International Journal of Genomics

International Journal of Genomics / 2017 / Article
Special Issue

The Promise of Agriculture Genomics

View this Special Issue

Research Article | Open Access

Volume 2017 |Article ID 2413150 |

Peng Jiang, Ping-Ping Zhang, Xu Zhang, Hong-Xiang Ma, "Genetic Diversity and Association Analysis for Solvent Retention Capacity in the Accessions Derived from Soft Wheat Ningmai 9", International Journal of Genomics, vol. 2017, Article ID 2413150, 8 pages, 2017.

Genetic Diversity and Association Analysis for Solvent Retention Capacity in the Accessions Derived from Soft Wheat Ningmai 9

Academic Editor: Mihai Miclăuș
Received02 Aug 2016
Revised14 Oct 2016
Accepted16 Jan 2017
Published05 Feb 2017


Solvent retention capacity (SRC) test is an effective method for quality evaluation of soft wheat. Ningmai 9 is a founder in soft wheat breeding. The SRC and genotype of Ningmai 9 and its 117 derivatives were tested. Association mapping was employed to identify the quantitative trait loci (QTL) associated with SRCs. Ningmai 9 had the allele frequency of 75.60% and 67.81% to its first- and second-generation derivatives, respectively, indicating higher contribution than theoretical expectation. Neighbor-joining cluster based on the genotyping data showed that Ningmai 9 and most of its first-generation derivatives were clustered together, whereas its second-generation derivatives were found in another group. The variation coefficients of SRCs in the derivatives ranged from 5.35% to 8.63%. A total of 29 markers on 13 chromosomes of the genome were associated with the SRCs. There were 6 markers associated with more than one SRC or detected in two years. The results suggested that QTL controlling SRCs in Ningmai 9 might be different from other varieties. Markers Xgwm44, Xbarc126, Xwmc790, and Xgwm232 associated with SRCs in Ningmai 9 might be used for quality improvement in soft wheat breeding.

1. Introduction

Soft wheat flour of low protein content is usually associated with the cookie quality [1], which produces good quality cookies with a large spread factor, such as low thickness, tender texture with smaller particle size, and low water absorption. Soft wheat yields less flour with a smaller average particle size and less damaged starch [2].

In comparing with hard wheat, solvent retention capacity (SRC) is used more often for evaluating the quality of soft wheat in cookie making [3]. SRC is the weight of solvent held by flour after centrifugation and draining. SRC tests were developed by Slade and Levine (1994) to estimate grain and end-use quality in soft wheat [4]. They are all based on a mixture of flour plus one of four different solutions: water, 5% sodium carbonate (NaCO3), 5% lactic acid, and 50% sucrose to predict water-holding capacity, damaged starch, gluten strength, and water soluble pentosan (arabinoxylan), respectively [3]. SRC was mainly determined by genotype [57]; however, most of the previous studies concerned the evaluation of SRC in different genotypes with various treatments, whereas the genetic mechanism of SRC received little attention.

Understanding genetic mechanisms and the identification of quantitative trait loci (QTL) associated with the components regulating end-use traits are the basis for quality improvement in wheat. Several mapping studies have been conducted to locate QTL associated with baking quality in wheat. However, most of them were conducted using hard wheat population. In soft wheat, Smith et al. (2011) reported large effect QTL for quality on chromosomes 1B and 2B [8]. Cabrera et al. (2015) identified 26 regions as potential QTL in a diversity panel and 74 QTL in all 5 biparental mapping populations [9].

Association mapping is a method to test the association between molecular markers and QTL based on linkage disequilibrium [10]. In recent years, it has been widely used for QTL detection in main crops, such as maize, wheat, and rice [1113]. Generally, natural populations with wide genetic basis were used for association mapping [14, 15]. In soft wheat, Cabrera et al. (2015) identified 26 regions as potential QTL in a diversity panel from the soft wheat breeding program in USA by using an association mapping approach [9]. Zhang et al. (2015) discovered several favorable allelic variations for SRCs by association mapping with a natural population including 176 varieties (lines) from China [16].

