapping of fusarium head blight resistance in contemporary barley breeding germplasm. Mol Breed 27:439–454 McIntosh GH, Whyte J, McArthur R, Nestel PJ (1991) Barley and wheat foods: influence on plasma cholesterol concentrations in hypercholesterolemic men. Am J Clin Nutr 53:1205–1209 Mun˜oz-Amatriaı´n M, Moscou MJ, Bhat PR, Svensson JT, Bartosˇ J, Sucha´nkova´ P, Sˇimkova´ H, Endo TR, Fenton RD, Lonardi S, Castillo AM, Chao S, Cistue´ L, Cuesta-Marcos A, Forrest KL, Hayden MJ, Hayes PM, Horsley RD,
123
Mol Breeding (2014) 34:1229–1243 Makoto K, Moody D, Sato K, Valle´s MP, Wulff BBH, Muehlbauer GJ, Dolezˇel J, Close TJ (2011) An improved consensus linkage map of barley based on flow-sorted chromosomes and single nucleotide polymorphism markers. Plant Genome 4:238–249 Nair S, Ullrich SE, Blake TK, Cooper B, Griffey CA, Hayes PM, Hole DJ, Horsley RD, Obert DE, Smith KP, Muehlbauer GJ, Baik BK (2010) Variation in kernel hardness and associated traits in USA barley breeding lines. Cereal Chem 87:461–466 Nair S, Ullrich SE, Baik BK (2011) Association of barley kernel hardness with physical grain traits and food processing parameters. Cereal Chem 88:147–152 Nelson JC, Andreescu C, Breseghello F, Finney PL, Gualberto DG, Bergman CJ, Pena RJ, Perretant MR, Leroy P, Qualset CO, Sorrells ME (2006) Quantitative trait locus analysis of wheat quality traits. Euphytica 149:145–159 Oziel A, Hayes PM, Chen FQ, Jones B (1996) Application of quantitative trait locus mapping to the development of winter habit malting barley. Plant Breed 115:43–51 QuindeAxtell Z, Ullrich SE, Baik BK (2004) Genotypic variation in color and discoloration potential of barley-based food products. Cereal Chem 81:752–758 QuindeAxtell Z, Powers J, Baik BK (2006) Retardation of discoloration in barley flour gel and dough. Cereal Chem 83:385–390 R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www-R-project.org Rohde W, Becker D, Salamini F (1988) Structural analysis of the waxy locus from Hordeumvulgare. Nucleic Acids Res 16:7185–7186 Rouve`s S, Boeuf C, Zwickert-Menteur S, Gautier MF, Joudrier P, Nelson JC, Bernard M, Jestin L (1996) Locating supplementary RFLP markers on barley chromosome 7 and synteny with homoeologous wheat group 5. Plant Breed 115:511–513 Rutkoski JE, Poland J, Jannink JL, Sorrells ME (2013) Imputation of unordered markers and the impact on genomic selection accuracy. G3 3:427–439 Schmitt MR, Wise ML (2009) Barley and oat b-glucan content measured by Calcofluor fluorescence in a microplate assay. Cereal Chem 86:187–190 Schondelmaier J, Jacobi A, Fischbeck G, Jahoor A (1992) Genetical studies on the mode of inheritance and localization of the amo1 (high amylose) gene in barley. Plant Breed 109:274–280 See D, Kanazin V, Kephart K, Blake T (2002) Mapping genes controlling variation in barley grain protein concentration. Crop Sci 42:680–685 Shimizu C, Kihara M, Aoe S, Araki S, Ito K, Hayashi K, Watari J, Sakata Y, Ikegami S (2008) Effect of high beta-glucan barley on serum cholesterol concentrations and visceral fat area in Japanese men—a randomized, double-blinded, placebo-controlled trial. Plant Foods Hum Nutr 63:21–25 Storey JD, Tibshirani R (2003) Statistical significance for genome wide studies. Proc Natl Acad Sci USA 100:9440–9445 Sz} ucs P, Blake VC, Bhat PR, Chao S, Close TJ, Cuesta-Marcos A, Muehlbauer GJ, Ramsay L, Waugh R, Hayes PM (2009) An integrated resource for barley linkage map and malting quality QTL alignment. Plant Genome 2:134–140
Mol Breeding (2014) 34:1229–1243 Taketa S, Matsuki K, Amano S, Saisho D, Himi E, Shitsukawa N, You T, Noda K, Takeda K (2010) Duplicate polyphenol oxidase genes on barley chromosome 2H and their functional differentiation in the phenol reaction of spikes and grains. J Exp Bot 61:3983–3993 Turuspekov Y, Beecher B, Darlington Y, Bowman J, Blake TK, Giroux MJ (2008a) Hardness locus sequence variation and endosperm texture in spring barley. Crop Sci 48:1007–1019 Turuspekov Y, Martin JM, Bowman JGP, Beecher BS, Giroux MJ (2008b) Associations between Vrs1 alleles and grain quality traits in spring barley Hordeum vulgare L. Cereal Chem 85:817–823 Ullrich SE, Clancy JA, Eslick RF, Lance RCM (1986) b-Glucan content and viscosity of extracts from waxy barley. J Cereal Sci 4:279–285 Walker CK, Panozzo JF, Ford R, Eckermann P, Moody D, Lehmensiek A, Appels R (2011) Chromosomal loci associated with endosperm hardness in a malting barley cross. Theor Appl Genet 122:151–162 Wang M, Jiang N, Jia T, Leach L, Cockram J, Comadran J, Shaw P, Waugh R, Luo Z (2012) Genome-wide association mapping of agronomic and morphologic traits in highly
1243 structured populations of barley cultivars. Theor Appl Genet 124:233–246 Williams PC, Kuzina FD, Hlynka I (1970) A rapid colorimetric procedure for estimating the amylose content of starches and flours. Cereal Chem 47:411–421 Wood PJ, Weisz J (1984) Use of calcofluor in analysis of oat beta-D-glucan. Cereal Chem 61:73–75 Yadav SK, Luthra YP, Sood DR, Aggarwal NK (2000) Gibberellic acid (GA3) induced changes in proanthocyanidins and malt quality of two- and six-row husked barleys. Plant Foods Hum Nutr 55:87–96 Yanaka M, Takata K, Terasawa Y, Ikeda TM (2011) Chromosome 5H of Hordeum species involved in reduction in grain hardness in wheat genetic background. Theor Appl Genet 123:1013–1018 Zheng GH, Sosulski FW (1998) Determination of water separation from cooked starch and flour pastes after refrigeration and freeze-thaw. J Food Sci 63:134–139 Zhou H, Muehlbauer G, Steffenson B (2012) Population structure and linkage disequilibrium in elite barley breeding germplasm from the United States. J Zhejiang Univ Sci B 13:438–451
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