欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2015, Vol. 41 ›› Issue (10): 1481-1489.doi: 10.3724/SP.J.1006.2015.01481

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

利用非条件和条件QTL解析油菜产量相关性状的遗传关系

焦聪聪1,2,黄吉祥2,汪义龙3,张晓玉4,2,熊化鑫1,2,倪西源2,赵坚义2,*   

  1. 1 浙江师范大学化学与生命科学学院, 浙江金华 321000；2 浙江农业科学院作物与核技术利用研究所, 浙江杭州 310021；3 上海捷瑞生物工程有限公司，上海 201619；4 杭州师范大学生命与环境科学学院，浙江杭州 310036
  • 收稿日期:2015-02-11 修回日期:2015-06-01 出版日期:2015-10-12 网络出版日期:2015-10-12
  • 通讯作者: 赵坚义，E-mail: jyzhao3@yahoo.com, Tel: 0571-86403406
  • 基金资助:

    本研究由国家高技术研究发展计划(863计划)项目(2011AA10A104)，浙江省重大科技专项(2012C12902-1)和浙江省旱作粮油科技创新团队项目(2011R50026-7)资助。

Genetic Analysis of Yield-Associated Traits by Unconditional and Conditional QTL in Brassica napus

JIAO Cong-Cong1,2,HUANG Ji-Xiang2,WANG Yi-Long3,ZHANG Xiao-Yu4,2,XIONG Hua-Xin1,2,NI Xi-Yuan2,ZHAO Jian-Yi2,*   

  1. 1 College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua 321000, China; 2 Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; 3 Shanghai Generay Biotech Co., Ltd, Shanghai 201619, China; 4 College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, China
  • Received:2015-02-11 Revised:2015-06-01 Published:2015-10-12 Published online:2015-10-12
  • Contact: 赵坚义，E-mail: jyzhao3@yahoo.com, Tel: 0571-86403406jiaodacong@126.comjiaodacong@126.com

RichHTML

PDF

1564

被引次数

5

摘要：

基于前期研究中构建的Sollux/Gaoyou DH群体在9个环境中的表型数据和新版遗传图谱，对油菜角果长度进行QTL定位，估测QTL的加性、上位性和环境互作效应。并通过条件QTL方法，解析角果长度与角果粒数和粒重之间的遗传关系，以期利用标记辅助，探讨通过选择角果长度基因型以增加角果粒数、提高千粒重，最终达到增加产量的可能性。结果共检测到在3个环境以上稳定表达的控制角果长度QTL 8个，加性效应值在0.09~0.26 cm之间，效应总和解释群体遗传总变异的60%。8对上位性QTL效应值在0.035~0.075 cm之间，效应总和为加性总效应的38%。QTL与环境互作效应只在少数位点和个别环境中显著。条件QTL研究表明，qSLA2、qSLC1-2和qSLC8-1位点，角果长度的变化对角果粒数影响较大；而通过选择qSLA7、qSLC1-2、qSLC8-1和qSLC8-2长角果标记基因型，可望同时提高角果粒数和千粒重。6个主效QTL 11个连锁标记基因型和表现型的关联分析，验证了条件QTL分析结果，表明通过对qSLA2、qSLA7、qSLC8-1和qSLC8-2位点6个连锁标记(ZAAS423、SUC1-3、ZAAS12a、ZAASA7-28、ZAAS433和ZAAS437)长角果基因型的聚合，可增长角果约2 cm，间接增加角果粒数2粒，同时提高千粒重0.4 g，从而可望实质性地提高油菜产量水平。

关键词: 甘蓝型油菜, 角果长度, 角果粒数, 千粒重, 非条件QTL, 条件QTL

Abstract:

Quantitative Trait Loci (QTLs) for silique length (SL) were mapped in the updated SG map using the phenotypic data from nine environments. QTLs with additive and epistatic effects and their interactions with environments were estimated. At QTL level, conditional QTL analysis was performed to dissect the genetic relationships between silique length and seed number per silique (SS), and between silique length and 1000-seed weight (SW). Our goal was to identify QTLs that are important for silique length, as indexed by their positive correlations with either seeds per silique or 1000-seed weight, or both of the traits. Markers linked to the target QTL can be developed for indirect selection of SS and SW. As shown by the results, we detected eight QTLs with additive effects, which together accounted for around 60% of the phenotypic variations. While the total effects of eight pairs of epistatic loci (additive × additive) ranged from 0.035 to 0.075 cm and their summation was 38% of the total additive effects. QTL by environmental interactions were significant only in few environments with small amount of genetic effects. The conditional QTL analysis revealed large impact of silique length on seed number per silique in three QTLs (qSLA2, qSLC1-2, and qSLC8-1). Allelic selection for long silique length in qSLA7, qSLC1-2, qSLC8-1, and qSLC8-2 loci could potentially increase the seed number per silique (SS) and 1000-seed weight (SW). Association analysis between genotypes linking to six related QTLs and the corresponding phenotypes of yield related traits indicated that the combination of long silique alleles from four QTLs (qSLA2, qSLA7, qSLC8-1, and qSLC8-2) by marker assistant selection of ZAAS423, SUC1-3, ZAAS12a, ZAASA7-28, ZAAS433, and ZAAS437 significantly increased about two cm in silique length. Meanwhile, two additional seeds per silique were increased, and the 1000-seed weight was enhanced by 0.4 g. Taken together, we suggest the importance of these QTLs and markers for yield breeding purpose in Brassica napus.

