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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (11): 2099-2110.doi: 10.3724/SP.J.1006.2021.04245


Genome-wide association study of seed density and its related traits in Brassica napus L.

LEI Wei1,2(), WANG Rui-Li1, WANG Liu-Yan1, YUAN Fang1,2, MENG Li-Jiao1,2, XING Ming-Li1,2, XU Lu1,2, TANG Zhang-Lin1,2, LI Jia-Na1,2, CUI Cui1,*(), ZHOU Qing-Yuan1,2,*()   

  1. 1College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
    2Chongqing rape Engineering Technology Research Center, Chongqing 400715, China
  • Received:2020-10-17 Accepted:2021-03-19 Online:2021-11-12 Published:2021-04-01
  • Contact: CUI Cui,ZHOU Qing-Yuan E-mail:1305600171@qq.com;cuicui@swu.edu.cn;qingyuan@swu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2018YFD0100500);China Agriculture Research System(CARS-12);Chongqing Technology Innovation and Application Development Project(cstc2019jscx-msxmX0383)


Seed density reflects the accumulation characteristics of crop photosynthetic products in the grains, which plays an important role in the thousand-seed weight of rape. Selecting high seed density germplasm resources and studying the genetic characteristics of seed density are very important in the breeding of rapeseed. A natural population containing 187 Brassica napus L. varieties (lines) with different genetic backgrounds was used as plant materials to determine the seed density and its related traits (thousand-seed weight and seed volume) in the two environments. Genome-wide association study was carried out based on the optimal model and the candidate genes associated with seed density, thousand-seed weight, and seed volume was predicted. In the two years, there were significant differences in the seed density and its related traits among 187 materials at P < 0.05, and three materials with high seed density or thousand-seed weight were selected. A total of 24 SNP loci, that were significantly associated with seed density, seed weight, and seed volume, were identified by GWAS, which explained the phenotypic variation of 8.21%-10.40%. Haplotype analysis was used to determine the block interval of the SNP sites. The blocks containing 11 SNPs covered 12 candidate genes, which mainly encoded transcription factors such as WOX8, HAIKU1, AP2/ERF transcription factors, Dof family-zinc finger superfamily, BZR1 transcription factors, enzymes such as BKI1, KAT2, CEL1, UBP15, DNA binding proteins, and hormone response proteins such as ARF2 and J3. These results provide the theoretical basis for the development of high seed density rape varieties and the functional research of subsequent genes.

Key words: Brassica napus L., seed density, thousand-seed weight, seed volume, genome-wide association study (GWAS)

Fig. 1

Frequency distribution of seed density, 1000-seed weight, and seed volume in two environments"

Table 1

Phenotypic statistics of seed density, thousand-seed weight, and seed volume in 187 Brassica napus L."

CV (%)
籽粒容重Seed density (kg m-3) 2018-2019 969.05 527.31 859.96** 61.16 7.11
2019-2020 946.00 433.93 804.35** 99.47 12.37
体积Seed volume (×10-9 m3) 2018-2019 7.30 2.58 4.60** 0.89 19.38
2019-2020 11.97 1.28 3.52** 1.27 35.93
千粒重Thousand-seed weight (×10-3 kg) 2018-2019 5.75 1.73 3.95** 0.76 19.24
2019-2020 7.39 0.70 2.80** 0.89 31.79

Table 2

Phenotypic statistics of the thousand-seed weight and seed volume of 81 high test density in Brassica napus L."

CV (%)
籽粒容重Seed density (kg m-3) 2018-2019 969.05 860.09 901.76** 26.41 2.93
2019-2020 946.00 806.90 875.16** 31.35 3.58
体积Seed volume (×10-9 m3) 2018-2019 6.36 2.69 4.51** 0.78 17.30
2019-2020 6.01 1.28 3.19** 0.87 27.26
千粒重Thousand-seed weight (×10-3 kg) 2018-2019 5.75 2.36 4.06** 0.69 16.87
2019-2020 5.01 1.16 2.78** 0.74 26.54

Table 3

Correlation analysis of three traits of rape seeds"

Seed density
Seed volume
Thousand-seed weight
容重Seed density 1 -0.29** 0.1
体积Seed volume -0.03 1 0.90**
千粒重Thousand-seed weight 0.31** 0.93** 1

Fig. 2

Quantile-Quantile plot for six models of association analysis using the optimal model for seeds density and its component trait in two years A: seed density in 2018 and 2019; B: seed density in 2019 and 2020; C: thousand-seed weight in 2018 and 2019; D: thousand-seed weight in 2019 and 2020; E: seed volume in 2018 and 2019; F: seed volume in 2019 and 2020."

