acs.jafc.5b05441

Jan 12, 2016 - The contents of the two phenolic acids were positively correlated with some agronomic traits, such as the first internode length, plant...
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Identification of Quantitative Trait Loci for the Phenolic Acid Contents and Their Association with Agronomic Traits in Tibetan Wild Barley Shengguan Cai, Zhigang Han, Yuqing Huang, Hongliang Hu, Fei Dai,* and Guoping Zhang* Zhejiang Key Laboratory of Crop Germplasm, Department of Agronomy, Zhejiang University, Hangzhou, Zhejiang 310058, People’s Republic of China S Supporting Information *

ABSTRACT: Phenolic acids have been of considerable interest in human nutrition because of their strong antioxidative properties. However, even in a widely grown crop, such as barley, their genetic architecture is still unclear. In this study, genetic control of two main phenolic acids, ferulic acid (FA) and p-coumaric acid (p-CA), and their associations with agronomic traits were investigated among 134 Tibetan wild barley accessions. A genome-wide association study (GWAS) identified three DArT markers (bpb-2723, bpb-7199, and bpb-7273) associated with p-CA content and one marker (bpb-3653) associated with FA content in 2 consecutive years. The contents of the two phenolic acids were positively correlated with some agronomic traits, such as the first internode length, plant height, and some grain color parameters, and negatively correlated with the thousandgrain weight (TGW). This study provides DNA markers for barley breeding programs to improve the contents of phenolic acids. KEYWORDS: agronomic trait, ferulic acid (FA), genome-wide association study (GWAS), p-coumaric acid (p-CA), Tibetan wild barley

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INTRODUCTION

At present, phenolic compounds have been considered as one of the key objectives in barley breeding. It is a quantitative trait; therefore, it is imperative to identify quantitative trait loci (QTLs) or genes responsible for phenolic acid metabolism. A number of studies have successfully identified QTLs for total polyphenol content,14,15 while few studies were concentrated on QTLs for individual phenolic acid content.16 The QTLs of total polyphenol content might not be associated with the individual phenolic acid content, because any phenolic acid only accounts for a small amount of total polyphenol.17 Therefore, identification of QTLs for individual phenolic acid is quite helpful for developing the cultivars or genotypes with a high phenolic acid content. Genetic diversity of cultivated barley declined gradually during its domestication through modern breeding.18,19 A narrower genetic background hinders the development of new barley cultivars. On the other hand, Tibetan wild barley is one of the progenitors of cultivated barley20,21 and has a wide phenotypic and genetic diversity in barley quality characters22,23 and abiotic stress tolerance.24 As a result of its long-term growth under the harsh environment with high ultraviolet (UV) radiation, Tibetan wild barley may develop an antioxidant system to cope with oxidative stress.20 It is phenolics that function as the main UV-absorbing compounds to protect plants from harmful effects of UV-B.25 Actually, it was reported that wild barley had a higher polyphenol content than cultivated barley.26 Therefore, it may be assumed that Tibetan

Phenolic acids are highly addressed in food production as a result of their strong antioxidant activities and high ability of scavenging free radicals.1 The consumption of foods rich in polyphenols may reduce risk of chronic inflammation, cardiovascular diseases, certain cancers, and diabetes.2 In plants, phenolic acids are important substrates for biosynthesis of aromatic amino acids and function as plant growth regulators in the relevant metabolisms.3,4 Moreover, they can bind with polysaccharides and lignin to form bridges or cross-link, which play important roles in cell wall biosynthesis.5 Barley is one of the most important cereal crops, mainly used as feed and malt.6 It was reported that 80% of phenolic compounds in beer originated from malt and raw barley.7 Ferulic acid (FA) and p-coumaric acid (p-CA) are considered as two main phenolic acids in barley grains.8 They are mainly present in the husk and aleurone layer and also in the center of kernels but with lower content.9 There is a huge difference in the phenolic acid content among plant tissues. In comparison to seed, awn has a markedly higher FA content and stem has a higher p-CA content than other tissues.10 Meanwhile, it is reported that phenolic content is closely related to seed color, with black-pigmented barley having higher total phenolic content than common barley.11 The colors of lemma, pericarp, and aleurone layer are mainly attributed to the presence of anthocyanin.12 The synthesis and translocation of phenolic acids and their distribution in different plant tissues are the results of coordinated activities that involve many primary and secondary metabolism pathways.13 However, few studies have been performed on the relationship between phenolic acids and agronomic traits. © XXXX American Chemical Society