Association mapping on founder parents and its derivatives can find some important QTL and favorable allelic variations in founder parent, which can be further used for marker assisted selection to produce more favorable varieties [17]. Ningmai 9 is a soft wheat cultivar with desirable quality and has been widely used in wheat production and as parent in the Yangtze River to Huai River valley area in China. A total of 20 cultivars derived from Ningmai 9 have been released in the past 10 years. Ningmai 9 has high general combining ability in SRCs [18]; however, the QTL and chromosome regions associated with SRCs in Ningmai 9 were unclear. In this study, the genotypes of Ningmai 9 and its derivatives were screened with SSR molecular markers covering whole-genome; meanwhile the phenotypes of SRCs were analyzed in two consecutive growth seasons. The genetic structure, genetic similarity, and association mapping were analyzed to reveal the relationship between Ningmai 9 and its derivatives and to identify molecular markers associated with SRCs.

2. Materials and Methods

2.1. Plant Materials and Phenotyping

Ningmai 9 and its 117 derivatives including 39 lines of first generation and 78 lines of second generation were used in this study (Table 1). The materials were planted in 2014 and 2015 at the experimental farm of Jiangsu Academy of Agricultural Sciences in Nanjing, China. Each line was planted in a plot comprising 3 rows with two replications. Each row was sowed with 50 seeds with the length of 1.3 m and a row-to-row distance of 0.25 m. After harvest and milling, the flour was tested for SRCs according to AACC 56-11 [19, 20]. The SRC of water, sodium carbonate, lactic acid, and sucrose were described as WSRC, SCSRC, LaSRC, and SuSRC, respectively.


ParentL1Ningmai 9

1st generationL2Ningmai 13
L3Ningmai 14
L4Ningmai 16
L5Shengxuan 6
L6Yangmai 18
L7Yangfumai 4
L9Nannong 0686
L10Ningmai 18
L11Ning 0556
L12Ning 07123
L13Ning 07119
L14Ning 0853
L15Ning 0866
L16Ning 0894
L17Ning 08105
L18Ning 0561
L19Ning 0564
L20Ning 0565
L21Ning 0417
L22Ning 0418
L23Ning 0422
L24Ning 0311
L25Ning 0316
L26Ning 0319
L27Ning 0320
L28Ning 0327
L29Ning 0331
L35Ning 9-11
L36Ning 9-36
L37Ning 9 Large 41
L38Ning 9 Large 44
L39Ning 9 Large 76
L40Ning 9 Large 78
L41Ning 9 Large 80

L30Ning 0798
L31Ning 07117
L34Ning 0797
L42Ning 0862
L43Ning 0869
L44Ning 0872
L45Ning 0880
L46Ning 0882
L47Ning 0884
L48Ning 0887
L49Ning 0893
L50Ning 0895
L51Ning 0897
L52Ning 0898
L53Ning 0899
L54Ning 08102
L55Ning 08104
L56Ning 08108
L57Ning 08110
L58Ning 08115
L59Ning 08116
L65Zhenmai 166
L66Ning 0867
L67Ning 0881
L68Ning 0883
L69Ning 0886
L70Ning 0896
L71Ning 08109
L72Ning 08111
2nd generationL73Ning 08112
L74Ning 08113

2.2. Genotype Analysis

DNA was extracted from fresh leaves using a CTAB procedure according to Saghai-Maroof et al. (1984) [21]. One hundred and eighty-five polymorphic simple sequence repeat (SSR) primer pairs were used to screen Ningmai 9 and its 117 derived lines in the study. These markers were randomly distributed across the wheat genome, and each chromosome included 5–14 markers with an average of 8.8 markers. Map positions of markers were based on the linkage map reported by Somers et al. (2004) [22].

Each 10 μL PCR contained 1 μL 10 × PCR buffer, 0.6 μL 15 mM MgCl2, 0.8 μL 2 mMdNTP, 1 μL 0.02 μM of each primer, 0.1 μLTaq DNA polymerase, 1 μL 0.02 μM template DNA, and 3.5 μL deionized water. The cycling system consisted of an initial denaturation step of 94°C/5 min, followed by 36 cycles of 94°C/45 s, 50~60°C/45 s, 72°C/60 s, and a final extension of 72°C/10 min. Amplification bands were electrophoretically separated through nondenaturating 6% polyacrylamide gels and visualized by silver staining.

