欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2024, Vol. 50 ›› Issue (2): 373-382.doi: 10.3724/SP.J.1006.2024.33021

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于多维组学数据的玉米农艺和品质性状预测研究

杨静蕾1,**(), 吴冰杰1,**(), 王安洲1, 肖英杰1,2,*()   

  1. 1作物遗传改良全国重点实验室 / 华中农业大学, 湖北武汉 430070
    2湖北洪山实验室, 湖北武汉 430070
  • 收稿日期:2023-04-02 接受日期:2023-09-13 出版日期:2024-02-12 网络出版日期:2023-10-09
  • 通讯作者: *肖英杰, E-mail: yxiao25@mail.hzau.edu.cn
  • 作者简介:杨静蕾, E-mail: yangjl@webmail.hzau.edu.cn;吴冰杰, E-mail: wubingjie@webmail.hzau.edu.cn
    **同等贡献
  • 基金资助:
    国家自然科学基金优秀青年科学基金项目(32122066)

Genomic prediction of maize agronomic and quality traits using multi-omics data

YANG Jing-Lei1,**(), WU Bing-Jie1,**(), WANG An-Zhou1, XIAO Ying-Jie1,2,*()   

  1. 1National Key Laboratory of Crop Genetic Improvement / Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2Hubei Hongshan Laboratory, Wuhan 430070, Hubei, China
  • Received:2023-04-02 Accepted:2023-09-13 Published:2024-02-12 Published online:2023-10-09
  • Contact: *E-mail: yxiao25@mail.hzau.edu.cn
  • About author:**Contributed equally to this work
  • Supported by:
    National Natural Science Foundation of China for Youth-Talent Fund(32122066)

RichHTML

15

PDF

511

摘要：

基因组选择是利用覆盖基因组的高密度标记对未知表型进行预测并选择的技术。在植物中, 利用该技术可对不同作物性状进行早期选择, 保留优势个体, 节约田间管理和表型鉴定成本, 大大加快育种进程。本研究使用rrBLUP和LASSO两种统计模型, 基于基因组、转录组和代谢组数据对玉米的农艺性状和品质性状进行了基因组预测。研究发现, 对于不同组学数据而言, 其预测能力高低依次为基因组、转录组、代谢组数据。对于不同性状而言, 品质性状的预测能力高于农艺性状。对于rrBLUP和LASSO两种模型而言, 基于基因组数据预测时所有性状均是rrBLUP为最优预测模型; 基于转录组数据预测时有53种性状是以rrBLUP为最佳预测模型, 2种性状以LASSO为最佳预测模型; 基于代谢组数据, 有43种性状以rrBLUP为最佳预测模型, 12种性状以LASSO为最佳预测模型。此外, 还发现用不同系谱材料进行预测时, 热带玉米预测温带玉米, 其效果略优于温带玉米预测热带玉米。而对于品质性状, 不同系谱间材料的预测精度高于同一系谱内。本研究系统评估了各种组学数据和不同统计模型对玉米农艺及品质性状预测能力的差异, 为未来玉米重要性状的基因组育种提供了理论依据。

关键词: 玉米, 农艺和品质性状, 基因组预测, 多维组学数据

Abstract:

Genomic selection predicts unknown phenotypes by using high-density genetic markers covering the genome. In the plant, this method allows early selection for traits, retaining dominant individuals and saving costs for field management and phenotype identification, which greatly accelerating the breeding process. In this study, genomic, transcriptomic, and metabolomic data were used for genomic prediction of agronomic and quality traits of maize by using two statistical models, rrBLUP, and LASSO. We found that the order of predictive power was genomic data, transcriptomic data, and metabolomic data. For different traits, genomic prediction was more powerful than agronomic traits for quality traits. For both rrBLUP and LASSO models, rrBLUP was the best model for all traits when using genomic data, 53 traits were the best predicted by rrBLUP and 2 traits were the best predicted by LASSO when using transcriptomic data, 43 traits were the best predicted by rrBLUP and 12 traits were the best predicted by LASSO, and 12 traits were the best predicted by LASSO based on metabolomic data. In addition, when performing genomic prediction using different lineages, the accuracy of predicting the temperate maize from the tropic maize was slightly better than that of predicting the tropic maize from the temperate. For quality traits, we found the cross-lineage prediction was higher than the within-lineage prediction. This study systematically evaluated the differences in the predictive ability of maize agronomic and quality traits based on various multi-omics data and statistical models, which providing a theoretical basis for future genomic breeding of important agricultural traits in maize.

