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

Authors: Peng Jiang; Ping-Ping Zhang; Xu Zhang; Hong-Xiang Ma
Journal: International Journal of Genomics
 (2017)

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## Abstract

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. MarkersXgwm44,Xbarc126,Xwmc790, andXgwm232 associated with SRCs in Ningmai 9 might be used for quality improvement in soft wheat breeding.


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## Body

## 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 [5–7]; 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 [11–13]. 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 (Table1). 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.Table 1
List of Ningmai 9 and its derivatives.
Generation Number Variety/lines Parent L1 Ningmai 9  1st generation L2 Ningmai 13 L3 Ningmai 14 L4 Ningmai 16 L5 Shengxuan 6 L6 Yangmai 18 L7 Yangfumai 4 L8 3E/158 L9 Nannong 0686 L10 Ningmai 18 L11 Ning 0556 L12 Ning 07123 L13 Ning 07119 L14 Ning 0853 L15 Ning 0866 L16 Ning 0894 L17 Ning 08105 L18 Ning 0561 L19 Ning 0564 L20 Ning 0565 L21 Ning 0417 L22 Ning 0418 L23 Ning 0422 L24 Ning 0311 L25 Ning 0316 L26 Ning 0319 L27 Ning 0320 L28 Ning 0327 L29 Ning 0331 L35 Ning 9-11 L36 Ning 9-36 L37 Ning 9 Large 41 L38 Ning 9 Large 44 L39 Ning 9 Large 76 L40 Ning 9 Large 78 L41 Ning 9 Large 80 L60 71666 L61 6E/123 L62 09-654 L64 09-444  L30 Ning 0798 L31 Ning 07117 L32 F307 L33 F308 L34 Ning 0797 L42 Ning 0862 L43 Ning 0869 L44 Ning 0872 L45 Ning 0880 L46 Ning 0882 L47 Ning 0884 L48 Ning 0887 L49 Ning 0893 L50 Ning 0895 L51 Ning 0897 L52 Ning 0898 L53 Ning 0899 L54 Ning 08102 L55 Ning 08104 L56 Ning 08108 L57 Ning 08110 L58 Ning 08115 L59 Ning 08116 L63 09-569 L65 Zhenmai 166 L66 Ning 0867 L67 Ning 0881 L68 Ning 0883 L69 Ning 0886 L70 Ning 0896 L71 Ning 08109 L72 Ning 08111 2nd generation L73 Ning 08112 L74 Ning 08113 L75 08F331 L76 08F333 L77 08F337 L78 08F353 L79 08F362 L80 08F386 L81 08F387 L82 08F396 L83 08F397 L84 08F399 L85 08F406 L86 08F407 L87 08F408 L88 08F409 L89 08F410 L90 08F411 L91 08F417 L92 08F418 L93 08F423 L94 08F424 L95 08F426 L96 08F432 L97 08F433 L98 08F434 L99 08F435 L100 08F436 L101 08F437 L102 08F442 L103 08F443 L104 08F444 L105 08F445 L106 08F446 L107 08F448 L108 08F449 L109 08F450 L110 08F451 L111 08F453 L112 08F454 L113 08F457 L114 08F458 L115 08F468 L116 08F459 L117 08F516 L118 08F517


### 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 10μL 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 Q matrix and K matrix were determined using STRUCTURE v2.3.4 [24]. Five independent simulations were processed for each k, 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 P≤0.01.