Received: November 16, 2015 Revised: January 3, 2016 Accepted: January 12, 2016

A

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry

expressed as y = Xβ + Qν + e, where X is the DArT marker matrix, Q is the sub-population membership matrix, e is the random error vector, and β and ν are coefficient vectors for the DArT marker and subpopulation membership, respectively. The third approach was the K model, which included the kinship matrix to reduce the confounding. The kinship matrix was calculated using the method proposed by Ritland,33 which is built into the program SPAGeDi.34 The K model was expressed as y = Xβ + Zμ + e, where Z is the kinship matrix and μ is a vector of random genetic effects [μ ∼ N(0, 2K)]. The forth approach was the Q + K model, y = Xβ + Qν + Zμ + e, which included both sub-population membership and kinship as covariates.35 A quantile−quantile (Q−Q) plot was displayed using TASSEL (version 3.0) to evaluate the fitness and efficiency of different models. p < 1 × 10−3 [−log10(p) > 3] was set as the threshold in association significance test. Statistical Analysis. Descriptive statistics in distribution of phenolic acid contents in the Tibetan wild population was performed using SPSS 19.0 (SPSS, Inc., Chicago, IL). The Pearson correlation index between phenolic acid contents and agronomic traits was calculated using SPSS 19.0 (SPSS, Inc., Chicago, IL). Significant correlation was evaluated at three levels: p < 0.05, p < 0.01, and p < 0.001. Haplotype analysis was performed according to polymorphism of DArT markers. Boxplot and manhattan plot was drawn using the R program (http://www.r-project.org/) and Sigmaplot version 12.3 (http://www.sigmaplot.com/).

wild barley should be wider in genetic diversity of phenolic acid metabolism. A genome-wide association study (GWAS) has become an efficient tool to identify the loci or genes controlling qualitative and quantitative traits in plants.27,28 In this study, a genomewide association (GWA) analysis of the phenolic acid content and agronomic traits was performed using 134 Tibetan wild barley accessions genotyped with 654 DArT markers with aims at (1) determining the phenotypic variation of individual phenolic acid content in Tibetan wild barley and their potential association with agronomic traits and (2) identifying the QTLs controlling the individual phenolic acid and agronomic traits in Tibetan wild barley.