2.3. Data Processing and Analysis

Excel 2007 was used for data preparation; ANOVA was performed using SPSS 17.0. Neighbor-joining cluster was performed with Mega 6.0 [23]. Both the matrix and matrix were determined using STRUCTURE v2.3.4 [24]. Five independent simulations were processed for each , ranging from 1 to 8, with a 10,000 burn-in length and 100,000 iterations. The association analysis was calculated using the mixed linear model (MLM) method incorporated into the TASSEL 3.0 software [25]. The significant marker-trait associations were declared for .

3. Results

3.1. Genetic Contribution of Ningmai 9 to Its Derivatives

A total of 490 alleles were detected with 1–7 and an average of 2.6 alleles per locus. The ratio of allele frequency between Ningmai 9 and its derivatives on the chromosomes ranged from 55.71% to 88.29% with an average of 75.60% for first generation and from 56.33% to 83.50% with an average of 67.81% for second generation (Table 2), which indicated that Ningmai 9 had a higher contribution to its derivatives than theoretically expected. Both first and second generation had highest allele frequency on chromosome 4A. The first generation possesses the higher allele frequency compared to the second on all chromosomes except for chromosome 6D.

ChromosomeAllele frequency (%)
1st generation2nd generation




Genome wide allele frequency with Ningmai 9 (%)
 1st generation75.60
 2nd generation67.81

3.2. Population Structure Analysis and Cluster Analysis

In order to eliminate the spurious association caused by population structure of the materials, the number of populations was calculated according to the method by Evanno et al. (2005) [26]. Two populations in the materials were previously reported in our research [27].

Neighbor-joining cluster based on the genotyping data also showed that there were 2 groups in the materials (Figure 1). Ningmai 9 and most of its first-generation derivatives were clustered together, whereas its second-generation derivatives were found in another group. Yangfumai 4 was distantly clustered with those two groups since it was a mutant induced from hybrid seed treated with radiation.

3.3. Phenotype Analysis

There were significant variations among the derivatives of Ningmai 9 for all SRCs. The value of each SRC of the derivatives was higher, on average, than that of Ningmai 9, and the variations were high with coefficients of variation (CV) ranging from 5.35% in SuSRC (2014) to 8.63% in WSRC (2015) (Table 3).

IndexYearNingmai 9MeanStdevMinMaxCV (%)


ANOVA revealed significant effects of genotype for all SRCs (Table 4). Year effect was also significant for SCSRC. ANOVA showed that the effect of genotype by year was not significant for each SRC. There was no significant difference among generations for SRCs except for SCSRC, though the values of all the SRCs in second generation were larger than that in first generation and in Ningmai 9 except for the value of LaSRC.

Index valueMultiple comparison test (S-N-K method)
GenotypeYearGenotype × yearNingmai 91st generation2nd generation


and show significant difference at 0.01 and 0.5 level, respectively; different small letters in the same row show significant difference at 0.05 level.

There was significant positive correlation between the two years for all SRCs (Table 5). The correlation between different SRCs was identical in the two years; SuSRC was significantly positively correlated with WSRC, SCSRC, and LaSRC, and there was also significant correlation between WSRC and SCSRC.



shows significant difference at 0.01 level. The correlation analysis for the same trait between 2014 and 2015 is marked on the diagonal; the correlation analysis among different traits in 2014 is marked below the diagonal, whereas the correlation analysis among different traits in 2015 is marked above.
3.4. Association Analysis

A total of 29 markers on 13 wheat chromosomes were associated with the SRCs (Table 6). Five markers associated with WSRC were identified on chromosomes 4A, 4D, 7B, and 7D, 21 markers on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 4A, 6A, 6B, 7A, 7B, and 7D were associated with SCSRC, two markers on chromosome 3B were associated with LaSRC, and four markers on chromosomes 1D, 2D, and 3B were associated with SuSRC. The QTL related to such markers could explain 5.12%~12.05% of the phenotypic variation. Xgwm44 was associated with WSRC and SCSRC, and Xwmc754 and Xwmc326 were associated with both LaSRC and SuSRC. Xbarc126, Xwmc517, Xgwm484, Xwmc754, Xwmc326, and Xgwm232 were detected in both years, and wmc754 and wmc326 associated with LaSRC presented different alleles in two years. Most of the alleles of the marker associated with more than one SRC or detected in two years had negative effects on their corresponding SRCs, which were a benefit for soft wheat quality.