## 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 (Table1). 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.Table 1
List of Ningmai 9 and its derivatives.
Generation Number Variety/lines Parent L1 Ningmai 9  1st generation L2 Ningmai 13 L3 Ningmai 14 L4 Ningmai 16 L5 Shengxuan 6 L6 Yangmai 18 L7 Yangfumai 4 L8 3E/158 L9 Nannong 0686 L10 Ningmai 18 L11 Ning 0556 L12 Ning 07123 L13 Ning 07119 L14 Ning 0853 L15 Ning 0866 L16 Ning 0894 L17 Ning 08105 L18 Ning 0561 L19 Ning 0564 L20 Ning 0565 L21 Ning 0417 L22 Ning 0418 L23 Ning 0422 L24 Ning 0311 L25 Ning 0316 L26 Ning 0319 L27 Ning 0320 L28 Ning 0327 L29 Ning 0331 L35 Ning 9-11 L36 Ning 9-36 L37 Ning 9 Large 41 L38 Ning 9 Large 44 L39 Ning 9 Large 76 L40 Ning 9 Large 78 L41 Ning 9 Large 80 L60 71666 L61 6E/123 L62 09-654 L64 09-444  L30 Ning 0798 L31 Ning 07117 L32 F307 L33 F308 L34 Ning 0797 L42 Ning 0862 L43 Ning 0869 L44 Ning 0872 L45 Ning 0880 L46 Ning 0882 L47 Ning 0884 L48 Ning 0887 L49 Ning 0893 L50 Ning 0895 L51 Ning 0897 L52 Ning 0898 L53 Ning 0899 L54 Ning 08102 L55 Ning 08104 L56 Ning 08108 L57 Ning 08110 L58 Ning 08115 L59 Ning 08116 L63 09-569 L65 Zhenmai 166 L66 Ning 0867 L67 Ning 0881 L68 Ning 0883 L69 Ning 0886 L70 Ning 0896 L71 Ning 08109 L72 Ning 08111 2nd generation L73 Ning 08112 L74 Ning 08113 L75 08F331 L76 08F333 L77 08F337 L78 08F353 L79 08F362 L80 08F386 L81 08F387 L82 08F396 L83 08F397 L84 08F399 L85 08F406 L86 08F407 L87 08F408 L88 08F409 L89 08F410 L90 08F411 L91 08F417 L92 08F418 L93 08F423 L94 08F424 L95 08F426 L96 08F432 L97 08F433 L98 08F434 L99 08F435 L100 08F436 L101 08F437 L102 08F442 L103 08F443 L104 08F444 L105 08F445 L106 08F446 L107 08F448 L108 08F449 L109 08F450 L110 08F451 L111 08F453 L112 08F454 L113 08F457 L114 08F458 L115 08F468 L116 08F459 L117 08F516 L118 08F517


## 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 10μL 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 Q matrix and K matrix were determined using STRUCTURE v2.3.4 [24]. Five independent simulations were processed for each k, 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 P≤0.01.


## 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 (Table2), 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.Table 2
The allele frequency between Ningmai 9 and its derivatives on chromosomes.
Chromosome Allele frequency (%) 1st generation 2nd generation 1A 71.28 57.33 2A 80.98 66.65 3A 71.23 64.16 4A 88.29 83.50 5A 77.09 75.34 6A 77.61 77.08 7A 74.47 60.23 Mean 77.28 69.18  1B 68.61 61.01 2B 73.08 70.61 3B 79.40 72.37 4B 76.71 64.02 5B 79.39 67.69 6B 69.97 62.60 7B 80.14 73.98 Mean 75.33 67.47  1D 75.30 66.54 2D 78.97 72.75 3D 64.86 56.33 4D 87.95 73.46 5D 72.49 64.24 6D 55.71 59.79 7D 84.09 74.27 Mean 74.19 66.77  Genome wide allele frequency with Ningmai 9 (%) 1st generation 75.60 2nd generation 67.81


### 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 (Figure1). 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 Co60 radiation.Figure 1
Neighbor-joining cluster of Ningmai 9 and its derivatives. Note: the genetic distance of L7 is so large that it is marked by dashed line.


