Association analysis of grain traits with SSR markers between Aegilops tauschii and hexaploid wheat (Triticum aestivum L.)

Journal of Integrative Agriculture 2015, 14(10): 1936–1948 Available online at www.sciencedirect.com

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RESEARCH ARTICLE

Association analysis of grain traits with SSR markers between Aegilops tauschii and hexaploid wheat (Triticum aestivum L.) ZHAO Jing-lan1, 2, WANG Hong-wei1, ZHANG Xiao-cun1, DU Xu-ye1, LI An-fei1, KONG Ling-rang1 1 2

State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai’an 271018, P.R.China Taishan Polytechnic, Tai’an 271000, P.R.China

Abstract Seven important grain traits, including grain length (GL), grain width (GW), grain perimeter (GP), grain area (GA), grain length/width ratio (GLW), roundness (GR), and thousand-grain weight (TGW), were analyzed using a set of 139 simple sequence repeat (SSR) markers in 130 hexaploid wheat varieties and 193 Aegilops tauschii accessions worldwide. In total, 1 612 alleles in Ae. tauschii and 1 360 alleles in hexaploid wheat (Triticum aestivum L.) were detected throughout the D genome. 197 marker-trait associations in Ae. tauschii were identified with 58 different SSR loci in 3 environments, and the average phenotypic variation value (R2) ranged from 0.68 to 15.12%. In contrast, 208 marker-trait associations were identified in wheat with 66 different SSR markers in 4 environments and the average phenotypic R2 ranged from 0.90 to 19.92%. Further analysis indicated that there are 6 common SSR loci present in both Ae. tauschii and hexaploid wheat, which are significantly associated with the 5 investigated grain traits (i.e., GA, GP, GR, GL, and TGW) and in total, 16 alleles derived from the 6 aforementioned SSR loci were shared by Ae. tauschii and hexaploid wheat. These preliminary data suggest the existence of common alleles may explain the evolutionary process and the selection between Ae. tauschii and hexaploid wheat. Furthermore, the genetic differentiation of grain shape and thousand-grain weight were observed in the evolutionary developmental process from Ae. tauschii to hexaploid wheat. Keywords: association analysis, grain traits, Aegilops tauschii, Triticum aestivum, SSR markers

inated from an interspecific hybridization between the

1. Introduction Extensive genetic evidence has showed that hexaploid wheat (Triticum aestivum L., 2n=6x=42, AABBDD) orig-

tetraploid, Triticum turgidum ssp. dicoccum (2n=4x=28, AABB), and the diploid, Aegilops tauschii (Dvorak et al. 1998). However, only a small number of Ae. tauschii genotypes in the restricted geographic origin were found to be involved in the origin of hexaploid wheat (Lagudah and Halloran 1988); and narrow genetic diversity in the D genome of hexaploid wheat may exist, based on studies of the morphological traits (Watanabe 1983), the isozymes, the

Received 24 October, 2014 Accepted 29 April, 2015 ZHAO Jing-lan, Tel: +86-538-8628134, E-mail: zhaojlan@sina. com; Correspondece KONG Ling-rang, Tel: +86-538-8249278, E-mail: [email protected]

sequence-tagged site (STS) markers (Talbert et al. 1998),

© 2015, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(15)61070-X

has resulted in increased genetic uniformity among wheat

and the restriction fragment length polymorphisms (RFLPs) (Dvorak et al. 1998). Furthermore, modern crop breeding varieties and the erosion of genetic diversity in cultivated

ZHAO Jing-lan et al. Journal of Integrative Agriculture 2015, 14(10): 1936–1948

wheat. Therefore, Ae. tauschii may serve as an important genes for improving the hexaploid wheat variety. To date, many useful genes, including different biotic and abiotic stress resistance genes, have been found in Ae. tauschii, and introgressed and utilized in wheat variety improvement programs via hybridization (Hsam et al. 2001). Grain shape and thousand-grain weight (TGW) associated with yield traits are important for wheat breeding (Breseghello and Sorrells 2006). Grain shape indicates a relative proportion of the main growth axes of the grain (Breseghello and Sorrells 2007; Gegas et al. 2010), and is generally estimated by the length, width, vertical perimeter, sphericity, and proportion of the horizontal axes (Breseghello and Sorrells 2007). During Einkorn and tetraploid wheat domestication, the wheat dramatically changed from a relatively small grain with a long, thin shape, to a uniform, larger grain with a short, wide shape (Gegas et al. 2010; Okamoto et al. 2012). In the process of hexaploid wheat speciation, a dramatic change in grain shape occurred due to the acquisition of non-tenacious glumes (Okamoto et al. 2012). During the subspecies differentiation of tetraploid and hexaploid wheat, grain shape diverged greatly based on the pleiotropic effects of the genes involved in differentiation (Gegas et al. 2010; Okamoto et al. 2013). Various studies have identified quantitative trait loci (QTLs) for grain shape in hexaploid wheat, and QTLs have been assigned to various chromosomes (Breseghello and Sorrells 2006, 2007; Gegas et al. 2010; Sun et al. 2010; Tsilo et al. 2010; Okamoto et al. 2013; Williams et al. 2013). Recently, an association study was developed to dissect a variety of complex traits in plants (Jiang et al. 2011; Mir et al. 2012). An association study has the advantage over conventional QTL mapping since it can be performed on a larger number of genotypes. However, a population used for conventional QTL mapping is developed from a bi-parental cross, allowing for the detection of only a subset of loci/ alleles within a plant and offering limited resolution due to insufficient recombination between the linked genetic loci. Therefore, an association study, rather than conventional QTL mapping, may present wider genetic variations and higher mapping resolution of the phenotypes and traits at a population level (Addington et al. 2011). A significant association with TGW has been observed in some wheat orthologs of the size-controlling genes in rice (Jiang et al. 2011; Su et al. 2011; Zhang et al. 2012). The objective of this study was to identify the loci governing grain shape and TGW and to characterize the allelic variation in Ae. tauschii and the D genome of hexaploid wheat using an association analysis.