Fig. 3

Manhattan plot of seeds density and its component trait in two years A: seed density in 2018 and 2019; B: seed density in 2019 and 2020; C: thousand-seed weight in 2018 and 2019; D: thousand-seed weight in 2019 and 2020; E: seed volume in 2018 and 2019; F: seed volume in 2019 and 2020."

Table 4

Summary of significant SNPs for seed density, thousand-seed weight, and seed volume"

R2 (%)
Seed density
2018-2019 K+PCA Bn-A01-p6774837 A01 6194252 5.12 9.52 T/C
Bn-scaff_17067_1-p175366 C02 27613356 5.06 8.64 T/C
Bn-A03-p8112016 C03 9899295 4.64 8.43 A/G
Bn-scaff_16069_1-p1916253 C07 38327703 4.59 9.39 A/G
Bn-scaff_21269_1-p313587 C08 37159594 4.53 8.54 A/G
2019-2020 K+PCA Bn-scaff_16394_2-p777469 C03 50479817 5.58 10.36 T/C
Bn-scaff_16394_2-p510062 C03 50830988 4.95 9.20 T/C
Seed volume
2018-2019 K+PCA Bn-A10-p9973634 C09 40023501 4.41 8.22 A/C
2019-2020 K+PCA Bn-Scaffold000232-p58128 A07 826353 5.32 9.88 A/G
R2 (%)
Bn-A07-p3618045 A07 5593584 5.32 9.88 T/C
Bn-A10-p5210204 A10 4798717 4.43 8.25 T/G
Bn-A10-p12933288 A10 12981101 4.88 9.07 A/C
Bn-scaff_15838_5-p603655 C01 3432942 5.03 9.34 T/C
Bn-scaff_17731_1-p749457 C01 7033263 4.41 8.21 A/C
Bn-scaff_17731_1-p979512 C01 7260158 4.76 8.84 A/G
Bn-scaff_21186_1-p36313 C04 43747053 4.65 8.65 A/G
Bn-scaff_21566_1-p5088 C04 43859433 4.60 8.56 A/C
Bn-scaff_16770_1-p306276 C05 35633542 5.26 9.78 T/C
Bn-scaff_20270_1-p1211632 C05 41648492 5.04 9.37 T/C
Bn-scaff_18439_1-p633449 C06 12752920 5.22 9.70 T/C
Bn-scaff_15763_1-p595094 C06 20165181 4.65 8.64 T/G
Bn-A07-p16045526 C06 26606197 4.63 8.62 A/C
千粒重Thousand-seed weight 2018-2019 K
2019-2020 K Bn-scaff_21186_1-p36313 C04 43747053 4.55 8.43 A/G
Bn-scaff_21566_1-p5088 C04 43859433 5.62 10.40 A/C

Fig. 4

Blocks including significant SNP markers and their candidate genes"

Table 5

Summary of candidate genes associated with seed density and related traits in Brassica napus L."