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MATERIALS AND METHODS

Plant Materials. A total of 134 Tibetan wild barley genotypes were planted in a field of an experimental farm, Zijingang campus, Zhejiang University, Hangzhou, China (Hangzhou, 120.2° E, 30.5° N) in 2 consecutive years, 2013 and 2014. Each genotype was sown in a 2 m row with 0.25 m between rows with three replications. Field management, including fertilization and weed and disease control, was the same as applied locally. At maturity, barley grains were harvested and stored in a cool room at 4 °C. The barley grains were finely ground and fitted with a 0.5 mm screen. Agronomic traits of all tested genotypes (year 2013) were recorded, including awn presence (AP), awn length (AL), first (top) internode length (FIL), plant height (PH), thousand-grain weight (TGW), and grain color parameters. Color parameters include L*, a*, b*, C, and H°. L* indicates lightness; a* indicates redness−greenness; b* indicates yellowness−blueness; C indicates color intensity or saturation; and H° indicates hue angle. L*, a*, and b* were measured using a portable spectrophotometer MiniScan EZ 4500L (HunterLab, Reston, VA). C was calculated as C = (a*2 + b*2)1/2, and H° was calculated as H° = tan−1(b*/a*) × 180°/ π.29 Extraction of Phenolic Acids. The extraction of phenolic acids was performed according to Zhao et al.,30 with some modification. The ground samples (0.2 g) and 4 mL of 80% methanol (v/v) were added to a 10 mL centrifuge tube and mixed thoroughly using a vortex mixer. The mixture was sonicated (40 kHz and 120 W) for 40 min at 70 °C and then centrifuged with 5000g for 15 min. After centrifugation, the supernatant was collected and freeze-dried in a vacuum freezing dryer. The residue was redissolved in 250 μL of 70% methanol [v/v, highperformance liquid chromatography (HPLC) grade] and filtered through a 0.45 μm membrane. The filtrate was transferred to 200 μL glass inserts in 2 mL autosampler vials (Agilent, Santa Clara, CA). Determination of Phenolic Acid Content. HPLC analysis of the phenolic acid content was carried out by Agilent 1260 liquid chromatography (Agilent, Santa Clara, CA) equipped with a Diamonsil 5 μm C18 (250 × 4.6 mm) column (Dikma, China). A 15 μL sample was injected for each measurement. The column temperature was set at 40 °C. Elution was performed using a gradient procedure with a mobile phase containing solvent A (0.1% formic acid in water) and solvent B (methanol) as in the following steps: 0 min, 30% B; 10 min, 45% B; 20 min, 50% B; 25 min, 80% B; 32 min, 30% B; and 37 min, 30% B. The solvent flow rate was set at 0.8 mL min−1. The individual phenolic acid content in the extract was calculated using a standard curve. GWAS of Phenolic Acid and Agronomic Traits. GWA analysis was performed by TASSEL version 3.0 (http://www.maizegenetics. net) using 654 DArT markers and traits, including phenolic acid content and panicle-related traits, in all Tibetan wild barley.31 To achieve really positive results in GWAS, four approaches were tested to ̈ approach, obtain the best fitted model. The first approach was a naive which is the linear regression between phenotype and markers without correction for confounding. The second approach was Q model, in which the sub-population membership Q matrix was introduced to reduce the confounding caused by the population structure. The Q matrix was evaluated by STRUCTURE software.32 The Q model was

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RESULTS Genetic Diversity of the Phenolic Acid Content in Tibetan Wild Barley. Because of the difference in chemical structure, free and bound phenolic acids differ in their biological functions. In this study, we only paid attention to the beneficial effect of phenolic acid in food and drink. Thus, only free phenolic acid was measured. Because FA and p-CA are two main phenolic acids in cereal grains,9 we measured the two chemicals in 134 Tibetan wild barley accessions. The mean p-CA content of all Tibetan wild barley accessions in 2013 and 2014 was 1.59 μg g−1 (ranging from 0.31 to 4.09 μg g−1) and 1.32 μg g−1 (ranging from 0.42 to 3.84 μg g−1), with the coefficient of variation (CV) being 50.2 and 57.5%, respectively (Table 1). The FA content in 2013 and 2014 ranged from 1.50 Table 1. Descriptive Statistics of Phenolic Acid Contents in Tibetan Wild Barleya p-CA minimum first quartile median third quartile maximum mean CV (%)

FA

2013

2014

2013

2014

0.31 1.08 1.38 1.91 4.09 1.59 50.2

0.42 0.81 1.11 1.56 3.84 1.32 57.5

1.50 2.70 3.17 3.93 6.80 3.43 32.4

1.44 2.81 3.30 3.82 7.06 3.48 30.8

a

p-CA, p-coumaric acid; FA, ferulic acid; and CV, coefficient of variation. The unit for phenolic acids is μg g−1.

to 6.80 μg g−1 and from 1.44 to 7.06 μg g−1, with means of 3.43 and 3.48 μg g−1 and CV of 32.4 and 30.8%, respectively. Moreover, the contents of phenolic acids were highly correlated between the 2 consecutive years (r = 0.869 and 0.765; p < 0.001) (Figure 1), indicating the high reliability of the phenotype data. Phenotypic Links between Phenolic Acid Contents and Agronomic Traits. Correlation analysis was conducted to investigate the potential links between the phenolic acid B