Key words: Brassica napus L., Silique length, Seed number per silique, 1000-seed weight, Unconditional QTL, conditional QTL

[1]Shi J Q, Li R Y, Qiu D, Jiang C C, Long Y, Morgan C, Bancroft I, Zhao J Y, Meng J L. Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics, 2009, 182: 851–861



[2]Fan C C, Cai G Q, Qin J, Li Q Y, 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. Thero Appl Genet, 2001, 121: 1289–1301



[3]Chay P, Thurling N. Identification of genes controlling pod length in spring rapeseed, Brassica napus L., and their utilization for yield improvement. Plant Breed, 1989, 103: 54–62



[4]刘定富, 蔡怀武. 甘蓝型油菜特长荚突变体的发现和鉴定. 湖北农学院学报, 1994, 4(2): 1–4



Liu D F, Cai H W. Detection and identification of specially-long pod mutant in Brassica napus L. J Hubei Agric Coll, 1994, 4(2): 1–4 (in Chinese with English abstract)



[5]Cai D F, Xiao Y J, Yang W, Ye W, Wang B. Association mapping of six yield related traits in rapeseed Brassica napus L. Theor Appl Genet, 2014, 127: 85–96



[6]Zhang L W, Yang G S, Liu P W, Hong D F, Li S P, He Q B. Genetic and correlation analysis of silique-traits in Brassica napus L. by quantitative trait locus mapping. Theor Appl Genet, 2011, 122: 21–31



[7]Yang P, Shu C, Chen L, Xu J S, Wu J, Liu K D. Identification of a major QTL for silique length and seed weight in oilseed rape (Brassica napus L.). Theor Appl Genet, 2012, 125: 285–296



[8]Li N, Shi J Q, Wang X F, Liu G H. A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed (Brassica napus L.). BMC Plant Biol, 2014, 14: 114



[9]张书芬, 宋文光, 任乐见. 甘蓝型双低油菜数量性状的遗传力及基因效应. 中国油料, 1996, 18(3): 1–3



Zhang S F, Song W G, Ren L J. Heritability and genetic effects of quantitative characters in double-low rapeseed(Brassica napus L.). Oil Crops China, 1996, 18(3): 1–3 (in Chinese with English abstract)



[10]Udall J A, Qui J D, 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



[11]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 population. Theor Appl Genet, 2007, 115: 849–858



[12]Li X N, Ramchiary N, Dhandapani V, Choi S R, Hur Y, Nou I S, Yoon M K, Lim Y P. Quantitative trait loci mapping in Brassica rapa revealed the structural and functional conservation of genetic loci governing morphological and yield component traits in the A, B, and C subgenomes of Brassica species. DNA Res, 2013, 20: 1–16



[13]Zhao J Y, Becker H C, Zhang D Q, Zhang Y F, Ecke W G. Oil content in a European × Chinese rapeseed population: QTL with additive and epistatic effects and their genotype-environment interactions. Crop Sci, 2005, 45: 51–59



[14]Zhao J Y, Huang J X, Chen F, Ni X Y, Xu F, Wang Y L, Jiang C C, Wang H, Xu A X, Huang R Z, Li D R, Meng J L. Molecular mapping of Arabidopsis thaliana lipid-related orthologous genes in Brassica napus. Theor Appl Genet, 2012, 124: 407–421



[15]Zhu J. Analysis of conditional genetic effects and variance components in developmental genetics. Genetics, 1995, 141: 1633–1639



[16]Wang S C, Bastern C J, Zeng Z B. Window QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, USA, 2006



[17]Yang J, Zhu J, Williams R W. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics, 2007, 23: 1527-1536