(%)Effect (allele) (%)Effect (allele)

WSRCXwmc4684AL4.74 × 10−36.61− (134)
Xwmc894DS8.10 × 10−35.94− (140)
Xwmc5177BL9.68 × 10−35.71+ (183)
Xgwm447DS8.11 × 10−35.78− (196)1.37 × 10−38.84− (196)
Xbarc1267DS6.48 × 10−36.13− (170)4.34 × 10−410.80− (170)

SCSRCXgwm1531BL5.65 × 10−36.02− (188)
Xcfd721DL4.55 × 10−36.22+ (310)
Xgwm2321DL7.64 × 10−48.57+ (144)
Xwmc6582AL6.82 × 10−35.44+ (250)
Xgwm2572BS9.54 × 10−35.16− (186)
Xgwm5392DL6.32 × 10−35.56+ (160)
Xgwm1022DS3.04 × 10−36.57+ (142)
Xgwm4842DS1.94 × 10−410.81− (179)2.90 × 10−36.96− (179)
Xwmc2313B1.09 × 10−38.17+ (240)
Xwmc7773B4.13 × 10−49.48− (100)
Xwmc6533B6.05 × 10−35.83− (160)
Xwmc2194AL6.68 × 10−35.47+ (160)
Xgwm1696AL9.54 × 10−35.68− (190)
Xwmc3976BL9.68 × 10−512.05
Xwmc7907A1.68 × 10−37.42− (108)
Xwmc8097A6.04 × 10−35.81− (180)
Xwmc3117BL9.64 × 10−35.12+ (120)
Xwmc6347DL4.16 × 10−410.17+ (210)
Xgwm4377DL6.04 × 10−35.81− (110)
Xgwm447DS6.61 × 10−35.88− (183)
Xcfd147DS1.80 × 10−37.62− (100)

LaSRCXwmc7543B2.09 × 10−38.77− (160)7.89 × 10−36.39+ (152)
Xwmc3263B7.20 × 10−37.15+ (186)8.29 × 10−37.00+ (186)

SuSRCXgwm2321DL5.17 × 10−410.44− (144)4.27 × 10−37.20− (144)
Xgwm3492DL5.97 × 10−36.47+ (310)
Xwmc7543B5.96 × 10−36.68− (160)
Xwmc3263B4.38 × 10−37.35+ (186)

The number in brackets following “+” or “−” represents the allele of markers, and “+” and “−” indicate a positive or negative effect by the allele of markers.

4. Discussion

Ningmai 9 is a soft wheat variety with stable soft wheat quality, high yield, wide adaptation, and resistance to multiple diseases including Fusarium head blight, soil born mosaic virus, and sharp eye spots released in 1997. Since 2006, 20 wheat cultivars derived from Ningmai 9 have been released to wheat production of the Yangtze River to the Huai River regions in China. As a founder parent, Ningmai 9 has a high general combining ability in sterile spikelet number (negative effect), grain weight per spike, protein content (negative effect), SRC (negative effect), and Fusarium head blight resistance, which means that it is easy to produce desirable traits in progenies [18]. At genomic level, founder parents have more favorable allelic variations than other varieties, and the genetic composition of new varieties is more similar to founder parent rather than the average value of parents. In this study, the genetic contribution of Ningmai 9 to its first and second generation was 75.60% and 67.81%, respectively, which were both significantly higher than theoretical expectation of 50% and 25%. The result was consistent with previous reports on other founder parents, such as Triumph/Yanda 1817 [28], Orofen [29], Bima 4 [30], and Zhou 8425B [31].