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2. Results 2.1. Genetic diversity in Ae. tauschii and hexaploid wheat varieties The genetic diversity in Ae. tauschii and hexaploid wheat varieties at the genomic level are listed in Table 1 (summary information of 139 SSR markers listed in Appendix A). A total of 1 612 alleles, ranging from 5 to 19, were identified at the 139 SSR loci in Ae. tauschii, compared with 1 360 alleles ranging from 3 to 13 in the hexaploid wheat varieties. The polymorphism information (PIC) values in Ae. tauschii ranged from 0.561 to 0.937 and the number of rare alleles, with a frequency of less than 5%, was 424 (26.3%). In contrast, the PIC values in wheat varieties ranged from 0.472 to 0.925, and the number of rare alleles was 356 (26.2%). The average genetic diversity index was 0.88 and 0.81 in Ae. tauschii and wheat varieties, respectively, indicating a higher diversity in Ae. tauschii compared with the D genome in the hexaploid wheat varieties.

Table 1 Allelic diversity of Aegilops tauschii and hexaploid wheat varieties revealed by 139 D-genome simple sequence repeat (SSR) markers Marker No. of accessions No. of polymorphic loci Allele range No. of alleles Polymorphism information (PIC) range Genetic diversity index

Aegilops tauschii 193 139 5–19 1 612 0.56–0.93 0.88

Triticum aestivum 130 139 3–13 1 360 0.37–0.91 0.81

2.2. Population structure of Ae. tauschii and hexaploid wheat varieties The population structure of Ae. tauschii and the wheat varieties were investigated using a Bayesian clustering approach to infer the number of clusters (populations) with STRUCTURE v2.2 software (Pritchard 2000). According to Evanno et al. (2005), ΔK was plotted against the number of sub-groups. For Ae. tauschii, the maximum value of ΔK occurred at K=6, indicating that 193 accessions were segregated into 6 sub-groups (Fig. 1-A). According to the Q matrix at K=6, a total of 166 accessions were used for simulations after removing accessions with probabilities less than 80% for all of the groups. The optimum K was verified to be 6 in both the ΔK analyses (Fig. 1-A). Group I included 30 Ae. tauschii accessions that originated mainly from the USA and Mexico (Appendix B). Group II contained

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A

2.3. Phenotypic performances and correlation analysis

200

∆K

150 100 50 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K B

40

∆K

30 20 10 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K

Fig. 1 Estimation of the number of populations for K ranging from 2 to 15 by calculating ∆K values in Aegilops tauschii (A) and hexaploid wheat varieties (B).

21 accessions, which were mainly collected from East Asia (China) and North America (USA and Canada). Group III included 31 accessions originating mainly from Eastern Europe and East Asia. Group IV included 32 accessions in which the geographic origin for most of the accessions was unknown, whereas part of the accessions was from West Asia. Group V included 25 accessions originating mainly from Asia, Europe (Russian) and North America (Mexico and USA). Group VI included 27 accessions, originating mainly from West Asia. The results indicated that the accessions originating from the same region tended to be in the same group. For hexaploid wheat varieties, the maximum value in the ΔK occurred at K=7, exhibiting 130 accessions segregated into 7 sub-groups (Fig. 1-B). After removing the accessions with the posterior probability <80%, 106 accessions were used for the simulations. The optimum K value was verified as 7 in both the ΔK analyses (Fig. 1-B). Groups I, II, and V, including 7, 19, and 26 accessions, respectively, were dominated by varieties from Shandong Province in China (Appendix B). Groups III and VI, including 18 and 8 accessions, respectively, were dominated by varieties from the Henan and Sichuan provinces of China. Groups VI and VII, including 4 and 7 accessions, respectively, were dominated by a foreign germplasm. Similarly, the results indicated that the wheat accessions originating from the same region tended to be in the same group.

Phenotypic performances for grain shape and TGW showed a remarkable difference in the 3 environments for Ae. tauschii and wheat varieties (Table 2). In detail, grain shape and TGW traits ranged as follows: For Ae. tauschii, grain length (GL) from 4.49 to 8.35 mm with a mean value of 6.05 mm, grain width (GW) from 2.29 to 4.15 mm with a mean value of 3.04 mm, grain length/width ratio (GLW) from 1.38 to 2.95 with a mean value of 2.01, grain perimeter (GP) from 10.99 to 18.65 mm with a mean value of 14.67 mm, roundness (GR) from 0.55 to 0.93 with a mean value of 0.76, grain area (GA) from 7.80 to 18.58 mm2 with a mean value of 13.09 mm2 and TGW from 6.72 to 12.33 g with a mean value of 9.05 g. For wheat varieties, GL from 4.41 to 6.96 mm with a mean value of 5.70 mm, GW from 2.52 to 4.11 mm with a mean value of 3.44 mm, GLW from 1.38 to 2.17 with a mean value of 1.66, GP from 11.10 to 16.85 mm with a mean value of 14.53 mm, GR from 0.62 to 0.77 with a mean value of 0.73, GA from 7.79 to 19.21 mm2 with a mean value of 13.98 mm2 and TGW from 21.01 to 59.41 g with a mean value of 43.78 g. The analysis of the correlations between the investigated traits in Ae. tauschii and wheat varieties is reported in Table 3. Pearson correlation coefficients of GL and GLW, GW, GA, simultaneously exhibited significant positive correlations (r=0.21, 0.53, 0.73 in Ae. tauschii and r=0.53, 0.46, 0.83 in wheat varieties, respectively). Significant positive correlations were also found between the GW and GP, GR, GA, TGW (r=0.47, 0.54, 0.81, 0.73 in Ae. tauschii and r=0.76, 0.52, 0.87, 0.18 in wheat varieties, respectively). But Pearson correlation coefficients of GW and GLW were found significant negative correlation (r=–0.71 in Ae. tauschii and r=–0.51 in wheat varieties, respectively). For Ae. tauschii, Pearson correlation coefficients of TGW and GL, GW, GR, GA, simultaneously exhibited significant positive correlations (r=0.81, 0.73, 0.96 and 0.99, respectively). But for wheat varieties, no significant correlation was observed between TGW and GL, GR, GA (r=0.04, 0.13 and 0.14, respectively).