[18]Wang D L, Zhu J, Li Z K, Paterson A H. Mapping QTLs with epistatic effects and QTL × environment interactions by mixed linear model approaches. Theor Appl Genet, 1999, 99: 1255–1264



[19]Qi L P, Mao L, Sun C M. Interpreting the genetic basis of silique traits in Brassica napus using a joint QTL network. Plant Breed, 2014, 133: 52–60



[20]Zhou Q H, Fu D H. In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus. Mol Breed, 2014, 33: 881–894
[1] 马毅娜, 吴晓明玉, 李藕琪, 王圆, 陈丽, 张盈川, 赵伦, 文静, 傅廷栋, 沈金雄. Bna-miR1040-EIF3A模块调控油菜开花时间的功能研究[J]. 作物学报, 2026, 52(2): 349-362.
[2] 王彬, 蒙姜宇, 邱浩良, 贺亚军, 钱伟. 甘蓝型油菜BnaDUF579基因家族的鉴定与表达模式分析[J]. 作物学报, 2025, 51(8): 2100-2110.
[3] 夏琦, 郭滢, 王坤美, 王思忆, 巨建业, 彭雅雯, 刘忠松, 夏石头. 甘蓝型油菜种子和种皮中水杨酸含量与原花色素积累的关系研究[J]. 作物学报, 2025, 51(5): 1189-1197.
[4] 王晓琳, 刘忠松, 康雷, 杨柳. 甘蓝型油菜角果长度和每角粒数基因定位以及角果皮转录组动态分析[J]. 作物学报, 2025, 51(4): 888-899.
[5] 张琴, 戴成, 马朝芝. 生长素响应报告基因转化甘蓝型油菜及各组织GUS动态信号分析[J]. 作物学报, 2025, 51(3): 667-675.
[6] 孙程明, 周晓婴, 陈锋, 张维, 王晓东, 彭琦, 郭月, 高建芹, 胡茂龙, 付三雄, 张洁夫. 长链非编码RNA (lncRNA)在甘蓝型油菜分枝角度调控中的功能分析与预测[J]. 作物学报, 2025, 51(3): 559-567.
[7] 雍瑞, 胡文静, 吴迪, 汪尊杰, 李东升, 赵蝶, 尤俊超, 肖永贵, 王春平. 小麦穗粒数QTL分析及其对千粒重多效性评价[J]. 作物学报, 2025, 51(2): 312-323.
[8] 黄绒, 周渠晨, 陈楚铭, 罗倩, 易东, 杜常欢, 黄祥宇, 盛锋, 杜雪竹. 过表达BnNRT2.3-like对油菜氮素吸收利用及产量的影响[J]. 作物学报, 2025, 51(12): 3184-3197.
[9] 巨建业, 杨柳, 陈浩, 康雷, 夏石头, 刘忠松. 单细胞核转录组分析揭示油菜种皮分化过程和种子颜色差异原因[J]. 作物学报, 2025, 51(11): 2860-2874.
[10] 张雯, 李玉, 王创, 石磊, 丁广大. 甘蓝型油菜磷转运蛋白BnaPT48的功能研究[J]. 作物学报, 2025, 51(11): 2983-2995.
[11] 胡志康, 舒雨, 王会, 杨莹莹, 廖俊宇, 刘佳, 成洪涛, 郭晨, 张园园, 刘胜毅, 胡琼, 梅德圣, 李超. 甘蓝型油菜苗期耐碱性种质综合鉴定与评价[J]. 作物学报, 2025, 51(10): 2681-2692.
[12] 王晨, 贺丹, 姚敏, 邱萍, 何昕, 熊兴华, 康雷, 刘忠松, 钱论文. 基于转录组分析鉴定甘蓝型油菜开花候选基因以及BnaCOR27功能验证[J]. 作物学报, 2025, 51(10): 2693-2704.
[13] 徐林珊, 郜耿东, 王宇, 王家星, 杨吉招, 武亚瑞, 张宵寒, 常影, 李真, 谢雄泽, 龚德平, 王晶, 葛贤宏. 甘蓝型油菜漆酶基因家族成员表达模式及与茎秆抗折力的关联分析[J]. 作物学报, 2025, 51(1): 134-148.
[14] 李嘉欣, 黄莹, 吴潞梅, 赵伦, 易斌, 马朝芝, 涂金星, 沈金雄, 傅廷栋, 文静. 甘蓝型油菜BnaSLY1基因进化分析及功能研究[J]. 作物学报, 2025, 51(1): 44-57.
[15] 钟元, 朱天宇, 戴成, 马朝芝. 耐亚磷酸盐除草剂转基因油菜的创建和抗性评价[J]. 作物学报, 2024, 50(5): 1158-1171.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2