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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0.325). On the contrary, no significant correlation was present between AL and phenolic acid, and there was a weak correlation between AP and phenolic acid. In this study, positive correlations were found between individual phenolic acid content and L*, a*, b*, and C, with the coefficient factor r being around 0.2 (Table 2). In our understanding, it is the first attempt to link free phenolic acid with grain color. In addition, the phenolic acid content was negatively correlated with TGW (Table 2). Most Fitted Model in GWAS. Because the confounding population structure would lead to false-positive association,27,35 the sub-population membership Q matrix and relative kinship K matrix were introduced in models to reduce the falsepositive association. Currently, we tested the four models in association analysis and analyzed the fitness of models using a Q−Q plot of p value distributions.36 The results showed that ̈ model resulted in a strong skew toward significance the naive for every trait (Figure S1 of the Supporting Information). This skew was induced by the effect of a profound population structure. However, the p value from the Q model still deviated from the expected value in this study (Figure S1 of the Supporting Information) as well as our previous GWAS studies using the same population.23,24 The possible reason could be that the evaluating method of sub-population membership Q matrix is based on the Hardy−Weinberg equilibrium (HWE), while the assumption of HWE is actually invalid because of extensive inbreeding in barley genotypes.28 Both Q + K and K models performed well in controlling the rate of false positives, while the K model seems to maintain a higher statistical power (Figure S1 of the Supporting Information). The Q + K model may lead to overcorrection of the population structure, because no significant association (p < 0.001) was detected for FA in 2013. Therefore, the K model was applied in the GWAS in this study. Identification of QTLs Controlling Phenolic Acid and Agronomic Traits. GWA analysis, performed using 12 traits and 654 DArT markers in 134 Tibetan wild accessions, identified a total of 53 significant marker−trait associations, including 15 associations with the phenolic acid content and 38 associations with agronomic traits (Tables 3 and 4 and Figure 3). Each of these markers accounted for 9.2−40.3% of phenotypic variation. The p-CA content was associated with three markers, bpb2723 (1H, 11.5 cM), bpb-7199 (3H, 13.7 cM), and bpb-7273 (3H, 53.2 cM), while the FA content was associated with one marker bpb-3653 (2H, 108.1 cM). These markers were detected in both years, indicating high reliability of these marker−trait associations. It is worth noted that two markers bpb-7273 and bpb-0836 on 53.2 and 61.9 cM of chromosome 3H were significantly associated with both FA and p-CA in either 2013 or 2014. The result suggested that these two phenolic acids may be linked with each other by sharing the same metabolic pathway. It can also partially explain the presence of a highly positive correlation between the two phenolic acid contents. In addition, haplotype analysis showed that there was a clear separation of phenolic acid contents between the two sets of accessions (Figure S2 of the Supporting Information). In other words, these markers can be used to screen the genotypes with a high content of phenolic acid in a barley breeding program. A total of 38 associations were found between markers and agronomic traits, including AP, AL, FIL, PH, TGW, and color parameters L*, a*, b*, C, and H°. Interestingly, both AP and

Figure 1. Correlation analysis of phenolic acid contents between two constitutive years: (a) correlation between p-CA content in 2013 and 2014 and (b) correlation between FA content in 2013 and 2014. All correlations were significant at the p < 0.001 level.

content and agronomic traits. Interestingly, FIL was highly and positively correlated with the phenolic acid content (r = 0.433− 0.580; p < 0.001) (Table 2 and Figure 2). PH was also positively correlated with the phenolic acid content (r = 0.218− Table 2. Correlation Analysis of Malt Phenolic Acids and Agronomic Traitsa p-CA AP AL FIL PH TGW L* a* b* C H°

FA

2013

2014

2013

2014

−0.198* 0.030 0.580*** 0.325*** −0.296** 0.218* 0.271** 0.262** 0.263** 0.095

−0.187* 0.131 0.541*** 0.267** −0.194* 0.170 0.181* 0.189* 0.190* 0.119

−0.172 0.066 0.433*** 0.229** −0.212* 0.216* 0.173 0.208* 0.207* 0.135

−0.187* 0.119 0.447*** 0.218* −0.200* 0.226* 0.232** 0.258** 0.258** 0.127

a *, **, and *** represent significant correlation at p < 0.05, p < 0.01, and p < 0.001 levels, respectively.