Physical interval
Gene ID in B. napus
Arabidopsis gene
SD 26393356-28833356 BnaC02g28720D AT5G42750 BKI1 [35]
9439295-10359295 BnaC03g18470D AT2G33150 KAT2 [36]
BnaC03g18850D AT5G45980 WOX8 [37]
BnaC03g19320D AT2G35230 IKU1 [38]
BnaC03g19550D AT2G35700 ERF38 [39]
37927703-38727703 BnaC07g36540D AT4G21030 ATDOF4.2 [40-41]
TSW 43147053-44459433 BnaC04g43460D AT1G70710 CEL1 [42]
SV 2632942-4232942 BnaC01g06800D AT1G17110 UBP15 [43]
6233263-7833263 BnaC01g11380D AT1G75080 BZR1 [35]
43147053-44459433 BnaC04g43460D AT1G70710 CEL1 [42]
12082920-13422920 BnaC06g10480D AT1G54060 ASIL1 [44]
39083501-40963501 BnaC09g35740D AT3G61830 ARF2 [45]
BnaC09g36560D AT3G44110 J3 [46]
[1] 刘后利. 实用油菜栽培学. 上海: 上海科学技术出版社, 1987. pp 316-320.
Liu H L. Practical Rape Cultivation. Shanghai: Shanghai Scientific and Technical Publishers, 1987. pp 316-320(in Chinese).
[2] 涂金星, 傅廷栋. 油菜品质育种现状及展望. 植物遗传资源学报, 2001, 2(4):53-58.
Tu J X, Fu T D. The status and prospects of quality breeding of rape. J Plant Genet Resour, 2001, 2(4):53-58 (in Chinese with English abstract).
[3] Clarke J M, Simpson G M. Influence of irrigation and seeding rates on yield and yield components of Brassica napus L. cv. tower. Can J Plant Sci, 1978, 58: 731-737.
doi: 10.4141/cjps78-108
[4] Butruille D V, Guries R P, Osborn T C. Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L. Genetics, 1999, 153: 949-964.
pmid: 10511570
[5] Lionneton E, Aubert G, Ochatt S, Merah O. Genetic analysis of agronomic and quality traits in mustard (Brassica juncea). Theor Appl Genet, 2004, 109: 792-799.
pmid: 15340689
[6] Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft L, Zhao J Y, Meng J L. Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus L. Genetics, 2009, 182: 851-861.
doi: 10.1534/genetics.109.101642
[7] 惠飞虎, 石剑飞, 孙家刚, 冷锁虎, 唐瑶, 左青松. 油菜的源库关系研究: III. 油菜库容变化对粒重的影响. 江苏农业学报, 2006, 22: 109-112.
Hui F H, Shi J F, Sun J G, Leng S H, Tang Y, Zuo Q S. Studies on source and sink of rapeseed: III. Effect of sink change on seed weight in rapeseed. J Jiangsu Agric Sci, 2006, 22: 109-112 (in Chinese with English abstract).
[8] 易斌, 陈伟, 马朝芝, 傅廷栋, 涂金星. 甘蓝型油菜产量及相关性状的QTL分析. 作物学报, 2006, 32: 676-682.
Yi B, Chen W, Ma C Z, Fu T D, Tu J X. Mapping of quantitative trait loci for yield and yield components in Brassica napus L. Acta Agron Sin, 2006, 32: 676-682 (in Chinese with English abstract).
[9] Quijada P A, Udall J A, Lambert B, Osborn T C. Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm. Theor Appl Genet, 2006, 113: 549-561.
pmid: 16767447
[10] Udall J A, Quijada P A, Lambert B, Osborn T C. Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. Identification of alleles from unadapted germplasm. Theor Appl Genet, 2006, 113: 597-609.
doi: 10.1007/s00122-006-0324-0
[11] Radoev M, Becker H C, Ecke W. Genetic analysis of heterosis for yield and yield components in rapeseed (Brassica napus L.) by quantitative trait locus mapping. Genetics, 2008, 179: 1547-1558.
doi: 10.1534/genetics.108.089680
[12] Fan C C, Cai G Q, Qin J, Li Q Y, Yang M G, Wu J Z, Fu T D, Liu K D, Zhou Y M. Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus L. Theor Appl Genet, 2010, 121: 1289-1301.
doi: 10.1007/s00122-010-1388-4
[13] Yang P, Shu C, Chen L, Xu J, Wu J, Liu K. Identification of a major QTL for silique length and seed weight in oilseed rape (Brassica napus L.). Theor Appl Genet, 2012, 125: 285-296.