Key words: maize, agronomic and quality trait, genomic prediction, multi-omics data

图1

基于基因组数据分析对农艺性状和品质性状的预测差异 A: 基于基因组数据预测2种表型性状的预测精度密度分布图; B: 基于基因组数据预测20个农艺性状和35个品质性状的预测精度。"

图2

不同组学数据对性状预测的差异 A: 对比不同组学数据预测不同类型性状的预测差异; B: 不同组学数据预测55个性状的预测结果分类; C: 应用不同染色体标记预测2种性状的预测差异; D: 利用3种组织数据预测2种性状的预测差异; E: 整合不同环境的代谢物数据预测2种性状的预测差异。"

图3

不同模型和数据组合对性状预测的整合评估 A: 基于不同组学数据55个性状的最佳预测模型; B: 基于不同组学数据55个性状在2个预测模型间预测精度变化; C: 55个性状的最佳预测数据和模型组合。"

图4

材料系谱差异对不同组学预测的影响 A, B: 基于不同组学数据对比材料系谱差异对预测表型结果的影响; C~E: 基于不同组学数据对比材料异质性对2种类型性状预测的影响。"

[1] He T H, Li C D. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. Crop J, 2020, 8: 688-700.
doi: 10.1016/j.cj.2020.04.005
[2] Steinwand M A, Ronald P C. Crop biotechnology and the future of food. Nat Food, 2020, 1: 273-283.
doi: 10.1038/s43016-020-0072-3
[3] Hickey L T, Hafeez A N, Robinson H, Jackson S A, Leal-Bertioli S C M, Tester M, Gao C X, Godwin I D, Hayes Ben J, Wulff B B H. Breeding crops to feed 10 billion. Nat Biotechnol, 2019, 37: 744-754.
doi: 10.1038/s41587-019-0152-9 pmid: 31209375
[4] Borlaug N E. Contributions of conventional plant breeding to food production. Science, 1983, 219: 689-693.
doi: 10.1126/science.219.4585.689 pmid: 17814030
[5] Watson A, Ghosh S, Williams M J, Cubby W S, Simmonds J, Rey M D, Asyraf Md Hatta M, Hinchliffe A, Steed A, Reynolds D, Adamski N M, Breakspear A, Korolev A, Rayner T, Dixon L E, Riaz A, Martin W, Ryan M, Edwards D, Batley J, Raman H, Carter J, Rogers C, Domoney C, Moore G, Harwood W, Nicholson P, Dieters M J, DeLacy I H, Zhou J, Uauy C, Boden S A, Park R F, Wulff B B H, Hickey L T. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants, 2018, 4: 23-29.
doi: 10.1038/s41477-017-0083-8 pmid: 29292376
[6] Meuwissen T H E, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157: 1819-1829.
doi: 10.1093/genetics/157.4.1819 pmid: 11290733
[7] Nakaya A, Isobe S N. Will genomic selection be a practical method for plant breeding? Ann Bot, 2012, 110: 1303-1316.
doi: 10.1093/aob/mcs109
[8] Farah M M, Swan A A, Fortes M R S, Fonseca R, Moore S S, Kelly M J. Accuracy of genomic selection for age at puberty in a multi-breed population of tropically adapted beef cattle. Anim Genet, 2016, 47: 3-11.
doi: 10.1111/age.12362 pmid: 26490440
[9] Kariuki C M, Brascamp E W, Komen H, Van Arendonk J A M. Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries. J Dairy Sci, 2017, 100: 2258-2268.
doi: S0022-0302(17)30052-8 pmid: 28109609
[10] Schaeffer L R. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 2006, 123: 218-223.
doi: 10.1111/j.1439-0388.2006.00595.x pmid: 16882088
[11] Lorenzana R E, Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet, 2009, 120: 151-161.
doi: 10.1007/s00122-009-1166-3 pmid: 19841887
[12] Zhang X C, Perez-Rodriguez P, Burgueno J, Olsen M, Buckler E, Atlin G, Prasanna B M, Vargas M, San Vicente F, Crossa J. Rapid cycling genomic selection in a multi-parental tropical maize population. G3: Gene Genome Genet, 2017, 7: 2315-2326.
doi: 10.1534/g3.117.043141