C

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Figure 2. Correlation analysis between phenolic acids and FIL: (a) correlation between FIL and p-CA in 2013, (b) correlation between FIL and FA in 2013, (c) correlation between FIL and p-CA in 2014, and (d) correlation between FIL and FA in 2014.

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DISCUSSION In this study, we determined phenotypic variance and genetic diversity of individual phenolic acid content in 134 Tibetan wild barley accessions. In a previous study, we measured the phenolic acid content of 68 cultivated barley cultivars.37 Comparatively, Tibetan wild barley has a significantly higher content of phenolic acid, especially FA (Figure 4). The results are consistent with those reported by Fujita et al.26 that wild barley had much higher phenolic content than cultivated barley. The reason why the wild barley contains a higher content of phenolics is described by its strong tolerance to the harsh environment of Tibet, including UV radiation and low temperature.20,25 It was reported that total phenolic content was associated with grain color and thousand-grain weight.14,29 In this study, we investigated the relationship between the free phenolic acid content and agronomic traits. Interestingly, the phenolic acid content was highly correlated with FIL (r = 0.433−0.580; p < 0.001). This phenotypic link could be attributed to the role of phenolic acid in regulating elongation of the internode. The first internode in cereal plants is a transmission medium responsible for nutrient transport from flag leaf to grain during grain development. As the important cell wall components, phenolic acids act as a bridge between lignin and polysaccharides.38 Recently, phenolic compounds in the cell wall have been proven to participate in the regulation of internode development in rice. 39 Moreover, significant correlation was also found between the phenolic acid content and grain color parameters. The correlation can be explained by the fact that p-CA is an important intermediate in biosynthesis of anthocyanin,40 directly responsible for grain color.12 In this study, the GWAS was performed to identify the QTLs of the phenolic acid content in Tibetan wild barley. To reduce the confounding possibly caused by the population structure, the K model was selected as a most appropriate model, as validated by the Q−Q plot. Similarly, the K model was applied

Table 3. List of DArT Markers Associated with Phenolic Acidsa trait p-CA in 2013

p-CA in 2014

FA in 2013

FA in 2014

marker

chromosome

position

−log10(p)

marker R2

bPb-2723 bPb-4902 bPb-7199 bPb-7273 bPb-0836 bPb-0055 bPb-2723 bPb-2230 bPb-7199 bPb-7273 bPb-3653 bPb-8737 bPb-0836 bPb-3653 bPb-7273

1H 1H 3H 3H 3H 6H 1H 2H 3H 3H 2H 2H 3H 2H 3H

11.5 126.7 13.7 53.2 61.9 68.2 11.5 60.4 13.7 53.2 108.1 108.7 61.9 108.1 53.2

3.00 3.44 3.10 3.66 4.68 3.39 3.68 3.06 3.44 3.14 3.48 3.14 3.08 3.34 3.31

0.093 0.107 0.097 0.116 0.157 0.105 0.121 0.096 0.113 0.100 0.109 0.096 0.095 0.106 0.104

a These markers were identified using the K model with a significance threshold as p < 1 × 10−3. R2 (marker) denotes the contribution of the marker for phenotypic variation.