2.4. Association analysis The marker-trait association was tested through a mixed linear model (Yu et al. 2005). Significant (P<0.05) candidate markers associated with the investigated traits were detected in Ae. tauschii and the hexaploid wheat population. A total of 197 marker-trait associations were identified with 58 different SSR markers in the 3 environments and the average value (AV, considered as one environment below) and the average phenotypic variation (R2) value ranged from

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ZHAO Jing-lan et al. Journal of Integrative Agriculture 2015, 14(10): 1936–1948

Table 2 Phenotypic performances for grain shape and TGW in three environments in Ae. tauschii and hexaploid wheat varieties Trait1) GL

GW

GLW

GP

GR

GA

TGW

　

Environment2) E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV

Range 3.57–9.09 4.64–7.88 4.49–8.27 4.49–8.35 2.00–4.20 2.12–4.19 2.31–4.17 2.29–4.15 1.34–3.35 1.44–3.02 1.35–2.89 1.38–2.95 8.70–20.00 11.15–18.12 10.90–18.54 10.99–18.65 0.48–0.89 0.63–0.89 0.45–1.04 0.55–0.93 4.90–20.10 7.35–20.14 7.76–18.58 7.80–18.58 6.20–23.10 5.80–22.30 5.40–22.10 6.72–12.33

Ae. tauschii Average 6.07 6.04 6.06 6.05 3.12 2.96 3.04 3.04 1.97 2.06 2.02 2.01 14.99 14.33 14.67 14.67 0.75 0.78 0.75 0.76 13.28 12.86 13.09 13.09 1.39 1.37 1.37 9.50

Std. 0.78 0.60 0.60 0.60 0.42 0.38 0.38 0.37 0.31 0.27 0.27 0.27 1.72 1.30 1.30 1.31 0.07 0.05 0.10 0.06 2.72 2.29 2.23 2.23 0.35 0.34 0.34 1.16

Range 4.40–6.90 4.32–7.15 4.10–7.07 4.41–6.96 2.50–4.10 2.44–4.25 2.37–4.12 2.52–4.11 1.44–2.28 1.35–2.39 1.27–2.40 1.38–2.17 10.96–16.66 10.38–17.29 10.76–16.97 11.10–16.85 0.62–0.78 0.63–0.79 0.57–0.80 0.62–0.77 8.00–18.23 6.83–20.18 7.02–19.34 7.79–19.21 19.68–58.28 20.67–61.16 22.68–59.28 21.01–59.41

T. aestivum Average 5.65 5.81 5.70 5.70 3.43 3.45 3.46 3.44 1.66 1.70 1.66 1.66 14.44 14.64 14.57 14.53 0.73 0.72 0.72 0.73 13.94 14.10 13.94 13.98 42.72 44.21 44.42 43.78

Std. 0.45 0.50 0.53 0.46 0.27 0.34 0.36 0.28 0.13 0.16 0.19 0.14 1.08 1.27 1.25 1.10 0.03 0.03 0.04 0.03 1.99 2.39 2.37 2.03 6.59 6.68 6.59 6.60

1)

GL, grain length; GW, grain width; GLW, grain length/width ratio; GP, grain perimeter; GR, grain roundness; GA, grain area; TGW, thousand-grain weight. The same as below. 2) E1, 2010–2011; E2, 2011–2012; E3, 2012–2013; AV, average of E1, E2, and E3. The same as below.

Table 3 Pearson correlation coefficients (r) between the investigated traits in Ae. tauschii and hexaploid wheat varieties Trait GW GLW GP GR GA TGW *

Species Ae. tauschii T. aestivum Ae. tauschii T. aestivum Ae. tauschii T. aestivum Ae. tauschii T. aestivum Ae. tauschii T. aestivum Ae. tauschii T. aestivum

GL 0.21** 0.53** 0.53** 0.46** –0.28** 0.93** 0.92** –0.28** 0.73** 0.83** 0.81** 0.04

GW

GLW

–0.71** –0.51** 0.47** 0.76** 0.54** 0.52** 0.81** 0.87** 0.73** 0.18*

–0.61** 0.14 0.19** –0.83** –0.18* –0.07 –0.05 –0.14

GP

–0.11 –0.07 0.18* 0.97** 0.09 0.1

GR

GA

0.91** 0.16 0.96** 0.13

0.99** 0.14

, significant at P≤0.05; **, significant at P≤0.01.