[13] Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity, 2017, 118: 302-310.
doi: 10.1038/hdy.2016.87 pmid: 27649618
[14] Ma Y S, Liu Z X, Wen Z X, Wei S H, Yang C M, Wang H C, Yang C Y, Lu W G, Xu R, Zhang W H, Wu J A, Hu G H, Luan X Y, Fu Y S, Wang S M, Han T F, Zhang M C, Zhang L, Yuan B, Guo Y, Reif J C, Jiang Y, Li W B, Wang D C, Qiu L J. Effect of population structure on prediction accuracy of soybean 100-seed weight by genomic selection. Crop J, 2018, 44: 43-52 (in Chinese with English abstract).
[15] Charmet G, Storlie E, Oury F X, Laurent V, Beghin D, Chevarin L, Lapierre A, Perretant M R, Rolland B, Heumez E, Duchalais L, Goudemand E, Bordes J, Robert O. Genome-wide prediction of three important traits in bread wheat. Mol Breed, 2014, 34: 1843-1852.
doi: 10.1007/s11032-014-0143-y
[16] Beyene Y, Semagn K, Mugo S, Tarekegne A, Babu R, Meisel B, Sehabiague P, Makumbi D, Magorokosho C, Oikeh S, Gakunga J, Vargas M, Olsen M, Prasanna B M, Banziger M, Crossa J. Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci, 2015, 55: 154-163.
doi: 10.2135/cropsci2014.07.0460
[17] Zhang X, Perez-Rodriguez P, Semagn K, Beyene Y, Babu R, Lopez-Cruz M A, Vicente F S, Olsen M, Buckler E, Jannink J L, Prasanna B M, Crossa J. Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity, 2015, 114: 291-299.
doi: 10.1038/hdy.2014.99 pmid: 25407079
[18] Cao S, Loladze A, Yuan Y B, Wu Y S, Zhang A, Chen J F, Huestis G, Cao J S, Chaikam V, Olsen M, Prasanna B M, San Vicente F, Zhang X C. Genome-wide analysis of Tar Spot Complex resistance in maize using genotyping-by-sequencing SNPs and whole-genome prediction. Plant Genome, 2017, 10: 1-14.
[19] Zhang A, Wang H W, Beyene Y, Semagn K, Liu Y, B Cao S L, Cui Z H, Ruan Y Y, Burgueno J, San Vicente F, Olsen M, Prasanna B M, Crossa J, Yu H Q, Zhang X C. Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Front Plant Sci, 2017, 8: 1916.
doi: 10.3389/fpls.2017.01916 pmid: 29167677
[20] Guo Z G, Tucker D M, Lu J W, Kishore V, Gay G. Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet, 2012, 124: 261-275.
doi: 10.1007/s00122-011-1702-9 pmid: 21938474
[21] Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger A E. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet, 2012, 44: 217-220.
doi: 10.1038/ng.1033 pmid: 22246502
[22] Shikha M, Kanika A, Rao A R, Mallikarjuna M G, Gupta H S, Nepolean T. Genomic selection for drought tolerance using genome-wide SNPs in maize. Front Plant Sci, 2017, 8: 550.
doi: 10.3389/fpls.2017.00550 pmid: 28484471
[23] Crossa J, Perez-Rodriguez P, Cuevas J, Montesinos-Lopez O, Jarquín D, de los Campos G, Burgueno J, Camacho-Gonzalez J M, Perez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X C, Gowda M, Roorkiwal M, Rutkoski J, Varshney R K. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci, 2017, 22: 961-975.
doi: S1360-1385(17)30184-X pmid: 28965742
[24] Azodi C B, Pardo J, VanBuren R, De Los Campos G, Shiu S H. Transcriptome-based prediction of complex traits in maize. Plant Cell, 2020, 32: 139-151.
doi: 10.1105/tpc.19.00332
