欢迎访问作物学报,今天是

作物学报 ›› 2013, Vol. 39 ›› Issue (04): 632-641.doi: 10.3724/SP.J.1006.2013.00632

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于DUS测试的标准品种形态性状稳定性和重要性分析

唐浩1,2,刘洪3,余汉勇4,张浙峰5,肖应辉1,杨益善6,陈立云1   

  1. 1 湖南农业大学, 湖南长沙410128;2 农业部科技发展中心,北京100122;3 华南农业大学,广东广州510642;4 中国水稻研究所,浙江杭州310006;
  • 收稿日期:2012-10-22 修回日期:2012-12-11 出版日期:2013-04-12 网络出版日期:2013-01-28
  • 基金资助:

    本研究由国家公益性行业(农业)科研专项经费项目(200903008)资助。

Stability and Importance of Morphological Characteristics in Example Varieties Based on DUS Test

TANG Hao1,2,LIU Hong3,YU Han-Yong4,ZHANG Zhe-Feng5,XIAO Ying-Hui1,YANG Yi-Shan6,CHEN Li-Yun1   

  1. 1 Hunan Agricultural University, Changsha 410128, China; 2 Development Center for Science & Technology of Ministry of Agriculture, Beijing 100122, China; 3 South China Agricultural University, Guangzhou 510642, China; 4 China National Rice Research Institute, Hangzhou 310006, China; 5 Sichuan Academy of Agricultural Sciences, Chengdu 610066, China; 6 Hunan Hybrid Rice Research Center, Changsha 410125, China
  • Received:2012-10-22 Revised:2012-12-11 Published:2013-04-12 Published online:2013-01-28

摘要:

为了进一步提高水稻品种特异性(Distinctness)、一致性(Uniformity)和稳定性(Stability)(简称DUS)测试的准确性,以水稻DUS测试指南中列出的49份水稻标准品种为材料,应用AMMI模型对水稻品种50DUS测试性状进行了稳定性分析,并采用随机森林算法对测试性状的重要性进行了评价。结果表明,不同性状的稳定性差异较大,以质量性状和假质量性状为主的目测性状稳定性高,而以数量性状为主的量测性状稳定性较低。不同性状的重要性参数差异甚大,总体上,以数量形式表示的量测性状的重要性参数较大,而以分级代码表示的目测性状重要性参数相对较小。对于茎节包露、穗类型、穗立形状、外颖茸毛和叶茸毛等稳定性较低、重要性参数不高的性状,可从水稻DUS测试指南中去除;对成熟期、抽穗期等稳定性较低但在生产实践中很重要的农艺性状,宜安排在申请者所在地参试点测试,使其表达状态真实表现;或者采用标准品种进行校正,从而提高测试的准确性。

关键词: 水稻, DUS测试, 目测, 量测, 稳定性, 重要性

Abstract:

In order to further improve the accuracy of distinctness, uniformity and stability (ab. DUS) test of rice varieties, the AMMI model was used to analyse the stability and Random Forest algorithm was used to evalue the importance for 50 charcacteristics of 49 rice example varieties listed in DUS test guideline. The results showed that stability is largely different amony different characteristics, and visual characteristics are highly stable for main qualitive characteristics and pseudo-qualitive characteristics, and measurement characteristics had low stability for quantitative characteristics . On the whole, the importance parameter difference was very big among different characteristics, which is larger for measurement characteristics, and relatively smaller for visual characteristics. Some characteristics with low stability and low importance could be excluded from DUS guideline, for example “panicle: exsertion”, “panicle: type”, “panicle: attitude”, “Spikelet: pubescence of lemma”, “leaf: pubescence”, while those with low stability, but very important in practice, for example “time of maturity” and “time of heading” could be only in the location of applicants tested to avoid misjustice and use example varieties for correction.