0.68 to 15.12% in the Ae. tauschii population (Appendix C). Four markers, i.e., gpw5179-6D, gpw306-6D, gwm583-5D and wmc160-5D, were closely associated with the GL in 3 to 4 environments, with R2 ranging from 0.68 to 3.19%. Seven markers, i.e., cfd28-1D, cfd27-1D, gpw326-5D, gpw51222D, gpw5185-4D, Xpsp3113-7D and wmc601-2D, indicated

a stable association with GW in more than 3 environments with R2 ranging from 4.16 to 10.69%. Six markers, including cfd32-1D, gdm33-1D, gpw322-3D, gpw5179-6D, gwm5835D and Xwmc111-2D, were associated with GP stably in more than 3 environments, with R2 ranging from 1.72 to 3.11%. One marker, cfd188-6D, associated with GR stably

4.60 5.09 6.02

14.24

1.21E-02

　

1.72E-02

3.05E-02 2.72E-02

5.70E-03 9.32E-04

8.70E-03 14.20

9.71

5.39

2.90E-03 9.33

P-value indicates the significance between marker and the phenotype. R2 value indicate the percentage of phenotypic variation explained by the marker. 2)

1)

15.15

5.28 5.49

12.28 7.50E-03 1.15E-02 4.80E-02 gpw5137-7D

gpw5181-7D

GR

3.69E-02

4.11E-02

　

4.71E-02

1.51 1.96E-02 3.05E-02 cfd4-3D GR

TGW

11.97 17.36

11.87 11.50

16.46 1.24E-02

1.75E-02 2.63E-02

4.50E-03

gwm583-5D GP

3.88E-02

gpw5179-6D GP

8.20E-03

5.23

14.06

8.71 9.65

2.29

2.55

1.92 3.11 5.30E-03

1.40E-03

2.00E-03

5.70E-03

1.50E-03

2.82

15.12 1.53E-02

3.79E-02 4.05E-02 2.05E-02

1.10E-02 1.55

15.52 19.55 16.18 13.76

1 .83 2.99E-02

1.10E-03 1.70E-03

2.74E-02 gpw5179-6D

gdm33-1D

GA

GP

4.01E-02

8.28E-04

7.10E-03 gwm583-5D

gdm33-1D

GL

GA

1.77E-02

2.22

2.48

9.77

1.98

2.04

10.00

1.79 1.57

2.22

2.24 14.33 2.60E-02

1.58E-02 1.54E-02

3.51E-02 6.50E-03

2.35E-02 13.19

12.43

13.64

2.55

13. 40 12.78 1.14E-02

6.60E-03

3.40E-03

E3

2.26 1.84

E2 E1

3.19 2.92E-04

AV E3

4.33E-04 4.40E-02

E2 E1

1.70E-03 16.41

AV E3

17.32

E2 E1

19.99 8.10E-03

AV E1

1.00E-03 gpw5179-6D GL

E2

E3

4.20E-03

R2 (%)2) Ae. tauschii P-value1) R2 (%)2) T. aestivum P-value1) Loci Trait

in more than 3 environments with an average R2 value of 9.5%. Seven markers including cfd27-1D, cfd32-1D, gdm331D, gpw322-3D, gwm583-5D, Xpsp3113-7D and wmc111-2D were associated with GA stably in more than 3 environments, with R2 ranging from 1.00 to 14.33%. Eight markers, consisting of cfd168-2D, gpw306-6D, gpw326-5D, gpw341-2D, gpw5166-3D, gpw5181-7D, wmc574-4D, wmc609-1D and wmc160-5D, were identified with TGW stably in more than 3 environments, with R2 ranging from 4.60 to 10.50%. However, all of the association markers were detected in less than 3 environments for GLW. Association analyses in hexaploid wheat varieties indicated that a total of 208 marker-trait associations were identified based on 66 different SSR markers in 4 environments, and R2 ranged from 0.90 to 19.92% (Appendix D). Three markers, i.e., gdm33-1D, gpw5179-6D and wmc167-2D, were associated with GL stably in more than 3 environments, with R2 ranging from 11.02 to 19.99%. Three markers including cfd63-1D, gdm33-1D and wmc48-4D were associated with GW stably in more than 3 environments with R2 ranging from 8.44 to 15.69%. Three markers, i.e., gpw5179-6D, gwm5835D and gdm33-1D, were associated with GP stably in more than 3 environments with R2 ranging from 9.33 to 19.55%. Eleven markers including cfd12-5D, cfd3-5D, gpw50643D, gpw5067-3D, gpw5137-7D, wmc289-5D, wmc634-7D, wmc640-5D, cfd39-4D, gpw5094-3D and wmc416-6D, were stably associated with the GR in more than 3 environments, with R2 ranging from 7.43 to 15.35%. One marker, gdm331D, showed a stable association with GA in more than 3 environments with an R2 averaged 13.04%. Five markers including gdm8-3D, gpw5149-1D, gpw5181-7D, Xpsp3113-7D and gwm182-5D, were associated with TGW stably in more than 3 environments, with R2 ranging from 4.33 to 12.27%. Again, all of the association markers were detected in less than 3 environments for GLW. In this study, a total of 10 common marker-trait associations were identified in Ae. tauschii and the hexaploid wheat varieties in 5 traits (GA, GL, GR, GP and TGW) with 6 different markers (Table 4). The cfd4-3D, detected in 2 environments, was associated with GR and explained an averaged phenotypic variations of 1.51 to 9.71% in Ae. tauschii and 8.71 to 9.65% in wheat varieties, respectively. The gdm33, mapped on chromosome 1D, was detected in the association between GA and GP in 3 environments in Ae. tauschii and wheat varieties, explaining the average phenotypic variations of 12.43 to 19.55% and 2.04 to 14.33%, respectively. The gpw5137, mapped on chromosome 7D, was detected in association with GR in 2 environments in Ae. tauschii and hexaploid wheat varieties, explaining the average phenotypic variations of 13.87 and 14.15%, respectively. The gpw5179, mapped on chromosome 6D, was detected in association with GA, GL and GP in 2 to 4 environments in Ae. tauschii and the wheat vari-