### 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) (Table3).Table 3
Phenotype variation for 4 SRCs of Ningmai 9 and its derivatives.
Index Year Ningmai 9 Mean Stdev Min Max CV (%) WSRC 2014 59.49 64.03 5.36 49.43 79.40 8.37 2015 59.60 63.72 5.50 49.98 77.25 8.63 SCSRC 2014 78.96 86.73 6.86 71.11 98.91 7.91 2015 79.85 85.66 6.97 70.38 100.15 8.13 LaSRC 2014 108.52 116.88 9.24 93.98 141.71 7.91 2015 109.05 117.23 9.57 96.21 146.57 8.16 SuSRC 2014 108.85 116.05 5.97 99.15 131.09 5.15 2015 109.51 117.06 6.45 104.39 132.75 5.51ANOVA revealed significant effects of genotype for all SRCs (Table4). 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.Table 4
ANOVA and multiple comparisons among generations for the SRCs of Ningmai 9 and its derivatives.
Index F value Multiple comparison test (S-N-K method) Genotype Year Genotype × year Ningmai 9 1st generation 2nd generation WSRC 5.24∗∗ 0.52 0.28 59.55a 61.15a 65.29a SCSRC 8.04∗∗ 6.02∗ 0.77 79.41a 81.14ab 88.81b LaSRC 4.75∗∗ 0.21 0.54 108.79a 117.72a 116.83a SuSRC 3.43∗∗ 3.00 0.51 109.18a 115.66a 117.10a ∗  ∗ 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 (Table5). 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.Table 5
Correlation analysis for the SRCs over two years in Ningmai 9 and its derivatives.
WSRC SCSRC LaSRC SuSRC WSRC 0.897∗∗ 0.663∗∗ −0.012 0.343 ∗ ∗ SCSRC 0.640∗∗ 0.825∗∗ −0.007 0.525 ∗ ∗ LaSRC 0.037 0.085 0.809∗∗ 0.403 ∗ ∗ SuSRC 0.311∗∗ 0.582∗∗ 0.398∗∗ 0.740 ∗ ∗ ∗  ∗ 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 (Table6). 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, andXwmc754 andXwmc326 were associated with both LaSRC and SuSRC.Xbarc126,Xwmc517,Xgwm484,Xwmc754,Xwmc326, andXgwm232 were detected in both years, andwmc754 andwmc326 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.Table 6
Association analysis for SRCs.
Traits Marker Chromosome 2014 2015 P R 2 (%) Effect (allele) P R 2 (%) Effect (allele) WSRC Xwmc468 4AL 4.74 × 10−3 6.61 − (134) Xwmc89 4DS 8.10 × 10−3 5.94 − (140) Xwmc517 7BL    9.68 × 10−3 5.71 + (183) Xgwm44 7DS 8.11 × 10−3 5.78 − (196) 1.37 × 10−3 8.84 − (196) Xbarc126 7DS 6.48 × 10−3 6.13 − (170) 4.34 × 10−4 10.80 − (170)  SCSRC Xgwm153 1BL    5.65 × 10−3 6.02 − (188) Xcfd72 1DL    4.55 × 10−3 6.22 + (310) Xgwm232 1DL 7.64 × 10−4 8.57 + (144) Xwmc658 2AL 6.82 × 10−3 5.44 + (250) Xgwm257 2BS    9.54 × 10−3 5.16 − (186) Xgwm539 2DL 6.32 × 10−3 5.56 + (160) Xgwm102 2DS 3.04 × 10−3 6.57 + (142) Xgwm484 2DS 1.94 × 10−4 10.81 − (179) 2.90 × 10−3 6.96 − (179) Xwmc231 3B 1.09 × 10−3 8.17 + (240) Xwmc777 3B 4.13 × 10−4 9.48 − (100) Xwmc653 3B    6.05 × 10−3 5.83 − (160) Xwmc219 4AL 6.68 × 10−3 5.47 + (160) Xgwm169 6AL    9.54 × 10−3 5.68 − (190) Xwmc397 6BL 9.68 × 10−5 12.05 — Xwmc790 7A 1.68 × 10−3 7.42 − (108) Xwmc809 7A    6.04 × 10−3 5.81 − (180) Xwmc311 7BL 9.64 × 10−3 5.12 + (120) Xwmc634 7DL 4.16 × 10−4 10.17 + (210) Xgwm437 7DL    6.04 × 10−3 5.81 − (110) Xgwm44 7DS    6.61 × 10−3 5.88 − (183) Xcfd14 7DS    1.80 × 10−3 7.62 − (100)  LaSRC Xwmc754 3B 2.09 × 10−3 8.77 − (160) 7.89 × 10−3 6.39 + (152) Xwmc326 3B 7.20 × 10−3 7.15 + (186) 8.29 × 10−3 7.00 + (186)  SuSRC Xgwm232 1DL 5.17 × 10−4 10.44 − (144) 4.27 × 10−3 7.20 − (144) Xgwm349 2DL 5.97 × 10−3 6.47 + (310) Xwmc754 3B 5.96 × 10−3 6.68 − (160) Xwmc326 3B    4.38 × 10−3 7.35 + (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.


## 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 (Table2), 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.Table 2
The allele frequency between Ningmai 9 and its derivatives on chromosomes.
Chromosome Allele frequency (%) 1st generation 2nd generation 1A 71.28 57.33 2A 80.98 66.65 3A 71.23 64.16 4A 88.29 83.50 5A 77.09 75.34 6A 77.61 77.08 7A 74.47 60.23 Mean 77.28 69.18  1B 68.61 61.01 2B 73.08 70.61 3B 79.40 72.37 4B 76.71 64.02 5B 79.39 67.69 6B 69.97 62.60 7B 80.14 73.98 Mean 75.33 67.47  1D 75.30 66.54 2D 78.97 72.75 3D 64.86 56.33 4D 87.95 73.46 5D 72.49 64.24 6D 55.71 59.79 7D 84.09 74.27 Mean 74.19 66.77  Genome wide allele frequency with Ningmai 9 (%) 1st generation 75.60 2nd generation 67.81


## 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 (Figure1). 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 Co60 radiation.Figure 1
Neighbor-joining cluster of Ningmai 9 and its derivatives. Note: the genetic distance of L7 is so large that it is marked by dashed line.


## 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) (Table3).Table 3
Phenotype variation for 4 SRCs of Ningmai 9 and its derivatives.
Index Year Ningmai 9 Mean Stdev Min Max CV (%) WSRC 2014 59.49 64.03 5.36 49.43 79.40 8.37 2015 59.60 63.72 5.50 49.98 77.25 8.63 SCSRC 2014 78.96 86.73 6.86 71.11 98.91 7.91 2015 79.85 85.66 6.97 70.38 100.15 8.13 LaSRC 2014 108.52 116.88 9.24 93.98 141.71 7.91 2015 109.05 117.23 9.57 96.21 146.57 8.16 SuSRC 2014 108.85 116.05 5.97 99.15 131.09 5.15 2015 109.51 117.06 6.45 104.39 132.75 5.51ANOVA revealed significant effects of genotype for all SRCs (Table4). 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.Table 4
ANOVA and multiple comparisons among generations for the SRCs of Ningmai 9 and its derivatives.
Index F value Multiple comparison test (S-N-K method) Genotype Year Genotype × year Ningmai 9 1st generation 2nd generation WSRC 5.24∗∗ 0.52 0.28 59.55a 61.15a 65.29a SCSRC 8.04∗∗ 6.02∗ 0.77 79.41a 81.14ab 88.81b LaSRC 4.75∗∗ 0.21 0.54 108.79a 117.72a 116.83a SuSRC 3.43∗∗ 3.00 0.51 109.18a 115.66a 117.10a ∗  ∗ 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 (Table5). 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.Table 5
Correlation analysis for the SRCs over two years in Ningmai 9 and its derivatives.