Key words: Rice, DUS test, Visual observation, Measurement observation, Stability, Importance

[1]The State Council of the People’s Republic of China (中华人民共和国国务院). Regulations of the People’s Republic of China on the Protection of New Varieties of Plants (中华人民共和国植物新品种保护条例). Beijing: China Agriculture Press, 1997 (in Chinese)



[2]Department of Agriculture of the People's Republic of China (中华人民共和国农业部). Distictness, Uniformity, and Stability Test Guidline of Plant New Varieties–Rice (植物新品种DUS测试指南——水稻). Beijing: China Agriculture Press, 2007 (in Chinese)



[3]Hu B-M(胡秉民), Gong X(耿旭). Crop Stability Analysis Method (作物稳定性分析法). Beijing: Science Press, 1993 (in Chinese)



[4]Liu W-J(刘文江), Li H-J(李浩杰), Wang X-D(汪旭东), Zhou K-D(周开达). Stability analysis for elementary characters of hybrid rice by AMMI model. Acta Agron Sin (作物学报), 2002, 28(4): 569–573 (in Chinese with English abstract)



[5]Kempton R A. The use of biplots in interpreting variety by environment interactions. J Agric Sci, 1984, 103: 123–135



[6]Nachit M M. Use of AMMI and linear regression models to analyze genotype environment interaction in durum wheat. Theor Appl Genet, 1992, 83: 597–601



[7]Cooper M. Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crops Res, 1999, 64: 13–34



[8]Wang L(王磊), Yang S-H(杨仕华), Shen X-H(沈希宏), Xie F-X(谢芙贤). Additive main effects and multiplicative interaction model (AMMI) graphs used in the plant variety trial data analysis. J Nanjing Agric Univ (南京农业大学学报), 1998, 21(2): 18–23 (in Chinese with English abstract)



[9]Xu N-Y(许乃银), Chen X-S(陈旭升), Guo Z-G(郭志刚), Zhang J-Y(张坚勇), Xiao S-H(肖松华), Di J-C(狄佳春), Liu J-G(刘剑光). Application of AMMI model to analyze cotton regional trial data. Jiangsu J Agric Sci (江苏农业学报), 2001, 17(4): 205–210 (in Chinese with English abstract)



[10]Jiang K-F(蒋开锋), Zheng J-K(郑家奎), Zhao G-L(赵甘霖), Zhu Y-C(朱永川), Wan X-Q(万先齐), Ding G-X(丁国祥). Stability of grain yield characteristics and their correlation in hybrid rice. Chin J Rice Sci (中国水稻科学), 2001, 15(1): 67–69 (in Chinese with English abstract)



[11]Zhang S-M(张斯梅), Yang S-J(杨四军), Gu K-J(顾克军), Zhang H-G(张恒敢), Xu B(许博), Chen J(陈涓). Analysis of yield characteristics and stability in wheat regional trials. Chin Agric Sci Bull (中国农学通报), 2012, 28(3): 172–176 (in Chinese with English abstract)



[12]Yu Q-Y(俞琦英), Zhao W-M(赵伟明). Analysis of yield characters and its stability in rape regional trial of Zhejiang Province. Acta Agric Zhejiangensis (浙江农业学报), 2010, 22(3): 337–340 (in Chinese with English abstract)



[13]Liu X(刘鑫), Shi J-Y(石建尧), Wang M-H(王明湖). Analysis on correlation and stability in rice quality of early rice varieties tested in Zhejiang Provincial Regional Tests. Acta Agric Jiangxi (江西农业学报), 2010, 22(6): 47–48 (in Chinese with English abstract)



[14]Jiang K-F(蒋开锋), Zheng J-K(郑家奎), Zhao G-L(赵甘霖), Zhu Y-C(朱永川), Wan X-Q(万先齐). Analysis of combining ability based on AMMI model. Acta Agron Sin (作物学报), 2000, 26(6): 959–962 (in Chinese with English abstract)