AV

ZHAO Jing-lan et al. Journal of Integrative Agriculture 2015, 14(10): 1936–1948

Table 4 Common loci associated with grain shape and TGW between Ae. tauschii and hexaploid wheat varieties

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ZHAO Jing-lan et al. Journal of Integrative Agriculture 2015, 14(10): 1936–1948

eties, explaining the average phenotypic variations of 1.55 to 19.99% and 2.26 to 15.12%, respectively. The gwm583, mapped on chromosome 5D, was detected in association with GL and GP in 2 and 3 environments of Ae. tauschii and wheat varieties, and explaining the average phenotypic variations of 11.50 to 12.78% and 1.57 to 2.29%, respectively. The gpw5181, mapped on chromosome 7D, was detected in association with the TGW in both 2 environments of Ae. tauschii and wheat varieties, explaining the average phenotypic variations of 5.39 and 5.23%, respectively.

gdm33-A150, gdm33-A170, gpw5179-A230, gpw5179-A237 and gpw5179-A248) (Fig. 3). All of the alleles except gpw5179-A248, exhibited a positive phenotypic effect, indicating these alleles increasing the GA both in Ae. tauschii and in hexaploid wheat varieties. Similarly, in GP, 3 common loci (gdm33-1D, gpw5179-6D and gwm583-5D) identified in Ae. tauschii and wheat varieties, shared 7 common alleles (gdm33-A135, gdm33-A140, gdm33-A150, gdm33-A170, gpw5179-A230, gpw5179-A237 and gwm583-A140) (Fig. 4) between Ae. tauschii and wheat varieties. All of the alleles except gwm583-A140, showed a positive phenotypic effect, indicating that these alleles increased GP for grain in Ae. tauschii as well as in hexaploid wheat varieties. Two common loci (cfd4-3D and gpw5137-7D), contributing to the GR, shared 7 common alleles (cfd4-A245, cfd4-A250, cfd4-A255, gpw5137-A155, gpw5137-A157, gpw5137-A162 and gpw5137-A165) (Fig. 5) between Ae. tauschii and the hexaploid wheat varieties. The alleles including cfd4-A245, cfd4-A250, gpw5137-A155 and gpw5137-A162 exhibited a positive phenotypic effect that indicated a corresponding increase in the GR for the grains of Ae. tauschii and the wheat varieties, whereas cfd4-A255, gpw5137-A157 and gpw5137-A165 exhibited a negative phenotypic effect on the GR of Ae. tauschii and wheat varieties. As to the TGW, only 1 common locus (gpw5181-7D) was identified between Ae. tauschii and wheat varieties and no significant loci were detected for TGW (Fig. 6).

2.5. Allelic effect Six common SSR loci, identified in both Ae. tauschii and wheat varieties, were significantly associated with grain shape and TGW. For GL, 2 common loci (gwm583-5D and gpw5179-6D), sharing 4 common alleles (gpw5179-A230, gpw5179-A237, gpw5179-A248 and gwm583-A140), were identified in Ae. tauschii and hexaploid wheat (Fig. 2). Among the above mentioned alleles, the alleles gpw5179-A230 and gpw5179-A237 showed a positive phenotypic effect, indicating that these alleles increase GL in Ae. tauschii as well as in wheat varieties, whereas gpw5179-A248 showed a negative phenotypic effect on the GL of Ae. tauschii and wheat varieties. Regarding GA, 2 common loci (gdm33-1D and gpw51796D) were detected in Ae. tauschii and wheat varieties, sharing 7 common alleles (gdm33-A135, gdm33-A140,

Allele effect (mm)

gpw5179-6D 0.6 0.4 0.2 0.0 –0.2 –0.4 –0.6

A190 A195 A200 A202 A205 A210 A212 A220 A230 A237 A240 A245 A248 A250 A255 A260

Allele

Ae. tauschii T. aestivum

gwm583-5D

Allele effect (mm)

0.4

1941

A122 A127 A132 A137 A140 A145 A148 A150 A153 A160 A170 A175 A180 A185 A190 A195 A200 A210

0.2 0 –0.2 –0.4 –0.6 –0.8 Allele

Ae. tauschii T. aestivum

Fig. 2 The phenotypic effect of the marker alleles at loci that are significantly associated with grain length (GL).

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Allele effect (mm2)

gdm33-1D 3 2 1 0 –1 –2 –3

A115 A120 A125 A130 A135 A138 A140 A142 A150 A170 A175 A180 A185 A195 A200 A205 A215 A220 A225

Allele

Ae. tauschii T. aestivum

gpw5179-6D

Allele effect (mm2)

2

A190 A195 A200 A202 A205 A210 A212 A220 A230 A237 A240 A245 A248 A250 A255 A260

1 0 –1 Allele

–2

Ae. tauschii T. aestivum

Fig. 3 The phenotypic effect of the marker alleles at loci that are significantly associated with grain area (GA).

gdm33-1D

Allele effect (mm)

2.0

A115 A120 A125 A130 A135 A138 A140 A142 A150 A170 A175 A180 A185 A195 A200 A205 A215 A220 A225

1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5

Allele

Ae. tauschii T. aestivum

gwm583-5D

Allele effect (mm)

1.0

A122 A127 A132 A137 A140 A145 A148 A150 A153 A160 A170 A175 A180 A185 A190 A195 A200 A210

0.5 0.0 –0.5 –1.0 –1.5 –2.0

Allele

Ae. tauschii T. aestivum

Allele effect (mm)

gpw5179-6D 1.0

A190 A195 A200 A202 A205 A210 A212 A220 A230 A237 A240 A245 A248 A250 A255 A260

0.5 0.0 –0.5 –1.0 –1.5

Allele

Ae. tauschii T. aestivum

Fig. 4 The phenotypic effect of the marker alleles at loci that are significantly associated with grain pemimeter (GP).