doi: 10.1007/s00122-012-1833-7 pmid: 22406980
[14] Qin L P, Mao L, Sun C M, Pu Y Y, Fu T D, Ma C Z, Shen J X, Tu J X, Yi B, Wu J. Interpreting the genetic basis of silique traits in Brassica napus L. using a joint QTL network. Plant Breed, 2014, 133: 52-60.
doi: 10.1111/pbr.2014.133.issue-1
[15] Li F, Chen B Y, Xu K, Wu J F, Song W L, Bancroft I, Harper A L, Trict M, Liu S Y, Gao G Z, Wang N A, Yan G X, Qiao J W, Li J, Li H, Xiao X, Zhang T Y, Wu X M. Genome-wide association study 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
[16] 荐红举, 魏丽娟, 李超, 唐章林, 李加纳, 刘列钊. 基于SNP遗传图谱定位甘蓝型油菜千粒重QTL位点. 中国农业科学, 2014, 47: 3953-3961.
Jian H J, Wei L J, Li C, Tang Z L, Li J N, Liu L Z. QTL mapping of 1000-seed weight in Brassica napus L. by using the high density SNP genetic map. Sci Agric Sin, 2014, 47: 3953-3961 (in Chinese with English abstract).
[17] 张晓芳, 张玉良. 我国小麦籽粒容重的研究. 作物品种资源, 1997, 12(2):24-25.
Zhang X F, Zhang Y L. Studies on the seed density of wheat in our country. Crop Var Res, 1997, 12(2):24-25 (in Chinese with English abstract).
[18] 刘保华, 马永安, 赵勇, 田纪春, 海燕, 杨学举. 普通小麦籽粒比重的QTL分析. 河北农业大学学报, 2013, 36(5):1-5.
Liu B H, Ma Y A, Zhao Y, Tian J C, Hai Y, Yang X J. QTLs mapping for grain specific gravity in common wheat. J Agric Univ Hebei, 2013, 36(5):1-5 (in Chinese with English abstract).
[19] 王霖, 冯维营, 黄玲, 邵敏敏, 孙雷明, 王洪刚. 小麦容重QTL定位. 山东农业科学, 2014, 46(4):24-27.
Wang L, Feng W Y, Huang L, Shao M M, Sun L M, Wang H G. QTL mapping for wheat test weight. Shandong Agric Sci, 2014, 46(4):24-27 (in Chinese with English abstract).
[20] 车海先, 李海玉. 玉米容重影响因素浅析. 粮食与食品工业, 2011, 18(1):56-58.
Che H X, Li H Y. Analysis of influencing factors on maize test weight. Cereal Food Ind, 2011, 18(1):56-58 (in Chinese with English abstract).
[21] Ding J Q, Ma J L, Zhang C R, Dong H F, Xi Z Y, Xia Z L, Wu J Y. QTL mapping for test weight by using F2:3 population in maize. J Genet, 2011, 90: 75-80.
doi: 10.1007/s12041-011-0036-3
[22] 许理文, 段民孝, 田红丽, 宋伟, 王凤格, 赵久然, 刘保林, 王守才. 基于SNP标记的玉米容重QTL分析. 玉米科学, 2015, 23(5):21-25.
Xu L W, Duan M X, Tian H L, Song W, Wang F G, Zhao J R, Liu B L, Wang S C. QTL Identification for test weight based on SNP mapping in maize. J Maize Sci, 2015, 23(5):21-25 (in Chinese with English abstract).
[23] 郭晋杰, 韩新桐, 张静, 陈景堂. 基于高密度遗传连锁图谱定位玉米子粒容重及相关性状QTL. 玉米科学, 2018, 26(6):27-32.
Guo J J, Han X T, Zhang J, Chen J T. High-density genetic linkage map construction and QTL mapping for kernel test weight and related traits in maize. J Maize Sci, 2018, 26(6):27-32 (in Chinese with English abstract).
[24] 刘文博. 大豆籽粒容重与种子萌发的相关性研究. 沈阳农业大学硕士学位论文, 辽宁沈阳, 2018.
Liu W B. Correlation between Bulk Density and Seed Germination. MS Thesis of Shenyang Agricultural University, Shenyang, Liaoning, China, 2018 (in Chinese with English abstract).
[25] Sun C M, Wang B Q, Yan L, Hu K N, Liu S, Zhou Y M, Guan C Y, Zhang Z Q, Li J N, Zhang J F, Chen S, Wen J, Ma C Z, Tu J X, Shen J X, Fu T D, Yi B. Genome-wide association study provides Insight into the genetic control of plant height in rapeseed (Brassica napus L.). Front Plant Sci, 2016, 7: 1102-1114.
[26] Chen L L, Wan H P, Qian J L, Guo J B, Sun C M, Wen J, Yi B, Ma C Z, Tu J X, Song L Q, Fu T D, Shen J X. Genome-wide association study of cadmium accumulation at the seedling stage in rapeseed (Brassica napus L.). Front Plant Sci, 2018, 9: 375-389.
doi: 10.3389/fpls.2018.00375