Solvent retention capacity (SRC) has been considered as an important breeding tool for predicting flour functionality of different wheat for different end uses ever since it has been developed [4, 32, 33]. SRC addresses the relative contributions to water absorption of each flour component using four different solvents: water, lactic acid, sodium carbonate, and sucrose. While WSRC has been associated with the overall water-holding capacity of all flour constituents, LaSRC is associated more specifically with the glutenin network formation and gluten elasticity or strength of flour. SCSRC is closely related to the amount of damaged starch of the flour, while SuSRC relates more specifically to the concentration of arabinoxylan and gliadin [19]. In this study, SRCs of Ningmai 9 and its derivatives were measured in two consecutive years, and all the SRCs of Ningmai 9 were lower than those of the derivatives on average, as wheat breeders did not take soft wheat as the only goal in wheat breeding. Therefore, genetic improvement for soft wheat quality would be strengthened in the future.

Identification of molecular marker associated with desired traits is a basis for marker assisted selection in wheat breeding. Association mapping is an effective method for identifying related markers. In this study, a total of 29 markers on 13 chromosomes were associated with the SRCs. Five markers associated with WSRC were identified on chromosomes 4A, 4D, 7B, and 7D. Cabrera et al. [9] and Carter et al. [34] discovered QTL related to WSRC on chromosomes 4A and 4D, respectively, and the QTL on 4A was close to Xwmc468 detected in this study. Twenty-one markers on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 4A, 6A, 6B, 7A, 7B, and 7D were associated with SCSRC in the study. Wmc751 on 3B reported by Carter et al. [34] was located at the interval between Xwmc777 and Xwmc653, and Xgwm44 on 7D was also reported by Zhang et al. [16]. Smith et al. (2011) found that a QTL on 2B associated with SCSRC was close to Xgwm257 by using 171 families from the cross Foster/Pioneer “25R26” [8]. Some markers on chromosomes 1A, 1B, 3A, 3B, 6A, and 7A related to SCSRC were also reported [8, 9, 16], but a little far from the ones we detected, as the markers on multiple chromosomes including chromosomes 1D, 2D, and 3B associated with LaSRC and SuSRC. There was high correlation between two years for all SRCs, but only a few markers were repeatedly detected, which might be due to a limited number of markers used in this study. The association mapping in Ningmai 9 and its derivatives showed that SRCs were determined by lots of minor QTL effects but also the environment, which suggest that the genetic mechanism of SRCs was complex in Ningmai 9 and QTL controlling SRCs might differ from other varieties. The favorable allelic variations of Xgwm44, Xbarc126, Xwmc790, and Xgwm232 associated with SRCs in Ningmai 9 may be used for quality improvement in soft wheat breeding.

Competing Interests

The authors declare that they have no competing interests.


This work was partially supported by the national key project for the research and development of China (2016YFD0100500) and the indigenous innovation foundation of Jiangsu provincial agricultural science and technology (CX[14]2002), China Agricultural Research System Program (CARS-03).