ZHAO Jing-lan et al. Journal of Integrative Agriculture 2015, 14(10): 1936–1948

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cfd4-3D A212 A225 A230 A235 A237 A238 A240 A245 A246 A250 A252 A255 A258 A260 A270 A275 A285 0.06

Allele effect

0.04 0.02 0 –0.02 –0.04 –0.06

Allele

Ae. tauschii T. aestivum

gpw5137-7D 0.03

A132 A140 A146 A149 A150 A153 A155 A157 A158 A162 A163 A165 A168 A170 A175 A180 A190 A205

Allele effect

0.02 0.01 0.00 –0.01 –0.02 –0.03 –0.04

Allele

Ae. tauschii T. aestivum

Fig. 5 The phenotypic effect of the marker alleles at loci that are significantly associated with grain roundness (GR).

gpw5181-7D A115 A120 A125 A130 A135 A148 A149 A150 A152 A157 A158 A163 A165 A167 A170 A172 A175 A180 A187 4

Allele eeffect (mg)

3 2 1 0 –1 –2 –3

Allele

Ae. tauschii T. aestivum

Fig. 6 The phenotypic effect of the marker alleles at loci that are significantly associated with thousand-grain weight (TGW).

3. Discussion 3.1. Population structure and the genetic discovery in the grain traits of Ae. tauschii and hexaploid wheat varieties The D genome of T. aestivum was reported to originat from the southeastern or southwestern Caspian Sea in Iran, and the subsp. strangulata is most likely the donor of the wheat D genome (Dvorak et al. 1988). However, it is unknown

which classified sublineage was the origin of the D genome of hexaploid wheat. Dvorak et al. (1988) suggested that the D-genome of hexaploid wheat included several sources, and the subsp. strangulata is a possible major D genome donor. Some recent reports supported the multiple-origin hypothesis of the D genome in hexaploid wheat (Lelley et al. 2000; Wang et al. 2013). However, the number of sources contributing to speciation of the hexaploid wheat is still under discussion. Our data indicated a direct genetic relationship between Ae. tauschii and the hexaploid wheat based on the

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SSR marker-trait associations. Polyploid speciation is accompanied by a ploidy bottleneck, and a small number of plants contributed to the formation of a new polyploid species constrain its initial genetic diversity (Dubcovsky et al. 2007). Despite the large genetic diversity in Ae. tauschii, only a few Ae. tauschii accessions participated in the origin of hexaploid wheat that resulted in limited D genome diversity in wheat (Dvorak et al. 1988; Dubcovsky et al. 2007). Ae. tauschii has a wide natural distribution in central Eurasia, spreading from northern Syria and Turkey to western China. In previous studies, the natural variation of Ae. tauschii was analyzed for several traits, including flowering time, morphological traits and hybrid lethality with the tetraploid wheat (Matsuoka et al. 2007, 2008, 2009; Takumi et al. 2009a). Hexaploid wheat synthetic lines can be artificially produced through allopolyploidization between the tetraploid wheat and Ae. tauschii, implying that agronomically important genes from natural variation in the Ae. tauschii population are available for wheat breeding through the construction of synthetic wheat (Takumi et al. 2009b). Knowledge of the genetic structure of the collections deposited in the gene banks is essential to select the accessions suitable for different genetic and

breeding purposes. Molecular marker is a powerful tool to study the genetic structure of plant populations. The molecular marker can also aid in tracing the geographic origin of accessions by comparing genetic fingerprints of the unknown accession with those of diverse germplasms from different regions. Microsatellites or simple sequence repeats (SSRs) are very useful codominant molecular markers that are known for their highly reproducible and polymorphic. In the present study, 166 Ae. tauschii accessions were segregated into 6 sub-groups using 139 SSR markers based on the Bayesian clustering approach, which showed a significant difference in the investigated traits and the respective grain character. Group I showed the least GR (0.75 for average), whereas the GP (14.76 mm) and GL (6.17 mm for average) values increased, indicating that this group has a long and plump grain character (Table 5). In Group II, the GW (2.86 mm), GA (12.30 mm), and TGW (12.96 g) values were the lowest, indicating that this group has the effect for a thin grain. Group III appeared to possess the most GP and the longest GL, implying a plump grain character. Both groups IV and V have a relatively small GA and GR, indicating a small grain character. Group VI possesses the most TGW and GW, exhibiting large and plump grain

Table 5 Phenotypic performances of sub-group for grain shape and TGW in 3 environments in Ae. tauchii Trait GL

GW

GLW

GP

GR

GA

TGW

　

Environment E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV E1 E2 E3 AV

I 6.32 6.02 6.17 6.17 3.03 2.89 2.97 2.97 2.11 2.12 2.11 2.11 15.35 14.19 14.76 14.76 0.72 0.77 0.74 0.75 13.46 12.51 12.99 12.99 13.89 13.33 13.42 14.50

II 6.07 5.99 6.02 6.03 2.93 2.77 2.87 2.86 2.08 2.17 2.11 2.12 14.74 13.97 14.35 14.35 0.73 0.78 0.74 0.75 12.55 12.04 12.31 12.30 12.50 12.48 12.74 14.13