[27] Xu L P, Hu K N, Zhang Z Q, Guan C Y, Chen S, Hua W, Li J N, Wen J, Yi B, Shen J X, Ma C Z, Tu J X, Fu T D. Genome-wide association study reveals the genetic architecture of flowering time in rapeseed (Brassica napus L.). DNA Res, 2016, 23: 43-52.
[28] Liu S, Fan C C, Li J N, Cai G Q, Yang Q Y, Wu J, Yi X Q, Zhang C Y, Zhou Y M. A genome-wide association study reveals novel elite allelic variations in seed oil content of Brassica napus L. Theor Appl Genet, 2016, 129: 1203-1215.
doi: 10.1007/s00122-016-2697-z
[29] Hatzig S V, Frisch M, Breuer F, Nesi N, Ducourmau S, Wagner M H, Leckband G, Abbdadi A, Snowdon R J. Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus L. Front Plant Sci, 2015, 6: 221-233.
doi: 10.3389/fpls.2015.00221 pmid: 25914704
[30] 韩光明, 蓝家样, 陈全求, 张胜昔, 李国荣. 一种利用ImageJ软件对棉花种子的计数方法. 棉花科学, 2019, 41(2):2-5.
Han G M, Lan J Y, Chen Q Q, Zhang S X, Li G R. A method for counting cotton seeds using ImageJ software. Cotton Sci, 2019, 41(2):2-5 (in Chinese with English abstract).
[31] Qu C M, Li J N, Fu F Y, Zhao H Y, Lu K, Wei L J, Xu X F, Liang Y, Li S M, Wang R, Li J N. Genome-wide association mapping and Identification of candidate genes for fatty acid composition in Brassica napus L. using SNP markers. BMC Genomics, 2017, 18: 232-248.
doi: 10.1186/s12864-017-3607-8
[32] Wan H P, Chen L L, Guo J B, Li Q, Wen J, Yi B, Ma C Z, Tu J X, Fu T D, Shen J X. Genome-wide association study reveals the genetic architecture underlying salt tolerance-related traits in rapeseed (Brassica napus L.). Front Plant Sci, 2017, 8: 593-607.
doi: 10.3389/fpls.2017.00593
[33] Bradbury P J, Zhang Z W, Kroon D E, Casstevens T M, Ramdoss Y, Buckler E S. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 2007, 23: 2633-2635.
pmid: 17586829
[34] Barrett J, Fry B, Maller J, Daly M. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 2005, 21: 263-265.
pmid: 15297300
[35] Jiang W, Huang H, Hu Y, Zhu S, Wang Z, Lin W. Brassinosteroid regulates seed size and shape in Arabidopsis. Plant Physiol, 2013, 162: 1965-1977.
doi: 10.1104/pp.113.217703
[36] Footitt S, Cornah J E, Pracharoenwattan A I, Bryce J H, Smith S M. The Arabidopsis 3-ketoacyl-CoA thiolase-2 (kat2-1) mutant exhibits increased flowering but reduced reproductive success. J Exp Bot, 2007, 58: 2959-2968.
pmid: 17728299
[37] Zhu T, Moschou P N, Alvarezi J M, Sohlberg J J, Von A S. Wuschel-related homeobox 2 is important for protoderm and suspensor development in the gymnosperm norway spruce. BMC Plant Biol, 2016, 16: 19-33.
doi: 10.1186/s12870-016-0706-7
[38] Garcia D, Fitz G J N, Berger F. Maternal control of integument cell elongation and zygotic control of endosperm growth are coordinated to determine seed size in Arabidopsis. Plant Cell, 2005, 17: 52-60.
doi: 10.1105/tpc.104.027136
[39] Lasserre E, Jobet E, Llauro C, Delseny M. AtERF38 (At2g35700), an AP2/ERF family transcription factor gene from Arabidopsis thaliana, is expressed in specific cell types of roots, stems and seeds that undergo suberization. Plant Physiol Biochem, 2008, 46: 1051-1061.
[40] Kushwaha H, Jillo K W, Singhl V K, Kumar A, Yadav D. Assessment of genetic diversity among cereals and millets based on PCR amplification using Dof (DNA binding with One Finger) transcription factor gene-specific primers. Plant Syst Evol, 2015, 301: 833-840.
doi: 10.1007/s00606-014-1095-8