FIL were strongly associated [−log10(p) = 10.7 and 4.9] with bpb-8143 located at 98.2 cM of 2H (Table 4). AL was associated with two chromosomal regions, including 66−69 cM in 3H (bpb-3320 and bpb-7278) and 111.7 cM in 7H (bpb0027 and bpb-9908). The lack of common marker between AL and AP indicated that awnless and awn length was controlled by different genes or loci. One major QTL (bpb-0631, 128.5 cM in 1H) was found to be associated with grain color parameters, including L*, a*, b*, and C. In addition, TGW was associated with five markers in 1H, 3H, and 7H, and PH was associated with one marker in 1H. D

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry Table 4. List of DArT Markers Associated with Agronomic Traitsa trait AP

AL

FIL

PH TGW

L*

a*

b* C

marker

chromosome

position

−log10(p)

marker R2

bPb-2225 bPb-6207 bPb-0890 bPb-8143 bPb-9746 bPb-0347 bPb-7069 bPb-7063 bPb-6611 bPb-0085 bPb-9562 bPb-4494 bPb-2304 bPb-7146 bPb-1621 bPb-5571 bPb-3320

2H 2H 2H 2H 3H 3H 3H 3H 4H 5H 5H 5H 6H 6H 6H 7H 3H

67.3 87 87 98.2 54.8 175.2 178.6 178.6 60.5 1.7 1.7 127.9 136.1 137.8 137.8 133.4 66.5

3.44 4.34 3.99 10.67 3.39 5.2 5.2 5.2 3.3 3.1 3 3.74 3.34 3.34 3.34 3.66 3.51

0.101 0.134 0.126 0.403 0.099 0.167 0.167 0.167 0.099 0.092 0.088 0.112 0.097 0.097 0.097 0.109 0.115

bPb-7278

3H

69.3

3.04

0.096

bPb-9908 bPb-0027 bPb-8143 bPb-0094 bPb-0055 bPb-1922 bPb-9718 bPb-6502 bPb-5289 bPb-1829 bPb-8823 bPb-0631 bPb-9552 bPb-6065 bPb-0395 bPb-0631 bPb-9552 bPb-6065 bPb-0631 bPb-0631

7H 7H 2H 3H 6H 1H 1H 1H 3H 3H 7H 1H 1H 1H 1H 1H 1H 1H 1H 1H

111.7 111.7 98.2 69.3 68.2 63 10.5 139.6 35.9 155.8 133.4 128.5 136.1 136.2 141.3 128.5 136.1 136.2 128.5 128.5

3.57 3.49 4.92 3.39 3.3 3.63 3.18 3.75 3.59 3.14 3.04 4.65 3.55 3.55 3.19 3.85 3.92 3.92 3.85 3.61

0.116 0.121 0.156 0.104 0.097 0.108 0.118 0.129 0.123 0.104 0.108 0.15 0.108 0.108 0.094 0.118 0.121 0.121 0.119 0.11

QTL reported by previous studies

Takahashi et al.42

Sameri Gyenis Sameri Gyenis

et et et et

al.44 al.45 al.44 al.45

Lundqvist et al.43

Lundqvist et al.43

Lundqvist et al.43 Lundqvist et al.43

a AP, awn presence; AL, awn length; FIL, first internode length; PH, plant height; TGW, thousand-grain weight; and L*, a*, b*, and C, color parameters. These markers were identified using the K model with a significance threshold as p < 1 × 10−3. R2 (marker) denotes the contribution of the marker for phenotypic variation.

in several association mapping studies in barley.28,41 The availability of the examined population and validity of the GWAS model were further confirmed by the fact that the QTLs of several agronomic traits (e.g., AP, AL, and grain color) detected here were co-located with the QTLs reported by previous studies.42−45 The AP-associated marker bpb-8143 (2H, 98.2 cM) is co-located with lks1 locus responsible for awnless.42 AL-associated markers bpb-3320 and bpb-7278 (3H, 66−69 cM) are co-located with a major QTL reported by Sameri et al.44 (uzu-e06m30.10.1, 65.5 cM in 3H) and Gyenis et al.45 (Bmac0067 and Bmag0006, 66−69 cM in 3H). Graincolor-related marker bpb-0631 (1H, 128.5 cM) is co-located with a main QTL controlling the black lemma and pericarp (blp) in barley.43,45 In our previous study, we identified a number of loci associated with the phenolic acid content in cultivated barley.37