Sub-group III 6.38 6.28 6.36 6.34 3.26 3.04 3.13 3.15 1.98 2.08 2.04 2.03 15.75 14.86 15.35 15.34 0.74 0.76 0.75 0.75 14.55 13.63 14.16 14.17 14.21 14.24 14.08 15.09

IV 5.76 5.84 5.83 5.81 2.99 2.83 2.90 2.91 1.95 2.08 2.03 2.02 14.38 13.80 14.10 14.09 0.74 0.77 0.75 0.75 12.04 11.98 12.01 12.01 13.66 13.44 13.55 13.95

V 5.91 5.91 5.93 5.91 3.24 2.99 3.14 3.13 1.86 2.00 1.92 1.91 14.80 14.15 14.49 14.48 0.78 0.78 0.79 0.78 13.46 12.58 13.05 13.05 14.18 14.32 14.23 14.44

VI 5.91 6.30 6.10 6.10 3.26 3.31 3.27 3.28 1.84 1.93 1.89 1.89 14.94 15.26 15.10 15.10 0.76 0.79 0.76 0.77 13.51 14.82 14.15 14.16 15.20 15.26 15.16 15.01

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characteristics that may be useful for increasing the yield potential in hexaploid wheat.

3.2. Allelic variation of loci that control grain traits In hexaploid wheat, the grain size and grain shape are important to grain quality and yield; therefore, these traits have drawn major attention from the wheat breeding community all over the world. Genetic dissection of the grain traits using QTL interval mapping and association mapping, followed by the use of associated molecular markers for marker-assisted selection, is an active area of research (Breseghello and Sorrells 2006; Kumar et al. 2006; Sun et al. 2010). Several meta-QTL have already been identified, either through bi-parental linkage mapping (Reif et al. 2011) or through association mapping, using a worldwide panel of 96 accessions in one study (Neumann et al. 2011) and a collection of 207 European soft winter wheat lines (Reif et al. 2011). In this study, a total of 16 alleles were identified from 6 common SSR loci between Ae. tauschii and hexaploid wheat and were found to be significantly associated with grain shape and TGW (Figs. 2–6, Table 6). We noted that the distribution of the alleles were uneven in the 6 investigated traits. There were relatively common alleles between Ae. tauschii and wheat varieties for GA (7), GP (7) and GR (8). Three common alleles were detected in GL at the gpw5179-6D and gwm583-5D loci (Fig. 2). Of these, 17 alleles showed a positive effect in Ae. tauschii and wheat varieties, indicating that the alleles increased the phenotypic value of the related grain traits. These common alleles may be the conservative alleles during the process of evolution and selection from Ae. tauschii to hexaploid wheat, suggesting that the possible origin of the alleles influence the grain size and shape traits. The alleles at 6 common loci that were significantly associated with 5 investigated grain traits (GA, GP, GR, GL and TGW) appeared remarkably different between Ae. tauschii and wheat varieties. There were 15 (78.9%) different alleles at gdm33-1D (Fig. 3), 13 (81.3%) at gpw5179-6D (Figs. 2–4), 17 (94.4%) at gwm583-5D (Figs. 2 and 4), 13 (76.5%) at cfd4-3D (Fig. 5), and 14 (77.8%) at

gpw5137-7D (Fig. 5, Table 5), respectively. No common locus was detected between Ae. tauschii and hexaploid wheat in GW. However, one locus (gpw5181-7D) was common between Ae. tauschii and hexaploid wheat in TGW (Fig. 6). Interestingly, gpw5181-7D, which showed the existence of many QTLs surrounding the loci associated with grain traits including GW (Mir et al. 2012), TGW (Wang et al. 2009; Nezhad et al. 2012), and grain yield (Marza et al. 2006) in previous studies, was significantly associated with TGW in this study. The locus involved 4 alleles in wheat and 15 in Ae. tauschii with no common alleles between them. The gpw5181-A120 detected in 99 of the 130 wheat accessions shows a remarkably high positive allele effect on the TGW, indicating that it could be one of the alleles contributing to the thousand-grain weight during the evolution and selection from Ae. tauschii to the wheat varieties. These differences in the alleles at common loci indicate the genetic differentiation of the loci controlling the grain shape and the TGW between Ae. tauschii and wheat varieties. The allelic variation at 6 SSR loci between Ae. tauschii and T. aestivum indicates the great potential for discovery and utilization of Ae. tauschii in wheat breeding. The D genome of wheat is known to contain QTL associated with grain traits, drought tolerance, and pathogen resistance (Genc et al. 2010; Mir et al. 2012; Vazquez et al. 2012). Further work may be conducted by comparative genomics approaches, such as differential gene loss and molecular markers to pursue the association in the variation at loci with agriculturally important traits, such as the grain size and grain shape, between Ae. tauschii and T. aestivum.

4. Conclusion In this study, significant associations between grain traits and SSR markers was found in Ae. tauschii and hexaploid wheat, while used mixed linear model approach which considers population structure and kinship. Our results clearly show that there are 6 common SSR loci present in both Ae. tauschii and hexaploid wheat, which are significantly associated with the 5 investigated grain traits (i.e., GA, GP, GR, GL, and TGW) and in total, 16 alleles derived from the 6 aforementioned SSR loci were shared by Ae. tauschii and

Table 6 the common loci and alleles associated with the investigated grain traits between Ae. tauschii and hexaploid wheat varieties Loci gdm33 gpw5179 gwm583 cfd4 gpw5137 gpw5181 Total

Ae. tauchii 13 8 11 12 13 15 72

T. aestivum 10 11 8 9 9 4 51

No. of common alleles 4 3 1 4 4 0 16

No. of different alleles 15 13 17 13 14 19 91

Total 19 16 18 17 18 19 107

Trait GA/GP GA/GP/GL GP/GL GR GR TGW

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hexaploid wheat. The existence of common alleles may explain the evolutionary process and the selection between Ae. tauschii and hexaploid wheat. Furthermore, the genetic differentiation of grain shape and thousand-grain weight were observed in the evolutionary developmental process from Ae. tauschii to hexaploid wheat.