[41] Gupta S, Pathak R K, Gupta S M, Gaur V S, Singh N K, Kumar A. Identification and molecular characterization of transcription factor gene family preferentially expressed in developing spikes of Eleusine coracana L. 3 Biotech, 2018, 8: 82.
doi: 10.1007/s13205-017-1068-z
[42] Shani Z, Dekel M, Tsabary G, Shoseyov O. Cloning and characterization of elongation specific endo-1,4-[beta]-glucanase (cel1) from Arabidopsis thaliana. Plant Mol Biol, 1997, 34: 837-842.
pmid: 9290636
[43] Li N, Li Y. Ubiquitin-mediated control of seed size in plants. Front Plant Sci, 2014, 5: 332-337
[44] Gao M J, Lydiate D J, Li X, Lui H, Gjetvaj B, Hegedus D D, Rozwadowski K. Repression of seed maturation genes by a trihelix transcriptional repressor in Arabidopsis seedlings. Plant Cell, 2009, 21: 54-71.
doi: 10.1105/tpc.108.061309
[45] Schruff M, Spielman M, Tiwari S, Adams S, Fenby N, Scott R. The anxin response factor 2 gene of Arabidopsis links auxin signalling, cell division, and the size of seeds and other organs. Development, 2006, 133: 251-261.
pmid: 16339187
[46] Salas-Munoz S, Rodriguez-Hernandez A A, Ortega-Amaro M A, Salazar-Badillo F B, Jimenez-Bremont J F. Arabidopsis AtDjA3 null mutant shows increased sensitivity to abscisic acid, salt, and osmotic stress in germination and post-germination stages. Front Plant Sci, 2016, 7: 220-230.
[47] Chen W, Zhang Y, Liu X P, Chen B Y, Tu J X, Fu T D. Detection of QTL for six yield-related traits in oilseed rape (Brassica napus) using DH and immortalized F2 populations. Theor Appl Genet, 2007, 115: 849-858.
pmid: 17665168
[48] Brinton J, Simmonds J, Uauy C. Ubiquitin-related genes are differentially expressed in isogenic lines contrasting for pericarp cell size and grain weight in hexaploid wheat. BMC Plant Biol, 2018, 18: 22-28.
doi: 10.1186/s12870-018-1241-5 pmid: 29370763
[49] Khan S U, Yang M J, Liu S, Zhang K, Khan M H U, Zhai Y G, Olalekan A, Fan C C, Zhou Y M. Genome-wide association studies in the genetic dissection of ovule number, seed number, and seed weight in Brassica napus L. Ind Crops Prod, 2019, 142: 111877.
doi: 10.1016/j.indcrop.2019.111877
[50] 孙程明, 陈锋, 陈松, 彭琦, 张维, 易斌, 张洁夫, 傅廷栋. 甘蓝型油菜每角粒数的全基因组关联分析. 作物学报, 2020, 46: 147-153.
Sun C M, Chen F, Chen S, Peng Q, Zhang W, Yi B, Zhang J F, Fu T D. Genome-wide association study of seed number per silique in rapeseed (Brassica napus L.). Acta Agron Sin, 2020, 46: 147-153(in Chinese with English abstract).
[51] 任义英, 崔翠, 王倩, 唐章林, 徐新福, 林呐, 殷家明, 李加纳, 周清元. 油菜主花序角果密度及其相关性状的全基因组关联分析. 中国农业科学, 2018, 51: 1020-1033.
Ren Y Y, Cui C, Wang Q, Tang Z L, Xu X F, Lin N, Yin J M, Li J N, Zhou Q Y. Genome-wide association analysis of silique density on racemes and its component traits in Brassica napus L. Sci Agric Sin, 2018, 51: 1020-1033 (in Chinese with English abstract).
[52] 周庆红, 周灿, 郑伟, 付东辉. 甘蓝型油菜角果长度全基因组关联分析. 中国农业科学, 2017, 50: 228-239.
Zhou Q H, Zhou C, Zheng W, Fu D H. Genome wide association analysis of silique length in Brassica napus L. Sci Agric Sin, 2017, 50: 228-239 (in Chinese with English abstract).
[53] Li N, Li Y. Signaling pathways of seed size control in plants. Curr Opin Plant Biol, 2016, 33: 23-32.
doi: 10.1016/j.pbi.2016.05.008
[54] 张雪晶, 江文波, 庞永珍. 植物种子大小调控机制的研究进展. 植物生理学报, 2016, 52: 998-1010.
Zhang X J, Jiang W B, Pang Y Z. Advances in the regulation mechanism of plant seed size. J Plant Physiol, 2016, 52: 998-1010 (in Chinese with English abstract)
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