  1. M. Moiraghi, L. Vanzetti, C. Bainotti, M. Helguera, A. León, and G. Pérez, “Relationship between Soft wheat flour physicochemical composition and cookie-making performance,” Cereal Chemistry, vol. 88, no. 2, pp. 130–136, 2011. View at: Publisher Site | Google Scholar
  2. A. Abboud, R. C. Hoseney, and G. Rubenthater, “Effect of fat and sugar in sugar-snap cookies and evaluation of tests to measure cookie flour quality,” Cereal Chemistry Journal, vol. 62, pp. 124–129, 1985. View at: Google Scholar
  3. M. Kweon, L. Slade, and H. Levine, “Solvent retention capacity (SRC) testing of wheat flour: principles and value in predicting flour functionality in different wheat-based food processes and in wheat breeding—a review,” Cereal Chemistry, vol. 88, no. 6, pp. 537–552, 2011. View at: Publisher Site | Google Scholar
  4. L. Slade and H. Levine, “Structure-function relationships of cookie and cracker ingredients,” Cereal Chemistry Journal, vol. 81, pp. 261–266, 1994. View at: Google Scholar
  5. M. J. Guttieri, D. Bowen, D. Gannon, K. O'Brien, and E. Souza, “Solvent retention capacities of irrigated soft white spring wheat flours,” Crop Science, vol. 41, no. 4, pp. 1054–1061, 2001. View at: Publisher Site | Google Scholar
  6. M. J. Guttieri and E. Souza, “Sources of variation in the solvent retention capacity test of wheat flour,” Crop Science, vol. 43, no. 5, pp. 1628–1633, 2003. View at: Publisher Site | Google Scholar
  7. Q. J. Zhang, Y. Zhang, Z. H. He, and R. J. Pena, “Relationship between soft wheat quality traits and cookie quality parameters,” Acta Agronomica Sinica, vol. 31, no. 9, pp. 1125–1131, 2005. View at: Google Scholar
  8. N. Smith, M. Guttieri, E. Souza, J. Shoots, M. Sorrells, and C. Sneller, “Identification and validation of QTL for grain quality traits in a cross of soft wheat cultivars pioneer brand 25r26 and foster,” Crop Science, vol. 51, no. 4, pp. 1424–1436, 2011. View at: Publisher Site | Google Scholar
  9. A. Cabrera, M. Guttieri, N. Smith et al., “Identification of milling and baking quality QTL in multiple soft wheat mapping populations,” Theoretical and Applied Genetics, vol. 128, no. 11, pp. 2227–2242, 2015. View at: Publisher Site | Google Scholar
  10. S. A. Flint-Garcia, J. M. Thornsberry, and E. S. Buckler, “Structure of linkage disequilibrium in plants,” Annual Review of Plant Biology, vol. 54, pp. 357–374, 2003. View at: Publisher Site | Google Scholar
  11. J. R. Andersen, T. Schrag, A. E. Melchinger, I. Zein, and T. Lübberstedt, “Validation of Dwarf8 polymorphisms associated with flowering time in elite European inbred lines of maize (Zea mays L.),” Theoretical and Applied Genetics, vol. 111, no. 2, pp. 206–217, 2005. View at: Publisher Site | Google Scholar
  12. F. Breseghello and M. E. Sorrells, “Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars,” Genetics, vol. 172, no. 2, pp. 1165–1177, 2006. View at: Publisher Site | Google Scholar
  13. H. A. Agrama, G. C. Eizenga, and W. Yan, “Association mapping of yield and its components in rice cultivars,” Molecular Breeding, vol. 19, no. 4, pp. 341–356, 2007. View at: Publisher Site | Google Scholar
  14. J. Bordes, C. Ravel, J. Le Gouis, A. Lapierre, G. Charmet, and F. Balfourier, “Use of a global wheat core collection for association analysis of flour and dough quality traits,” Journal of Cereal Science, vol. 54, no. 1, pp. 137–147, 2011. View at: Publisher Site | Google Scholar
  15. J. C. Reif, M. Gowda, H. P. Maurer et al., “Association mapping for quality traits in soft winter wheat,” Theoretical and Applied Genetics, vol. 122, no. 5, pp. 961–970, 2011. View at: Publisher Site | Google Scholar
  16. Y. Zhang, X. Zhang, J. Guo, D. Gao, and B. Zhang, “Association analysis of solvent retention capacity in soft wheat,” Acta Agronomica Sinica, vol. 41, no. 2, pp. 251–258, 2015. View at: Publisher Site | Google Scholar
  17. X. Y. Zhang, Y. P. Tong, G. X. You et al., “Hitchhiking effect mapping: a new approach for discovering agronomic important genes,” Scientia Agricultura Sinica, vol. 39, pp. 1526–1535, 2006. View at: Google Scholar
  18. J. B. Yao, H. X. Ma, P. P. Zhang et al., “Research of wheat elite parent Ningmai 9 and its utilization,” Acta Agriculturae Nucleatae Sinica, vol. 26, pp. 17–21, 2012. View at: Google Scholar