[25] Bhering L L, Junqueira V S, Peixoto L A, Cruz C D, Laviola B G. Comparison of methods used to identify superior individuals in genomic selection in plant breeding. Genet Mol Res, 2015, 14: 10888-10896.
doi: 10.4238/2015.September.9.26 pmid: 26400316
[26] Yan H L, Guo H Y, Xu W X, Dai C H, Kimani W, Xie J Y, Zhang H F, Li T, Wang F, Yu Y J, Ma M, Hao Z F, He Z Y. GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods. J Hazard Mater, 2023, 441: 129929.
doi: 10.1016/j.jhazmat.2022.129929
[27] Tibshirani R. Regression shrinkage and selection via the lasso. J Roy Stat Soc B, 1996, 58: 267-288.
[28] Ahmad I, Singh A, Fahad M, Waqas M M. Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Comput Electron Agric, 2020, 178: 105732-105732.
doi: 10.1016/j.compag.2020.105732
[29] Islam M S, Fang D D, Jenkins J N, Guo J, McCarty J C, Jones D C. Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton. Mol Genet Genom, 2020, 295: 67-79.
doi: 10.1007/s00438-019-01599-z
[30] Tsai H Y, Janss L L, Andersen J R, Orabi J, Jensen J D, Jahoor A, Jensen J. Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat. Sci Rep, 2020, 10: 1-15.
doi: 10.1038/s41598-019-56847-4
[31] Zhang F, Wu J F, Sade N, Wu S, Egbaria A, Fernie A R, Yan J B, Qin F, Wei C, Brotman Y, Dai M Q. Genomic basis underlying the metabolome-mediated drought adaptation of maize. Genome Biol, 2021, 22: 260-260.
doi: 10.1186/s13059-021-02481-1 pmid: 34488839
[32] Qin S, Xu Y, Nie Z, Liu H, Gao W, Li C, Zhao P. Metabolomic and antioxidant enzyme activity changes in response to cadmium stress under boron application of wheat (Triticum aestivum). Environ Sci Pollut Res Int, 2022, 29: 34701-34713.
doi: 10.1007/s11356-021-17123-z
[33] Hu X H, Xie W B, Wu C C, Xu S Z. A directed learning strategy integrating multiple omic data improves genomic prediction. Plant Biotechnol J, 2019, 17: 2011-2020.
doi: 10.1111/pbi.13117 pmid: 30950198
[34] Li H, Peng Z Y, Yang X H, Wang W D, Fu J J, Wang J H, Han Y J, Chai Y C, Guo T T, Yang N, Liu J, Warburton M L, Cheng Y B, Hao X M, Zhang P, Zhao J Y, Liu Y J, Wang G Y, Li J S, Yan J B. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet, 2013, 45: 43-50.
doi: 10.1038/ng.2484 pmid: 23242369
[35] Yang N, Lu Y L, Yang X H, Huang J, Zhou Y, Ali F, Wen W W, Liu J, Li J S, Yan J B. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet, 2014, 10: e1004573.
doi: 10.1371/journal.pgen.1004573
[36] Wang H, Xu S T, Fan Y M, Liu N N, Zhan W, Liu H J, Xiao Y J, Li K, Pan Q C, Li W Q, Deng M, Liu J, Jin M, Yang X H, Li J S, Li Q, Yan J B. Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnol J, 2018, 16: 1464-1475.
doi: 10.1111/pbi.12889 pmid: 29356296
[37] Deng M, Li D Q, Luo J Y, Xiao Y J, Liu H J, Pan Q C, Zhang X H, Jin M L, Zhao M C, Yan J B. The genetic architecture of amino acids dissection by association and linkage analysis in maize. Plant Biotechnol J, 2017, 15: 1250-1263.
doi: 10.1111/pbi.12712 pmid: 28218981
[38] Wen W W, Li D, Li X, Gao Y Q, Li W Q, Li H H, Liu J, Liu H J, Chen W, Luo J, Yan J B. Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun, 2014, 5: 1-10.