WSRC SCSRC LaSRC SuSRC WSRC 0.897∗∗ 0.663∗∗ −0.012 0.343 ∗ ∗ SCSRC 0.640∗∗ 0.825∗∗ −0.007 0.525 ∗ ∗ LaSRC 0.037 0.085 0.809∗∗ 0.403 ∗ ∗ SuSRC 0.311∗∗ 0.582∗∗ 0.398∗∗ 0.740 ∗ ∗ ∗  ∗ 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 (Table6). 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, andXwmc754 andXwmc326 were associated with both LaSRC and SuSRC.Xbarc126,Xwmc517,Xgwm484,Xwmc754,Xwmc326, andXgwm232 were detected in both years, andwmc754 andwmc326 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.Table 6
Association analysis for SRCs.
Traits Marker Chromosome 2014 2015 P R 2 (%) Effect (allele) P R 2 (%) Effect (allele) WSRC Xwmc468 4AL 4.74 × 10−3 6.61 − (134) Xwmc89 4DS 8.10 × 10−3 5.94 − (140) Xwmc517 7BL    9.68 × 10−3 5.71 + (183) Xgwm44 7DS 8.11 × 10−3 5.78 − (196) 1.37 × 10−3 8.84 − (196) Xbarc126 7DS 6.48 × 10−3 6.13 − (170) 4.34 × 10−4 10.80 − (170)  SCSRC Xgwm153 1BL    5.65 × 10−3 6.02 − (188) Xcfd72 1DL    4.55 × 10−3 6.22 + (310) Xgwm232 1DL 7.64 × 10−4 8.57 + (144) Xwmc658 2AL 6.82 × 10−3 5.44 + (250) Xgwm257 2BS    9.54 × 10−3 5.16 − (186) Xgwm539 2DL 6.32 × 10−3 5.56 + (160) Xgwm102 2DS 3.04 × 10−3 6.57 + (142) Xgwm484 2DS 1.94 × 10−4 10.81 − (179) 2.90 × 10−3 6.96 − (179) Xwmc231 3B 1.09 × 10−3 8.17 + (240) Xwmc777 3B 4.13 × 10−4 9.48 − (100) Xwmc653 3B    6.05 × 10−3 5.83 − (160) Xwmc219 4AL 6.68 × 10−3 5.47 + (160) Xgwm169 6AL    9.54 × 10−3 5.68 − (190) Xwmc397 6BL 9.68 × 10−5 12.05 — Xwmc790 7A 1.68 × 10−3 7.42 − (108) Xwmc809 7A    6.04 × 10−3 5.81 − (180) Xwmc311 7BL 9.64 × 10−3 5.12 + (120) Xwmc634 7DL 4.16 × 10−4 10.17 + (210) Xgwm437 7DL    6.04 × 10−3 5.81 − (110) Xgwm44 7DS    6.61 × 10−3 5.88 − (183) Xcfd14 7DS    1.80 × 10−3 7.62 − (100)  LaSRC Xwmc754 3B 2.09 × 10−3 8.77 − (160) 7.89 × 10−3 6.39 + (152) Xwmc326 3B 7.20 × 10−3 7.15 + (186) 8.29 × 10−3 7.00 + (186)  SuSRC Xgwm232 1DL 5.17 × 10−4 10.44 − (144) 4.27 × 10−3 7.20 − (144) Xgwm349 2DL 5.97 × 10−3 6.47 + (310) Xwmc754 3B 5.96 × 10−3 6.68 − (160) Xwmc326 3B    4.38 × 10−3 7.35 + (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 includingFusarium 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), andFusarium 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 toXwmc468 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 betweenXwmc777 andXwmc653, andXgwm44 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 toXgwm257 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 ofXgwm44,Xbarc126,Xwmc790, andXgwm232 associated with SRCs in Ningmai 9 may be used for quality improvement in soft wheat breeding.



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*Source: 2413150-2017-02-05.xml*