  19. C. Gaines, “Report of the AACC committee on soft wheat flour. Method 56-11, solvent retention capacity profile,” Cereal Foods World, vol. 45, pp. 303–306, 2000. View at: Google Scholar
  20. L. C. Haynes, A. D. Bettge, and L. Slade, “Soft wheat and flour products methods review: solvent retention capacity equation correction,” Cereal Foods World, vol. 54, no. 4, pp. 174–175, 2009. View at: Publisher Site | Google Scholar
  21. M. A. Saghai-Maroof, K. M. Soliman, R. A. Jorgensen, and R. W. Allard, “Ribosomal DNA spacer-length polymorphisms in barley: mendelian inheritance, chromosomal location, and population dynamics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 81, no. 24, pp. 8014–8018, 1984. View at: Publisher Site | Google Scholar
  22. D. J. Somers, P. Isaac, and K. Edwards, “A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.),” Theoretical and Applied Genetics, vol. 109, no. 6, pp. 1105–1114, 2004. View at: Publisher Site | Google Scholar
  23. K. Tamura, G. Stecher, D. Peterson, A. Filipski, and S. Kumar, “MEGA6: molecular evolutionary genetics analysis version 6.0,” Molecular Biology and Evolution, vol. 30, no. 12, pp. 2725–2729, 2013. View at: Publisher Site | Google Scholar
  24. M. J. Hubisz, D. Falush, M. Stephens, and J. K. Pritchard, “Inferring weak population structure with the assistance of sample group information,” Molecular Ecology Resources, vol. 9, no. 5, pp. 1322–1332, 2009. View at: Publisher Site | Google Scholar
  25. P. J. Bradbury, Z. Zhang, D. E. Kroon, T. M. Casstevens, Y. Ramdoss, and E. S. Buckler, “TASSEL: software for association mapping of complex traits in diverse samples,” Bioinformatics, vol. 23, no. 19, pp. 2633–2635, 2007. View at: Publisher Site | Google Scholar
  26. G. Evanno, S. Regnaut, and J. Goudet, “Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study,” Molecular Ecology, vol. 14, no. 8, pp. 2611–2620, 2005. View at: Publisher Site | Google Scholar
  27. P. Jiang, P.-P. Zhang, X. Zhang, X. Chen, and H.-X. Ma, “Association analysis for mixograph properties in Ningmai 9 and its deriva-tives,” Acta Agronomica Sinica, vol. 42, no. 8, pp. 1168–1175, 2016. View at: Publisher Site | Google Scholar
  28. J. Han, L. Zhang, J. Li et al., “Molecular dissection of core parental cross “Triumph/Yanda1817” and its derivatives in wheat breeding program,” Acta Agronomica Sinica, vol. 35, no. 8, pp. 1395–1404, 2009. View at: Publisher Site | Google Scholar
  29. X. J. Li, X. Xu, W. H. Liu, X. Q. Li, and L. H. Li, “Genetic diversity of the founder parent Orofen and its progenies revealed by SSR markers,” Scientia Agricultura Sinica, vol. 42, pp. 3397–3404, 2009. View at: Google Scholar
  30. Y.-Y. Yuan, Q.-Z. Wang, F. Cui, J.-T. Zhang, B. Du, and H.-G. Wang, “Specific loci in genome of wheat milestone parent bima 4 and their transmission in derivatives,” Acta Agronomica Sinica, vol. 36, no. 1, pp. 9–16, 2010. View at: Publisher Site | Google Scholar
  31. Y. G. Xiao, G. H. Yin, H. H. Li et al., “Genetic diversity and genome-wide association analysis of stripe rust resistance among the core wheat parent zhou 8425b and its derivatives,” Scientia Agricultura Sinica, vol. 44, pp. 3919–3929, 2011. View at: Google Scholar
  32. A. Colombo, G. T. Pérez, P. D. Ribotta, and A. E. León, “A comparative study of physicochemical tests for quality prediction of Argentine wheat flours used as corrector flours and for cookie production,” Journal of Cereal Science, vol. 48, no. 3, pp. 775–780, 2008. View at: Publisher Site | Google Scholar
  33. C. Guzmán, G. Posadas-Romano, N. Hernández-Espinosa, A. Morales-Dorantes, and R. J. Peña, “A new standard water absorption criteria based on solvent retention capacity (SRC) to determine dough mixing properties, viscoelasticity, and bread-making quality,” Journal of Cereal Science, vol. 66, pp. 59–65, 2015. View at: Publisher Site | Google Scholar
  34. A. H. Carter, K. Garland-Campbell, and K. K. Kidwell, “Genetic mapping of quantitative trait loci associated with important agronomic traits in the spring wheat (Triticum aestivum L.) Cross “Louise” × “Penawawa”,” Crop Science, vol. 51, no. 1, pp. 84–95, 2011. View at: Publisher Site | Google Scholar

Copyright © 2017 Peng Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles