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Acta Agronomica Sinica ›› 2018, Vol. 44 ›› Issue (05): 629-641.doi: 10.3724/SP.J.1006.2018.00629

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS •     Next Articles

Identification of Candidate Genes for Seed Glucosinolate Content of Rapeseed by Using Genome-wide Association Mapping and Co-expression Networks Analysis

Da-Yong WEI1,2,3(), Yi-Xin CUI3, Qing XIONG4, Qing-Lin TANG1,2, Jia-Qin MEI3, Jia-Na LI3, Wei QIAN3,*()   

  1. 1 College of Horticulture and Landscape Architecture, Southwest University, Chongqing 400715, China
    2 Key Laboratory of Horticulture Science for Southern Mountainous Regions, Ministry of Education, Chongqing 400715, China
    3 College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
    4 School of Computer and Information Science, Chongqing 400715, China
  • Received:2017-12-10 Accepted:2018-03-15 Online:2018-05-20 Published:2018-03-16
  • Contact: Wei QIAN E-mail:swuwdy@swu.edu.cn;qianwei666@hotmail.com
  • Supported by:
    This study was supported by National Natural Science Foundation of China (31601333), the National Basic Research Program of China (973 Program) (2015CB150201), and the Fundamental Research Funds for the Central Universities (XDJK2017B036).

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Abstract:

Seed meal of rapeseed (Brassica napus L.) is a valuable protein source for livestock raising. However, high seed glucosinolates (GSL) content is harmful and toxic to livestock. Therefore, identifying candidate genes of seed GSL content is important in rapeseed breeding for low seed GSL. In this study, a genome-wide association study (GWAS) for seed GSL content was conducted using 157 rapeseed lines grown in four consecutive years. Meanwhile, a weighted gene co-expression network analysis (WGCNA) was carried out in early seed development stage of 15 low and 15 high seed GSL content lines for detecting candidate genes. In total, 45 SNPs found by GWAS significantly associated with seed GSL contents, explaining 13.5%-23.3% of the phenotypic variance per SNP. These SNPs were mainly detected from three intervals on chromosomes A09, C02, and C09, covering five known GSL metabolism genes. A total of 2275 differentially expressed genes (DEGs) were identified by RNA-Seq between rapeseed lines with low and high seed GSL contents. These DEGs were clustered into 12 modules by WGCNA, of which one module (contains 163 DEGs) was mainly enriched in the GSL biosynthetic process. By using a weighted analysis for this module, 13 hub-genes were detected including nine known GSL metabolic genes. Among the 18 candidate genes identified by GWAS and WGCNA, 14 genes showed significant correlation between their expressions and the seed GSL contents (r = 0.376-0.638, P < 0.05). Furthermore, one gene, BnaC02g41790D (MAM1), was detected by both GWAS and WGCNA. Five haplotypes were formed by five SNPs that significantly linked with BnaC02g41790D, and 63% of the rapeseed population (99/157) were found to carry Hap 5 with significant lower seed GSL contents (an average of 50.79 μmol g-1) compared with those carrying the other four haplotypes (95.04-110.28 μmol g-1). By GWAS and WGCNA, our study not only identified the candidate genes for seed GSL content of rapeseed, but also provided a guidance for digging candidate genes for other complex traits.

Key words: Brassica napus, GWAS, WGCNA, Re-sequencing, RNA-seq, Seed GSL content

Supplementary fig. 1

Gene cluster dendrogram and module splitting a: gene cluster dendrogram tree, y-coordinate indicates the cluster distance between genes; b: modules assignment cut using dynamic tree; c: merging of modules whose expression profiles are very similar."

Table 1

Phenotypic variation of seed GSL in association population"

年份
Year		 范围
Range		 平均值±标准偏差 Mean±SD 峰度值
Kurtosis		 偏度值
Skewness		 变异系数
CV (%)
2013 27.38-159.40 68.72±36.07 -1.06 0.67 50.29
2014 25.25-149.60 70.24±35.49 -0.99 0.59 54.98
2015 24.17-153.70 69.78±38.37 -0.95 0.65 50.54
2016 24.57-169.20 71.78±36.09 -0.84 0.57 52.49

Supplementary table 1

Source of the accessions in this study"

编号
Code		 名称
Name		 种子硫苷含量 Seed GSL content (μmol g-1)
2016 2015 2014 2013
1 自交种 Inbreed line 28.06 32.25 25.25 28.06
2 自交种 Inbreed line 24.57 27.11 28.88 28.06
3 自交种 Inbreed line 28.41 29.21 26.79 30.40
4 自交种 Inbreed line 30.45 27.61 27.20 30.33
5 苏油3号 Suyou 3 28.18 33.83 29.82 30.46
6 Rivette 30.00 27.05 32.50 29.12
7 自交种 Inbreed line 38.14 30.50 33.01 27.38
8 自交种 Inbreed line 34.63 33.41 30.85 31.50
9 自交种 Inbreed line 29.28 28.72 28.79 33.88
10 华双5号 Huashuang 5 30.95 31.07 33.47 29.52
11 自交种 Inbreed line 35.33 37.41 30.12 33.00
12 自交种 Inbreed line 28.74 25.62 28.67 34.74
13 自交种 Inbreed line 29.84 31.20 31.06 32.71
14 自交种 Inbreed line 32.15 30.71 32.50 32.08
15 自交种 Inbreed line 30.22 30.78 31.62 33.59
16 Larissa 35.12 25.38 34.82 30.92
17 自交种Inbreed line 33.40 38.23 28.41 37.88
18 自交种 Inbreed line 32.24 28.43 36.95 29.40
19 Altex 33.04 28.34 32.66 35.42
20 自交种Inbreed line 36.75 34.01 36.23 32.31
21 Bronowski 32.02 33.49 32.11 36.81
22 西南大学7号 SWU 7 34.10 36.77 36.17 32.75
23 自交种 Inbreed line 42.75 38.88 37.30 31.90
24 自交种 Inbreed line 33.71 33.61 35.58 33.78
25 Korall 37.66 30.19 38.69 31.61
26 自交种 Inbreed line 37.08 32.68 35.05 36.10
27 两优586 (F2)-6-3 Liangyou 586 (F2)-6-3 28.39 33.34 29.66 41.63
28 自交种 Inbreed line 35.90 33.57 37.52 34.14
29 Pauline 34.96 25.22 33.89 38.58
30 ACS N45 31.48 24.17 35.29 37.59
31 Ability 38.19 30.50 35.69 37.34
32 自交种 Inbreed line 34.60 28.83 39.01 34.57
33 Campino 31.97 32.19 38.15 35.81
34 自交种 Inbreed line 34.64 39.29 38.90 35.67
35 自交种 Inbreed line 51.38 31.85 38.73 37.54
36 自交种 Inbreed line 35.53 34.29 37.13 39.33
37 自交种 Inbreed line 37.15 39.56 33.93 42.90
编号
Code		 名称
Name		 种子硫苷含量 Seed GSL content (μmol g-1)
2016 2015 2014 2013
38 自交种 Inbreed line 41.53 38.44 35.13 41.86
39 自交种 Inbreed line 42.87 73.00 37.94 39.10
40 Omega 104.23 91.61 38.38 38.71
41 自交种 Inbreed line 37.47 38.82 42.36 35.69
42 Q2 90.64 56.73 36.82 44.85
43 自交种 Inbreed line 31.31 30.93 44.48 34.73
44 自交种 Inbreed line 31.29 33.77 51.03 29.67
45 SW Sinatra 39.61 26.67 30.63 51.22
46 Andor 36.17 25.78 40.15 42.52
47 Tenor 37.59 38.33 45.89 37.66
48 Aurora 36.04 34.34 47.48 36.77
49 Clipper 39.78 45.41 40.64 43.75
50 自交种 Inbreed line 41.80 45.28 44.52 40.35
51 自交种 Inbreed line 41.59 36.32 45.48 39.77
52 自交种 Inbreed line 41.30 41.84 44.09 42.22
53 Granit 38.66 34.06 39.92 44.87
54 自交种 Inbreed line 33.59 34.04 42.23 44.32
55 自交种 Inbreed line 38.73 48.88 43.76 43.72
56 Montego 42.93 34.94 44.67 43.51
57 Allure 49.91 41.99 46.39 42.28
58 自交种 Inbreed line 40.08 41.41 46.04 42.97
59 自交种 Inbreed line 38.54 45.50 44.62 44.85
60 NK Passion 48.49 46.20 46.13 43.47
61 Campari 46.73 52.85 48.12 42.50
62 Lord 61.09 81.47 45.95 45.95
63 自交种 Inbreed line 43.36 41.15 53.41 39.47
64 DRAKKAR 33.97 30.30 48.14 45.05
65 中双5号 Zhongshuang 5 53.71 47.45 48.14 45.61
66 Lilian 48.66 42.94 50.87 43.28
67 Rapid 50.27 49.00 49.92 44.69
68 Aragon 54.04 43.79 55.15 39.59
69 Lisandra 48.37 31.85 54.87 40.51
70 821选×品93-498 F8 821 Xuan × Pin 93-498 F8 57.52 46.82 43.17 53.81
71 油研2号× 84-24016 Youyan 2 × 84-24016 76.70 79.30 41.67 56.24
72 Pivot 31.05 31.35 58.42 41.61
73 SLM 0512 56.17 30.58 53.72 48.36
74 Musette 61.05 59.73 53.41 49.47
75 Lion 92.53 103.65 55.58 48.03
76 Baros 52.43 38.73 59.54 45.92
77 Korinth 58.77 50.00 47.20 57.10
78 Odin 68.97 60.00 60.20 50.25
79 Beluga 59.42 52.59 54.80 58.19
80 Express 617 83.78 46.97 60.90 52.10
81 SWGospel 65.64 52.66 62.86 52.86
82 Fortis 77.72 45.97 65.66 53.94
83 Remy 68.03 53.29 65.36 54.30
84 BRISTOL 76.19 64.94 71.36 49.38
85 Jantar 69.91 47.81 61.98 60.50
86 Binera 86.68 81.60 62.24 60.40
87 Boston 74.55 52.60 64.13 59.12
88 Licapo 60.23 44.54 66.08 57.42
89 Alesi 73.32 50.75 66.78 59.90
90 Nugget 73.98 52.76 68.27 58.44
91 Jessica 71.92 73.47 69.80 57.61
92 HANNA 76.35 76.02 62.78 67.88
93 Duplo 74.18 76.93 74.05 57.65
94 Wesroona 71.19 79.28 67.37 64.48
95 Laser 76.81 58.87 75.33 57.31
96 Recital 70.94 52.25 72.36 62.24
97 Escort 82.04 65.80 75.95 65.02
98 Darmor 96.64 141.00 78.38 63.00
99 Ww 1286 76.39 97.97 73.08 70.24
100 (D57×Oro)×油研2号-F6 (D57×Oro)×Youyan 2-F6 72.92 56.81 67.07 76.86
101 Falcon 72.21 73.11 76.75 71.03
102 华油6号 Huayou 6 84.04 91.28 73.50 78.47
103 贵农78-6-112 Guinong 78-6-112 78.63 89.86 71.61 85.40
104 Chuosenshu 86.26 108.84 88.64 88.43
105 Lirabon 109.95 104.49 98.28 86.98
106 Pera 91.52 98.47 76.04 111.30
107 Manitoba 87.89 94.70 114.99 73.71
108 Nugget 105.01 94.40 104.71 87.41
109 Regina II 89.18 95.83 93.33 99.70
110 Oro 74.40 88.15 85.84 108.59
111 Galant 94.22 73.92 87.67 106.82
112 V8 108.70 89.26 109.77 85.73
113 Nakate Chousen 126.30 124.36 91.44 104.60
114 CRESOR 92.36 84.73 89.55 106.61
115 Hankkija’s Lauri 99.37 128.28 96.18 100.79
116 TANTAL 103.22 115.34 95.86 104.73
117 云油14 Youyou 14 96.84 102.85 106.21 96.13
118 K26-96 114.75 102.00 103.92 100.34
119 Spaeths Zollerngold 104.30 106.79 100.98 108.09
120 Baltia 112.01 93.61 106.74 102.92
121 Mali 101.14 105.85 102.20 114.77
122 RESYN-H048 112.25 106.00 104.38 113.97
123 Daichousen (fuku) 114.21 106.34 114.41 106.62
124 Orpal 95.24 96.20 110.46 111.72
125 Orriba 110.66 112.91 112.52 111.31
126 EMERALD 110.66 112.91 109.30 114.72
127 湘油16 Xiangyou 16 100.05 94.64 110.22 116.66
128 Daichousen (mizuyasu) 102.05 100.97 113.01 114.26
129 Zephyr 114.07 105.58 117.34 112.79
130 Wesreo 114.03 101.87 118.95 113.23
131 Miyauchi Na 106.39 138.65 119.01 110.30
132 Olivia 115.30 115.82 119.21 113.47
133 Taisetsu 107.13 116.00 111.10 122.19
134 JANETZKIS 133.00 129.88 115.00 119.73
135 JetNeuf 112.14 132.64 120.52 114.68
136 Gulliver 110.21 102.68 113.37 119.94
137 Leonessa 123.22 112.63 114.65 122.08
138 Ceska Krajova 107.40 105.93 123.48 115.89
139 Skrzeszowicki 143.05 131.53 119.89 119.54
140 CANARD 74.88 122.32 114.91 125.84
141 Mansholt 102.00 107.84 122.01 121.82
142 Palu 140.92 128.05 126.79 118.74
143 Parapluie 131.00 142.78 124.71 122.51
144 Kruglik 119.41 114.38 118.23 129.02
145 Czyzowska 108.55 122.55 116.10 131.66
146 Edita 147.21 115.40 131.10 119.89
147 Sonnengold 128.77 144.22 133.00 125.70
148 Conny 139.65 122.66 134.30 128.54
149 湘农油-1 Xiangnongou-1 120.54 101.40 135.57 123.31
150 西农长角×((D57×Oro)×85-64)F7
Xinongchangjiao×((D57×Oro)×85-64)F7		 106.86 128.13 122.10 141.81
151 MOANA 139.74 145.62 138.69 125.34
152 Mlochowski 118.99 120.69 133.42 131.32
153 Samo 169.19 152.80 137.73 132.82
154 Suigenshu 124.93 144.33 147.49 126.56
155 Nunsdale 134.00 151.62 144.68 131.49
156 Gisora 150.75 148.00 131.50 159.43
157 Dippes 149.04 153.73 149.56 143.58

Supplementary table 2

ANOVA of phenotype for seed GSL in association population"

变异来源
Source		 自由度
df		 均方
MS		 概率值
P-value
基因型 Genotype 156 9238.54 <0.001
环境 Environment 3 406.70 0.0002
基因型×环境 Genotype×Environment 459 165.83 <0.001
重复 Repeat 1 0.18 0.9561

Fig. 1

Manhattan plot of GWAS The dashed lines represent the bonferroni-adjusted significance threshold -lg (P) = 7."

Table 2

GO enrichment and KEGG pathway analysis for brown module"

编号
ID		 GO/KEGG项
GO/KEGG term		 P值
P-value		 错误发现率
False discovery rate
GO: 0019761 硫苷生物合成进程 Glucosinolate biosynthetic process 1.90×10-30 1.90×10-28
KEGG: ko00966 硫苷生物合成 Glucosinolate biosynthesis 6.00×10-07 5.30×10-04
KEGG: ko01210 2-氧代羧酸代谢 2-Oxocarboxylic acid metabolism 3.40×10-10 3.00×10-07

Table 3

Hub genes analysis for Brown module"

排名 Rank 枢纽基因
Hub genes		 染色体和位置
Chromosome and position		 权重值
Weight value		 已知的硫苷代谢基因 Known GSL genes 拟南芥同源基因
Arabidopsis homologue genes
1 BnaC09g23550D C09:21069980-21071017 0.28 BAT5 AT4G12030
2 BnaC05g29760D C05:28673017-28675449 0.27 / AT3G22740
3 BnaC08g08320D C08:12425941-12428358 0.25 / AT4G14040
4 BnaC04g12860D C04:10121243-10124859 0.25 UDP-G AT2G31790
5 BnaC05g12520D C05:7308750-7311146 0.24 CYP79A2 AT1G16410
6 BnaC05g33030D C05:32516433-32519269 0.24 BCAT4 AT3G19710
7 BnaC04g50950D C04:48340448-48341488 0.22 / AT2G46650
8 BnaA04g06630D A04:5277893-5279719 0.22 CYP83A1 AT4G13770
9 BnaA08g07580D A08:7544499-7547006 0.22 / AT4G14040
10 BnaA09g21170D A09:13876818-13877853 0.21 BAT5 AT4G12030
11 BnaA03g35400D A03:17272166-17274784 0.21 BCAT4 AT3G19710
12 BnaC02g41790D C02:44598027-44600079 0.20 MAM1 AT5G23010
13 BnaA06g11010D A06:5753484-5755863 0.20 CYP79A2 AT1G16410

Fig. 2

Linkage disequilibrium and haplotype effect analysis a: the LD analysis between the candidate gene BnaC02g41790D and five significant SNPs from GWAS, the arrows shows physical location of the candidate gene and five significant SNPs on chromosome C02. b: the haplotype effect analysis for five significant SNPs in chromosome C02; ** indicates significant difference at P<0.01."

Supplementary fig. 2

Correlation analysis between core gene expression levels and seed GSL contents in 30 extreme accessions * and ** indicate significant correlation at P<0.05 and 0.01, respectively."

[1] Chalhoub B, Denoeud F, Liu S, Parkin I A, Tang H, Wang X, Chiquet J, Belcram H, Tong C, Samans B, Correa M, Da Silva C, Just J, Falentin C, Koh C S, Le Clainche I, Bernard M, Bento P, Noel B, Labadie K, Alberti A, Charles M, Arnaud D, Guo H, Daviaud C, Alamery S, Jabbari K, Zhao M, Edger P P, Chelaifa H, Tack D, Lassalle G, Mestiri I, Schnel N, Le Paslier M C, Fan G, Renault V, Bayer P E, Golicz A A, Manoli S, Lee T H, Thi V H, Chalabi S, Hu Q, Fan C, Tollenaere R, Lu Y, Battail C, Shen J, Sidebottom C H, Wang X, Canaguier A, Chauveau A, Berard A, Deniot G, Guan M, Liu Z, Sun F, Lim Y P, Lyons E, Town C D, Bancroft I, Wang X, Meng J, Ma J, Pires J C, King G J, Brunel D, Delourme R, Renard M, Aury J M, Adams K L, Batley J, Snowdon R J, Tost J, Edwards D, Zhou Y, Hua W, Sharpe A G, Paterson A H, Guan C, Wincker P. Early allopolyploid evolution in the post-NeolithicBrassica napus oilseed genome. Science, 2014, 345: 950-953
[2] Nagaharu U.Genomic analysis in Brassica with special reference to the experimental formation of B. napus and peculiar bode of fertilization. Jpn J Bot, 1935, 7: 389-452
[3] 刘后利. 油菜遗传育种学. 北京: 中国农业大学出版社, 2000. pp 146-154
Liu H L.Genetics and Breeding in Rrapeseed. Beijing: Chinese Agricultural Universitatis Press, 2000. pp 146-154 (in Chinese)
[4] Mithen R.Glucosinolates-biochemistry, genetics and biological activity.Plant Growth Regul, 2001, 34: 91-103
doi: 10.1023/A:1013330819778
[5] Fahey J W, Zalcmann A T, Talalay P.The chemical diversity and distribution of glucosinolates and isothiocyanates among plants.Phytochemistry, 2001, 56: 5-51
doi: 10.1016/S0031-9422(00)00316-2
[6] Halkier B A, Gershenzon J.Biology and biochemistry of glucosinolates.Annu Rev Plant Biol, 2006, 57: 303-333
doi: 10.1146/annurev.arplant.57.032905.105228 pmid: 16669764
[7] Bak S, Feyereisen R.The involvement of two p450 enzymes, CYP83B1 and CYP83A1, in auxin homeostasis and glucosinolate biosynthesis.Plant Physiol, 2001, 127: 108-118
doi: 10.1104/pp.127.1.108 pmid: 11553739
[8] Grubb C D, Abel S.Glucosinolate metabolism and its control.Trends Plant Sci, 2006, 11: 89-100
doi: 10.1016/j.tplants.2005.12.006 pmid: 16406306
[9] Mikkelsen M D, Naur P, Halkier B A.Arabidopsis mutants in the C-S lyase of glucosinolate biosynthesis establish a critical role for indole-3-acetaldoxime in auxin homeostasis. Plant J, 2004, 37: 770-777
doi: 10.1111/j.1365-313X.2004.02002.x pmid: 14871316
[10] Wittstock U, Halkier B A.Cytochrome P450 CYP79A2 from Arabidopsis thaliana L. Catalyzes the conversion of L-phenylalanine to phenylacetaldoxime in the biosynthesis of benzylglucosinolate. J Biol Chem, 2000, 275: 14659-14666
doi: 10.1074/jbc.275.19.14659 pmid: 10799553
[11] Wang X, Wang H, Wang J, Sun R, Wu J, Liu S, Bai Y, Mun J H, Bancroft I, Cheng F, Huang S, Li X, Hua W, Wang J, Wang X, Freeling M, Pires J C, Paterson A H, Chalhoub B, Wang B, Hayward A, Sharpe A G, Park B S, Weisshaar B, Liu B, Li B, Liu B, Tong C, Song C, Duran C, Peng C, Geng C, Koh C, Lin C, Edwards D, Mu D, Shen D, Soumpourou E, Li F, Fraser F, Conant G, Lassalle G, King G J, Bonnema G, Tang H, Wang H, Belcram H, Zhou H, Hirakawa H, Abe H, Guo H, Wang H, Jin H, Parkin I A, Batley J, Kim J S, Just J, Li J, Xu J, Deng J, Kim J A, Li J, Yu J, Meng J, Wang J, Min J, Poulain J, Wang J, Hatakeyama K, Wu K, Wang L, Fang L, Trick M, Links M G, Zhao M, Jin M, Ramchiary N, Drou N, Berkman P J, Cai Q, Huang Q, Li R, Tabata S, Cheng S, Zhang S, Zhang S, Huang S, Sato S, Sun S, Kwon S J, Choi S R, Lee T H, Fan W, Zhao X, Tan X, Xu X, Wang Y, Qiu Y, Yin Y, Li Y, Du Y, Liao Y, Lim Y, Narusaka Y, Wang Y, Wang Z, Li Z, Wang Z, Xiong Z, Zhang Z.The genome of the mesopolyploid crop species Brassica rapa. Nat Genet, 2011, 43: 1035-1039
[12] Liu S Y, Liu Y M, Yang X H, Tong C B, Edwards D, Parkin I A P, Zhao M X, Ma J X, Yu J Y, Huang S M, Wang X Y, Wang J Y, Lu K, Fang Z Y, Bancroft I, Yang T J, Hu Q, Wang X F, Yue Z, Li H J, Yang L F, Wu J, Zhou Q, Wang W X, King G J, Pires J C, Lu C X, Wu Z Y, Sampath P, Wang Z, Guo H, Pan S K, Yang L M, Min J M, Zhang D, Jin D C, Li W S, Belcram H, Tu J X, Guan M, Qi C K, Du D Z, Li J N, Jiang L C, Batley J, Sharpe A G, Park B S, Ruperao P, Cheng F, Waminal N E, Huang Y, Dong C H, Wang L, Li J P, Hu Z Y, Zhuang M, Huang Y, Huang J Y, Shi J Q, Mei D S, Liu J, Lee T H, Wang J P, Jin H Z, Li Z Y, Li X, Zhang J F, Xiao L, Zhou Y M, Liu Z S, Liu X Q, Qin R, Tang X, Liu W B, Wang Y P, Zhang Y Y, Lee J, Kim H H, Denoeud F, Xu X, Liang X M, Hua W, Wang X W, Wang J, Chalhoub B, Paterson A H. The Brassica oleracea genome reveals the asymmetrical evolution of polyploid genomes. Nat Commun, 2014, 5: 3930
[13] Fu Y, Lu K, Qian L W, Mei J Q, Wei D Y, Peng X H, Xu X F, Li J N, Frauen M, Dreyer F, Snowdon R J, Qian W.Development of genic cleavage markers in association with seed glucosinolate content in canola.Theor Appl Genet, 2015, 128: 1029-1037
doi: 10.1007/s00122-015-2487-z pmid: 25748114
[14] Howell P M, Sharpe A G, Lydiate D J.Homoeologous loci control the accumulation of seed glucosinolates in oilseed rape (Brassica napus). Genome, 2003, 46: 454-460
[15] Zhao J, Meng J.Detection of loci controlling seed glucosinolate content and their association with Sclerotinia resistance in Brassica napus. Plant Breed, 2003, 122: 19-23
doi: 10.1046/j.1439-0523.2003.00784.x
[16] Li F, Chen B Y, Xu K, Wu J F, Song W L, Bancroft I, Harper A L, Trick M, Liu S Y, Gao G Z, Wang N, Yan G X, Qiao J W, Li J, Li H, Xiao X, Zhang T Y, Wu X M.Genome-wide association sudy dissects the genetic architecture of seed weight and seed quality in rapeseed (Brassica napus L.). DNA Res, 2014, 21: 355-367
doi: 10.1093/dnares/dsu002 pmid: 24510440
[17] Qu C M, Li S M, Duan X J, Fan J H, Jia L D, Zhao H Y, Lu K, Li J N, Xu X F, Wang R.Identification of candidate genes for seed glucosinolate content using association mapping in Brassica napus L. Genes, 2015, 6: 1215-1229
[18] Harper A L, Trick M, Higgins J, Fraser F, Clissold L, Wells R, Hattori C, Werner P, Bancroft I.Associative transcriptomics of traits in the polyploid crop species Brassica napus. Nat Biotechnol, 2012, 30: 798-802
doi: 10.1038/nbt.2302 pmid: 20
[19] Lu G, Harper A L, Trick M, Morgan C, Fraser F, O'Neill C, Bancroft I. Associative transcriptomics study dissects the genetic architecture of seed glucosinolate content in Brassica napus. DNA Res, 2014, 21: 613-625
[20] Langfelder P, Horvath S.WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics, 2008, 9: 559
doi: 10.1186/1471-2105-9-559
[21] 宋长新, 雷萍, 王婷. 基于WGCNA算法的基因共表达网络构建理论及其R软件实现. 基因组学与应用生物学, 2013, 32: 135-141
doi: 10.3969/gab.032.000135
Song C X, Lei P, Wang T.Gene co-expression network analysis ased onWGCNA algorithm-theory and implementation in R Software.Genom Appl Biol, 2013, 32: 135-141 (in Chinese with English abstract)
doi: 10.3969/gab.032.000135
[22] Farber C R.Systems-level analysis of genome-wide association data.G3-Genes Genom Genet, 2013, 3: 119-129
doi: 10.1534/g3.112.004788 pmid: 23316444
[23] SAS V9.13 software.SAS Institute, Cary, NC, USA, 2005
[24] Li H, Durbin R.Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics, 2009, 25: 1754-1760
doi: 10.1093/bioinformatics/btp324
[25] McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo M A. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data.Genome Res, 2010, 20: 1297-1303
doi: 10.1101/gr.107524.110
[26] Aulchenko Y S, Ripke S, Isaacs A, Van Duijn C M. GenABEL: an R library for genome-wide association analysis.Bioinformatics, 2007, 23: 1294-1296
doi: 10.1093/bioinformatics/btm108 pmid: 17384015
[27] Merk H L, Yarnes S C, Van Deynze A.Trait diversity and potential for selection indices based on variation among regionally adapted processing tomato germplasm.J Am Soc Hort Sci, 2012, 137: 427-437
doi: 10.1002/aur.1564
[28] Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D R, Pimentel H, Salzberg S L, Rinn J L, Pachter L.Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.Nat Protoc, 2012, 7: 562-578
doi: 10.1038/nprot.2012.016
[29] Li Q, Yang X H, Xu S T, Cai Y, Zhang D L, Han Y J, Li L, Zhang Z X, Gao S B, Li J S, Yan J B.Genome-wide association studies identified three independent polymorphisms associated with alpha-tocopherol content in maize kernels.PLoS One, 2012, 7: e36807
doi: 10.1371/journal.pone.0036807 pmid: 3352922
[30] Kroymann J, Textor S, Tokuhisa J G, Falk K L, Bartram S, Gershenzon J, Mitchell-Olds T.A gene controlling variation in Arabidopsis glucosinolate composition is part of the methionine chain elongation pathway.Plant Physiol, 2001, 127: 1077-1088
doi: 10.1104/pp.010416
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