5. Materials and methods 5.1. Plant materials A total of 130 wheat varieties and 193 Ae. tauschii accessions worldwide were used in this study (Appendix B). Briefly, this 323 accessions were tested in field trials carried out during 3 cropping seasons (2010–2011 (E1), 2011–2012 (E2), and 2012–2013 (E3)) at the experimental station of Shandong Agricultural University in Tai’an, Shandong Province, China. The plant materials were sown on October 7, 2010 (E1), October 9, 2011 (E2) and October 7, 2012 (E3), respectively. For each plot, the number of days to germination (from sowing to germination), the number of days to flowering (from germination to flowering) and the number of days to harvesting (germination to harvesting) were recorded every year. The mean of replicated days to flowering and harvesting date was used for each plant. For 193 Ae. tauschii accessions, the flowering time ranged from 197 to 224 d and the harvesting time ranged from 229 to 248 d. For 130 wheat varieties, the flowering time ranged from 194 to 204 d; the harvesting time ranged from 222 to 236 d. All 3 environments were irrigated fields, and each plot consisted of 5 rows, 1.5 m long and spaced 25 and 50 cm apart, with 50 and 25 plants in each row and 2 replicates, respectively, for wheat varieties and Ae. tauschii.

5.2. Phenotype analysis A minimum of 200 grains from each sample was scanned on a flat-bed scanner (ScanExpress, Mustek Systems Inc., Hsin-Chu, Taiwan of China). An image of each grain was obtained as the aerial view of the ventral side of the grain. The grain length (GL), the grain width (GW), the grain perimeter (GP), and the grain area (GA) were measured using Image-Pro® Plus ver. 4.5 software (Media Cybernetics Inc., MD, USA). The grain length/width ratio (GLW) and the roundness (GR) (ratio of perimeter squared to area) were calculated based on the primary data. The thousand-grain weight (TGW) was determined by the average of two thousa nd-grain weights.

5.3. Genotype analysis Genomic DNA was extracted from the leaves of Ae. tauschii

and hexaploid wheat using the cetyltrimethyl ammonium bromide (CTAB) method (Saghai-Maroof et al. 1984). A total of 139 simple sequence repeat (SSR) markers were selected and their chromosome locations were obtained from the GrainGenes database (http://wheat.pw.usda.gov/ GG2). The markers were analyzed using PCR (denaturation at 95°C for 3 min, followed by 35 cycles at 94°C for 45 s, annealing at a Tm temperature of 60°C for 45 s, 72°C for 90 s, and a final extension step at 72°C for 10 min). The PCR products were separated on a 6% gel using polyacrylamide gel electrophoresis (PAGE).

5.4. Allelic diversity and population structure The alleles revealed by 139 SSR markers in wheat and Ae. tauschii were estimated using PowerMarker V3.25 (http:// www.powermarker.net), including the allele number, allele frequency, and polymorphism information (PIC). Nei’s measure of the average genetic diversity (1973) was calculated for each locus according to the following formula: He=1–Σkj=1 pj2, where pj is the frequency of the jth allele and k is the total number of alleles. The model-based (Bayesian) cluster software program STRUCTURE 2.2 (Pritchard et al. 2000) was chosen to estimate the population structure of the 130 wheat germplasm accessions and the 193 Ae. tauschii accessions with 21 unlinked markers that are distributed across 7 chromosomes of the D genome. The software was run using a burn-in of 10 000 and a run length of 100 000, with an admixture model and correlated allele frequencies for inferring the number (K) of the subpopulations. The K number was set from 2 to 15. Five independent STRUCTURE runs were calculated for each K, and an average likelihood value across the 5 runs was calculated.

5.5. Statistical analyses and association mapping A population structure Q+K (kinship) model was used to account for the population structure and the relationship of the individual plants among wheat and Ae. tauschii. A relative kinship matrix was calculated on the basis of 139 SSR loci using a method proposed by Ritland (1996), which is built into the program SPAGeDi (Hardy and Vekemans 2002). The Q+K model was performed with a mixed linear model (MLM) in TASSEL V2.1 (Yu et al. 2005; Bradbury et al. 2007). The default run parameters of the convergence criterion were set at 1E–4 and the maximum number of iterations was set at 200 for this study. The allele effects of the SSR loci, which were significantly associated with the detected traits, were computed using the method proposed by Breseghello and Sorrels (2006). The marker alleles with less than 5 counts in the population

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were combined with the missing data and the null alleles. The phenotypic effect of the marker alleles was calculated according to the following formula: αi=Σxij/ni–ΣNk/nk, where αi is the phenotypic effect value of a marker allele, xij is the phenotypic value of the jth accession of the ith allele, ni is the number of accessions carrying the ith allele, Nk is the phenotypic value of the kth accession carrying the null allele, and nk is the number of accessions carrying the null allele. If αi>0, the allele is considered positive for the associated trait, and if αi<0, the allele is considered negative for the associated trait.

Acknowledgements We acknowledge financial supports by the National 973 Program of China (2014CB138100), the National Natural Science Foundation of China (31171553, 31471488 and 31200982), and the National High-Tech R&D Program of China (2011AA100102). Appendix associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm

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