[15]Wan X-Y(万向元), Hu P-S(胡培松), Wang H-L(王海莲), Kong L-N(孔令娜), Bi J-C(毕京翠), Chen L-M(陈亮明), Zhang J-Y(张坚勇), Zhai H-Q(翟虎渠), Wan J-M(万建民). Analysis on stability of AC, GT and PC in rice varieties (Orzya sativa L.). Sci Agric Sin (中国农业科学), 2005, 38(1): 1–6 (in Chinese with English abstract)



[16]Wan X-Y(万向元), Chen L-M(陈亮明), Wang H-L(王海莲), Xiao Y-H(肖应辉), Bi J-C(毕京翠), Liu X(刘喜), Zhai H-Q(翟虎渠), Wan J-M(万建民). Stability analysis for the RVA profile properties of rice starch. Acta Agron Sin (作物学报), 2004, 30(12): 1185–1191 (in Chinese with English abstract)



[17]Zhang J-Y(张坚勇), Wan X-Y(万向元), Xiao Y-H(肖应辉), Wang C-M(王春明), Liu S-J(刘世家), Chen L-M(陈亮明), Zhai H-Q(翟虎渠), Wan J-M(万建民). Analysis on stability of eating quality of cooked rice (Orzya sativa L.). Sci Agric Sin (中国农业科学), 2004, 37(6): 788–794 (in Chinese with English abstract)



[18]Zhang J-Y(张坚勇), Xiao Y-H(肖应辉), Wan X-Y(万向元), Liu S-J(刘世家), Wang C-M(王春明), Chen L-M(陈亮明), Kong L-N(孔令娜), Zhai H-Q(翟虎渠), Wan J-M(万建民). Stability analysis for appearance qualities of rice cultivar. Acta Agron Sin (作物学报), 2004, 30(6): 548–554 (in Chinese with English abstract)



[19]Breiman L. Random forests. Machine Learn, 2001, 45: 5–32



[20]Zhao T-T-G(赵铜铁钢), Yang D-W(杨大文), Cai X-M(蔡喜明), Cao Y(曹勇). Predict seasonal low flows in the upper Yangtze River using random forests model. J Hydroelect Eng (水力发电学报), 2012, (3): 18–24 (in Chinese with English abstract)



[21]Ma X(马昕), Guo J(郭静), Sun X(孙啸). Prediction of RNA-binding residues in proteins using random forest. J Southeast Univ (Nat Sci Edn)(东南大学学报?自然科学版), 2012, 42(1): 50–54 (in Chinese with English abstract)



[22]Guo Y-J (郭颖婕), Liu X-Y(刘晓燕), Guo M-Z(郭茂祖), Zou Q(邹权). Identification of plant resistance gene with random forest. J Front Comput Sci Technol (计算机科学与探索), 2012, 6(1): 67–77 (in Chinese with English abstract)



[23]Li J-G(李建更), Gao Z-K(高志坤). Random Forests: an important feature genes selection method of tumor. Acta Biophys Sin (生物物理学报), 2009, 25(1): 51–56 (in Chinese with English abstract)



[24]Peng G-L(彭国兰), Lin C-D(林成德). A model based on random forests for enterprises credit assessment. J Fuzhou Univ (Nat Sci Edn) (福州大学学报?自然科学版), 2008, (suppl-1): 153–156 (in Chinese with English abstract)



[25]Li J-G(李建更), Gao Z-K(高志坤), Ruan X-G(阮晓钢). Random forests-based gene pathway analysis of gastric cancer microarray data. J Biol (生物学杂志), 2010, (2): 1–4 (in Chinese with English abstract)



[26]SAS Institute Inc. SAS/STAT User’s Guide, Cary, NC, USA, 1988



[27]Tang Q Y, Zhang C X. Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research. Insect Sci, 2012. DOI: 10. 1111/j.1744-7917.2012. 01519.x

