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
Soft wheat flour of low protein content is usually associated with the cookie quality [
In comparing with hard wheat, solvent retention capacity (SRC) is used more often for evaluating the quality of soft wheat in cookie making [
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 [
Association mapping is a method to test the association between molecular markers and QTL based on linkage disequilibrium [
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 [
Ningmai 9 and its 117 derivatives including 39 lines of first generation and 78 lines of second generation were used in this study (Table
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 |
DNA was extracted from fresh leaves using a CTAB procedure according to Saghai-Maroof et al. (1984) [
Each 10
Excel 2007 was used for data preparation; ANOVA was performed using SPSS 17.0. Neighbor-joining cluster was performed with Mega 6.0 [
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
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 |
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) [
Neighbor-joining cluster based on the genotyping data also showed that there were 2 groups in the materials (Figure
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.
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
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 | 5.51 |
ANOVA revealed significant effects of genotype for all SRCs (Table
ANOVA and multiple comparisons among generations for the SRCs of Ningmai 9 and its derivatives.
Index | | Multiple comparison test (S-N-K method) | ||||
---|---|---|---|---|---|---|
Genotype | Year | Genotype × year | Ningmai 9 | 1st generation | 2nd generation | |
WSRC | 5.24 | 0.52 | 0.28 | 59.55 | 61.15 | 65.29 |
SCSRC | 8.04 | 6.02 | 0.77 | 79.41 | 81.14 | 88.81 |
LaSRC | 4.75 | 0.21 | 0.54 | 108.79 | 117.72 | 116.83 |
SuSRC | 3.43 | 3.00 | 0.51 | 109.18 | 115.66 | 117.10 |
There was significant positive correlation between the two years for all SRCs (Table
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 | |
SCSRC | 0.640 | 0.825 | −0.007 | |
LaSRC | 0.037 | 0.085 | 0.809 | |
SuSRC | 0.311 | 0.582 | 0.398 | |
A total of 29 markers on 13 wheat chromosomes were associated with the SRCs (Table
Association analysis for SRCs.
Traits | Marker | Chromosome | 2014 | 2015 | ||||
---|---|---|---|---|---|---|---|---|
| | Effect (allele) | | | Effect (allele) | |||
WSRC | | 4AL | 4.74 × 10−3 | 6.61 | − (134) | |||
| 4DS | 8.10 × 10−3 | 5.94 | − (140) | ||||
| 7BL | 9.68 × 10−3 | 5.71 | + (183) | ||||
| 7DS | 8.11 × 10−3 | 5.78 | − (196) | 1.37 × 10−3 | 8.84 | − (196) | |
| 7DS | 6.48 × 10−3 | 6.13 | − (170) | 4.34 × 10−4 | 10.80 | − (170) | |
| ||||||||
SCSRC | | 1BL | 5.65 × 10−3 | 6.02 | − (188) | |||
| 1DL | 4.55 × 10−3 | 6.22 | + (310) | ||||
| 1DL | 7.64 × 10−4 | 8.57 | + (144) | ||||
| 2AL | 6.82 × 10−3 | 5.44 | + (250) | ||||
| 2BS | 9.54 × 10−3 | 5.16 | − (186) | ||||
| 2DL | 6.32 × 10−3 | 5.56 | + (160) | ||||
| 2DS | 3.04 × 10−3 | 6.57 | + (142) | ||||
| 2DS | 1.94 × 10−4 | 10.81 | − (179) | 2.90 × 10−3 | 6.96 | − (179) | |
| 3B | 1.09 × 10−3 | 8.17 | + (240) | ||||
| 3B | 4.13 × 10−4 | 9.48 | − (100) | ||||
| 3B | 6.05 × 10−3 | 5.83 | − (160) | ||||
| 4AL | 6.68 × 10−3 | 5.47 | + (160) | ||||
| 6AL | 9.54 × 10−3 | 5.68 | − (190) | ||||
| 6BL | 9.68 × 10−5 | 12.05 | — | ||||
| 7A | 1.68 × 10−3 | 7.42 | − (108) | ||||
| 7A | 6.04 × 10−3 | 5.81 | − (180) | ||||
| 7BL | 9.64 × 10−3 | 5.12 | + (120) | ||||
| 7DL | 4.16 × 10−4 | 10.17 | + (210) | ||||
| 7DL | 6.04 × 10−3 | 5.81 | − (110) | ||||
| 7DS | 6.61 × 10−3 | 5.88 | − (183) | ||||
| 7DS | 1.80 × 10−3 | 7.62 | − (100) | ||||
| ||||||||
LaSRC | | 3B | 2.09 × 10−3 | 8.77 | − (160) | 7.89 × 10−3 | 6.39 | + (152) |
| 3B | 7.20 × 10−3 | 7.15 | + (186) | 8.29 × 10−3 | 7.00 | + (186) | |
| ||||||||
SuSRC | | 1DL | 5.17 × 10−4 | 10.44 | − (144) | 4.27 × 10−3 | 7.20 | − (144) |
| 2DL | 5.97 × 10−3 | 6.47 | + (310) | ||||
| 3B | 5.96 × 10−3 | 6.68 | − (160) | ||||
| 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.
Ningmai 9 is a soft wheat variety with stable soft wheat quality, high yield, wide adaptation, and resistance to multiple diseases including
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 [
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. [
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[