[39] Endelman J B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome, 2011, 4: 250-255.
doi: 10.3835/plantgenome2011.08.0024
[40] Zheng L W, Ma S J, Shen D D, Fu H, Wang Y, Liu Y, Shah K, Yue C P, Huang J Y. Genome-wide identification of Gramineae histone modification genes and their potential roles in regulating wheat and maize growth and stress responses. BMC Plant Biol, 2021, 21: 543.
doi: 10.1186/s12870-021-03332-8 pmid: 34800975
[41] De Los Campos G, Hickey J M, Pong-Wong R, Daetwyler H D, Calus M P. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics, 2013, 193: 327-345.
doi: 10.1534/genetics.112.143313 pmid: 22745228
[1] 赵荣荣, 丛楠, 赵闯. 基于Landsat 8影像提取豫中地区冬小麦和夏玉米分布信息的最佳时相选择[J]. 作物学报, 2024, 50(3): 721-733.
[2] 梁星伟, 杨文亭, 金雨, 胡莉, 傅小香, 陈先敏, 周顺利, 申思, 梁效贵. 玉米穗轴的颜色变化，是偶然还是与农艺性状存在关联？ ——以历年国审普通品种为例 [J]. 作物学报, 2024, 50(3): 771-778.
[3] 薛明, 汪晨晨, 姜露光, 刘浩, 张路遥, 陈赛华. 玉米花序发育基因AFP1的定位及功能研究[J]. 作物学报, 2024, 50(3): 603-612.
[4] 毛燕, 郑名敏, 牟成香, 谢吴兵, 唐琦. 渗透胁迫下玉米自然反义转录本cis-NATZmNAC48启动子的功能分析[J]. 作物学报, 2024, 50(2): 354-362.
[5] 马娟, 曹言勇. 玉米杂交群体产量性状及其特殊配合力全基因组关联分析[J]. 作物学报, 2024, 50(2): 363-372.
[6] 杨晨曦, 周文期, 周香艳, 刘忠祥, 周玉乾, 刘芥杉, 杨彦忠, 何海军, 王晓娟, 连晓荣, 李永生. 控制玉米株高基因PHR1的基因克隆[J]. 作物学报, 2024, 50(1): 55-66.
[7] 岳润清, 李文兰, 孟昭东. 转基因抗虫耐除草剂玉米自交系LG11的获得及抗性分析[J]. 作物学报, 2024, 50(1): 89-99.
[8] 宋旭东, 朱广龙, 张舒钰, 章慧敏, 周广飞, 张振良, 冒宇翔, 陆虎华, 陈国清, 石明亮, 薛林, 周桂生, 郝德荣. 长江中下游地区糯玉米花期耐热性鉴定及评价指标筛选[J]. 作物学报, 2024, 50(1): 172-186.
[9] 杨立达, 任俊波, 彭新月, 杨雪丽, 罗凯, 陈平, 袁晓婷, 蒲甜, 雍太文, 杨文钰. 施氮与种间距离下大豆/玉米带状套作作物生长特性及其对产量形成的影响[J]. 作物学报, 2024, 50(1): 251-264.
[10] 王丽平, 王晓钰, 傅竞也, 王强. 玉米转录因子ZmMYB12提高植物抗旱性和低磷耐受性的功能鉴定[J]. 作物学报, 2024, 50(1): 76-88.
[11] 艾蓉, 张春, 悦曼芳, 邹华文, 吴忠义. 玉米转录因子ZmEREB211对非生物逆境胁迫的应答[J]. 作物学报, 2023, 49(9): 2433-2445.
[12] 黄钰杰, 张啸天, 陈会丽, 王宏伟, 丁双成. 玉米ZmC2s基因家族鉴定及ZmC2-15耐热功能分析[J]. 作物学报, 2023, 49(9): 2331-2343.
[13] 杨文宇, 吴成秀, 肖英杰, 严建兵. 基于Adaptive Lasso的两阶段全基因组关联分析方法[J]. 作物学报, 2023, 49(9): 2321-2330.
[14] 白岩, 高婷婷, 卢实, 郑淑波, 路明. 近四十年来我国玉米大品种的历史沿革与发展趋势[J]. 作物学报, 2023, 49(8): 2064-2076.
[15] 王兴荣, 张彦军, 涂奇奇, 龚佃明, 邱法展. 一个新的玉米细胞核雄性不育突变体ms6的鉴定与基因定位[J]. 作物学报, 2023, 49(8): 2077-2087.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 李绍清, 李阳生, 吴福顺, 廖江林, 李达模. 水稻孕穗期在淹涝胁迫下施肥的优化选择及其作用机理[J]. 作物学报, 2002, 28(01): 115 -120 .
[2] 王兰珍;米国华;陈范骏;张福锁. 不同产量结构小麦品种对缺磷反应的分析[J]. 作物学报, 2003, 29(06): 867 -870 .
[3] 杨建昌;张亚洁;张建华;王志琴;朱庆森. 水分胁迫下水稻剑叶中多胺含量的变化及其与抗旱性的关系[J]. 作物学报, 2004, 30(11): 1069 -1075 .
[4] 袁美;杨光圣;傅廷栋;严红艳. 甘蓝型油菜生态型细胞质雄性不育两用系的研究Ⅲ. 8-8112AB的温度敏感性及其遗传[J]. 作物学报, 2003, 29(03): 330 -335 .
[5] 王永胜;王景;段静雅;王金发;刘良式. 水稻极度分蘖突变体的分离和遗传学初步研究[J]. 作物学报, 2002, 28(02): 235 -239 .
[6] 王丽燕;赵可夫. 玉米幼苗对盐胁迫的生理响应[J]. 作物学报, 2005, 31(02): 264 -268 .
[7] 田孟良;黄玉碧;谭功燮;刘永建;荣廷昭. 西南糯玉米地方品种waxy基因序列多态性分析[J]. 作物学报, 2008, 34(05): 729 -736 .
[8] 胡希远;李建平;宋喜芳. 空间统计分析在作物育种品系选择中的效果[J]. 作物学报, 2008, 34(03): 412 -417 .
[9] 王艳;邱立明;谢文娟;黄薇;叶锋;张富春;马纪. 昆虫抗冻蛋白基因转化烟草的抗寒性[J]. 作物学报, 2008, 34(03): 397 -402 .
[10] 郑希;吴建国;楼向阳;徐海明;石春海. 不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析[J]. 作物学报, 2008, 34(03): 369 -375 .
京ICP备15008789号-2