[1] 田甜, 陈丽娟, 何华勤. 基于Meta-QTL和RNA-seq的整合分析挖掘水稻抗稻瘟病候选基因[J]. 作物学报, 2022, 48(6): 1372-1388.
[2] 郑崇珂, 周冠华, 牛淑琳, 和亚男, 孙伟, 谢先芝. 水稻早衰突变体esl-H5的表型鉴定与基因定位[J]. 作物学报, 2022, 48(6): 1389-1400.
[3] 周文期, 强晓霞, 王森, 江静雯, 卫万荣. 水稻OsLPL2/PIR基因抗旱耐盐机制研究[J]. 作物学报, 2022, 48(6): 1401-1415.
[4] 郑小龙, 周菁清, 白杨, 邵雅芳, 章林平, 胡培松, 魏祥进. 粳稻不同穗部籽粒的淀粉与垩白品质差异及分子机制[J]. 作物学报, 2022, 48(6): 1425-1436.
[5] 颜佳倩, 顾逸彪, 薛张逸, 周天阳, 葛芊芊, 张耗, 刘立军, 王志琴, 顾骏飞, 杨建昌, 周振玲, 徐大勇. 耐盐性不同水稻品种对盐胁迫的响应差异及其机制[J]. 作物学报, 2022, 48(6): 1463-1475.
[6] 杨建昌, 李超卿, 江贻. 稻米氨基酸含量和组分及其调控[J]. 作物学报, 2022, 48(5): 1037-1050.
[7] 杨德卫, 王勋, 郑星星, 项信权, 崔海涛, 李生平, 唐定中. OsSAMS1在水稻稻瘟病抗性中的功能研究[J]. 作物学报, 2022, 48(5): 1119-1128.
[8] 朱峥, 王田幸子, 陈悦, 刘玉晴, 燕高伟, 徐珊, 马金姣, 窦世娟, 李莉云, 刘国振. 水稻转录因子WRKY68在Xa21介导的抗白叶枯病反应中发挥正调控作用[J]. 作物学报, 2022, 48(5): 1129-1140.
[9] 王小雷, 李炜星, 欧阳林娟, 徐杰, 陈小荣, 边建民, 胡丽芳, 彭小松, 贺晓鹏, 傅军如, 周大虎, 贺浩华, 孙晓棠, 朱昌兰. 基于染色体片段置换系群体检测水稻株型性状QTL[J]. 作物学报, 2022, 48(5): 1141-1151.
[10] 肖健, 陈思宇, 孙妍, 杨尚东, 谭宏伟. 不同施肥水平下甘蔗植株根系内生细菌群落结构特征[J]. 作物学报, 2022, 48(5): 1222-1234.
[11] 王泽, 周钦阳, 刘聪, 穆悦, 郭威, 丁艳锋, 二宫正士. 基于无人机和地面图像的田间水稻冠层参数估测与评价[J]. 作物学报, 2022, 48(5): 1248-1261.
[12] 陈悦, 孙明哲, 贾博为, 冷月, 孙晓丽. 水稻AP2/ERF转录因子参与逆境胁迫应答的分子机制研究进展[J]. 作物学报, 2022, 48(4): 781-790.
[13] 王吕, 崔月贞, 吴玉红, 郝兴顺, 张春辉, 王俊义, 刘怡欣, 李小刚, 秦宇航. 绿肥稻秆协同还田下氮肥减量的增产和培肥短期效应[J]. 作物学报, 2022, 48(4): 952-961.
[14] 巫燕飞, 胡琴, 周棋, 杜雪竹, 盛锋. 水稻延伸因子复合体家族基因鉴定及非生物胁迫诱导表达模式分析[J]. 作物学报, 2022, 48(3): 644-655.
[15] 陈云, 李思宇, 朱安, 刘昆, 张亚军, 张耗, 顾骏飞, 张伟杨, 刘立军, 杨建昌. 播种量和穗肥施氮量对优质食味直播水稻产量和品质的影响[J]. 作物学报, 2022, 48(3): 656-666.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!