The current results showed that some of them shared the same loci detected in Tibetan wild barley. For example, bpb-0631 (128.5 cM, 1H), contributing to p-CA of cultivated barley, is located near bpb-4902 (126.7 cM, 1H) in Tibetan wild barley, and a chromosomal region (54.8−55.6 cM, 3H) associated with the FA content of cultivated barley is located near bpb-7273 (53.2 cM, 3H) controlling both FA and p-CA contents of Tibetan wild barley. Co-localization of these loci or chromosomal region strongly indicated the presence of common genes or metabolic pathway controlling the phenolic acid content of cultivated and Tibetan wild barley. On the other hand, some novel loci associated with phenolic acid were identified in Tibetan wild barley. These markers include bpb7199 (13.7 cM, 3H) controlling the p-CA content and bpb3653 (108.1 cM, 2H) controlling the FA content. The presence of these loci (genes) could be attributed to the wider genetic E

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Figure 3. Manhattan plots of GWAS results: (a) p-CA in 2013, (b) p-CA in 2014, (c) FA in 2013, and (d) FA in 2014. The marker name in blue indicates that this marker is associated with p-CA, and the marker name in green indicates that this marker is associated with FA, in 2 consecutive years, while the marker name in red indicates that this marker is associated with both p-CA and FA.

proteins interact with phenylalanine ammonialyase (PAL) via mediating ubiquitination of PAL.47 PAL catalyzes phenylalanine to synthesize trans-cinnamic acid that is then further transformed into different phenolic products, including p-CA.48,49 Downregulation of the F-box protein enhances the production of polyphenols in Arabidopsis.50 However, the roles of these genes in phenolic acid metabolism are unclear, and their functions are waiting to be studied in future work.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.5b05441. Q−Q plots of the p value under four GWAS models (Figure S1) and haplotype analysis of phenolic acid contents by phenolic-acid-associated DArT markers (Figure S2) (PDF)

Figure 4. Boxplots of phenolic acids in Tibetan wild and cultivated barley: (a) p-CA and (b) FA. W2013, phenolic acids of Tibetan wild barley in 2013; W2014, phenolic acids of Tibetan wild barley in 2014; and C, phenolic acids of cultivated barley. The data of phenolic acid contents in cultivated barley was cited from Cai et al.37

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AUTHOR INFORMATION

Corresponding Authors

*Telephone: +86-571-88982115. Fax: +86-571-88982117. Email: [email protected]. *Telephone: +86-571-88982115. Fax: +86-571-88982117. Email: [email protected].

diversity of Tibetan wild barley, while these alleles may be lost in cultivated barley during modern breeding.18,19 The findings indicate the possibility of identifying novel alleles or metabolic pathways participating in phenolic acid synthesis in the wild barley. The release of the barley genome is quite helpful for identifying probable candidate genes based on QTL results.46 The sequences of DArT markers were submitted to IPK Barley Blast Server (http://webblast.ipk-gatersleben.de/barley) to search for the corresponding contigs, some of which contain genes with annotations. As a result, the sequences of markers bpb-2230 and bpb-7199 were found to match the contig_41360 and contig_127835 containing genes encoding Fbox protein and ubiquitin-protein ligase, respectively. It has been reported that Arabidopsis Kelch domain-containing F-box

Author Contributions

Guoping Zhang, Shengguan Cai, and Fei Dai designed the experiments; Shengguan Cai, Zhigang Han, Yuqing Huang, and Hongliang Hu conducted the experiments and analyzed data; and Shengguan Cai and Guoping Zhang wrote the paper. Funding

This study was supported by the National Natural Science Foundation of China (31201166, 31471480, and 31401369), the China Agriculture Research System (CARS-05), the Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP), and the Fundamental Research Funds for the Central Universities. F

DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank Jingqun Yuan and Mei Li, the technician of 985-Institute of Agrobiology and Environmental Sciences of Zhejiang University, for the assistance in HPLC analysis.

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DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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DOI: 10.1021/acs.jafc.5b05441 J. Agric. Food Chem. XXXX, XXX, XXX−XXX