基于元分析和生物信息学分析的玉米抗旱相关性状QTL一致性区间定位

doi:10.3724/SP.J.1006.2010.01457

作物学报 ›› 2010, Vol. 36 ›› Issue (09): 1457-1467.doi: 10.3724/SP.J.1006.2010.01457

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于元分析和生物信息学分析的玉米抗旱相关性状QTL一致性区间定位

栗文娟^1,2,刘志斋^2,3,石云素²,宋燕春²,王天宇^2,*,徐辰武^1,*,黎裕²

¹扬州大学江苏省作物遗传生理重点实验室，江苏扬州225009；²中国农业科学院作物科学研究所，北京 100081；³西南大学农学院，重庆400716

收稿日期:2010-01-15 修回日期:2010-04-20 出版日期:2010-09-12 网络出版日期:2010-07-05
通讯作者: 王天宇, E-mail: wangtianyu@263.net; 徐辰武, E-mail: qtls@yzu.edu.cn
基金资助:
本研究由国家重点基础研究发展计划(973计划)项目(2006CB101700), 国家高技术研究发展计划(863计划)项目(2006AA10Z188)和国家自然科学基金项目(30730063, 30971846)资助。

Detection of Consensus Genomic Region of QTLs Relevant to Drought-Tolerance in Maize by QTL Meta-Analysis and Bioinformatics Approach

LI Wen-Juan^1,2,LIU Zhi-Zhai^2,3,SHI Yun-Su²,SONG Yan-Chun²,WANG Tian-Yu^2*,XU Chen-Wu^1*,LI Yu²

¹ Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Yangzhou University, Yangzhou 225009, China; ²Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; ³Southwest University, Chongqing 400716, China

Received:2010-01-15 Revised:2010-04-20 Published:2010-09-12 Published online:2010-07-05
Contact: WANG Tian-Yu,E-mail:wangtianyu@263.net; 徐辰武, E-mail: qtls@yzu.edu.cn

摘要/Abstract

摘要： 定位玉米基因组中一致的抗旱性区段是玉米抗旱分子育种的重要基础。本研究对至今发表的在干旱条件下定位的相关性状QTL信息搜集整理，以IBM2 2008 Neighbors为参考图谱，利用overview分析和元分析方法进行Meta-QTL (MQTL)检测，共发掘79个MQTL，生物信息学分析结果显示，有43个区间内包含抗旱相关基因信息，占检出MQTL总数的54.43%。基于MaizeGDB网站的Genome Browser中的遗传图谱与物理图谱的整合信息，进行MQTL物理距离的估算，根据maizesequence网站的玉米基因组序列信息，进行初步的抗旱基因预测表明，这些区段中包含丰富的MYB、bZIP以及DREB转录因子序列信息以及大量的LEA基因家族成员。

关键词: 玉米, 抗旱性, “一致性”QTL, 元分析, 生物信息学方法

Abstract: Mapping consensus genomic regions for drought-tolerance is of great importance in the molecular breeding of maize. The present research integrated informations of the published QTLs relevant to drought-tolerance mapped in the environment of water stress. On the basis of the high-density linkage map of IBM2 2008 Neighbors, a total of 79 Meta-QTLs (MQTLs) were screened out through the methods of “overview” and meta-analysis, and the bioinformatic analysis indicated that 43 of these MQTLs (54.43%) contained the information of genes conferring drought tolerance. By integrating the genetic map and the physical map of maize via Genome Brower in maize genome database (http://www.maizegdb.org/), we estimated the physical map distance of MQTLs and analyzed the function of these candidate drought tolerance-related genomic regions based on the maize genome sequence information from the maize sequence database (http://www.maizesequence.org/). The results showed that these regions contained abundant sequences of transcription factors of MYB, bZIP and DREB, and a number of functional genes of LEA family.

Key words: Maize(Zea mays L.), Drought tolerance, Meta-QTL, Meta-analysis, Bioinformatics approach

栗文娟,刘志斋,石云素,宋燕春,王天宇,徐辰武,黎裕. 基于元分析和生物信息学分析的玉米抗旱相关性状QTL一致性区间定位[J]. 作物学报, 2010, 36(09): 1457-1467.

LI Wen-Juan, LIU Zhi-Zhai, SHI Yun-Su, SONG Yan-Chun, WANG Tian-Yu, XU Chen-Wu, LI Yu. Detection of Consensus Genomic Region of QTLs Relevant to Drought-Tolerance in Maize by QTL Meta-Analysis and Bioinformatics Approach[J]. Acta Agron Sin, 2010, 36(09): 1457-1467.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://zwxb.chinacrops.org/CN/10.3724/SP.J.1006.2010.01457

https://zwxb.chinacrops.org/CN/Y2010/V36/I09/1457

参考文献

[1] Li Y(黎裕), Wang T-Y(王天宇), Shi Y-S(石云素), Song Y-C(宋艳春). Advances and prospects on QTL analysis of drought tolerance of maize (Zea mays L.). Agric Res Arid Areas (干旱地区农业研究), 2004, 22(1): 32-39 (in Chinese with English abstract)
[2] Li X-H(李雪华), Li X-H(李新海), Hao Z-F(郝转芳), Tian Q-Z(田清震), Zhang S-H(张世煌). Consensus map of the QTL relevant to drought tolerance of maize under drought conditions. Sci Agric Sin (中国农业科学), 2005, 38(5): 882-890 (in Chinese with English abstract)
[3] Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics, 2004, 168: 2169-2185
[4] Hanocq E, Laperche A, Jaminon O, Laine AL, Le Gouis J. Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis. Theor Appl Genet, 2007, 114: 569-584
[5] Ji H-L(吉海莲), Li X-H(李新海), Xie C-X(谢传晓), Hao Z-F(郝转芳), Lü X-L(吕香玲), Shi L-Y(史利玉), Zhang S-H(张世煌). Comparative QTL mapping of resistance to sporisorium reiliana in maize based on meta-analysis of QTL locations. J Plant Genet Resour (植物遗传资源学报), 2007, 8(2): 132-139 (in Chinese with English abstract)
[6] Wang Y(王毅), Yao J(姚骥), Zhang Z-F(张征锋), Zheng Y-L(郑用琏). Comparative analysis of QTL integrated mapping and statistical analysis of QTLs affecting plant height in maize. Chin Sci Bull (科学通报), 2006, 51(15): 1776-1786 (in Chinese)
[7] Xu D P, Duan X L, Wang B Y, Hong B M, David Ho T H, Wu R. Expression of a late embryogenesis abundant protein gene, HVA1, from Barley confers tolerance to water deficit and salt stress in transgenic rice. Plant Physiol, 1996, 110: 249-257
[8] Liang Z(梁峥), Ma D-Q(马德钦), Tang L(汤岚), Hang Y-G(洪益国), Luo A-L(骆爱玲), Dai X-Y(戴秀玉). Expression of the Spinach Betaine Aldehyde Dehydrogenase (BADH) gene in transgenic tobacco plants. Chin J Biotech (生物工程学报), 1997, 13(3): 236-240 (in Chinese with English abstract)
[9] Schnable P S, Ware D, Fulton R S, Stein J C, Wei F S, Pasternak S, Liang C Z, Zhang J W, Fulton L, Graves T A, Minx P, Reily A D, Courtney,L, Kruchowski S S, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock S M, Belter E, Du F Y, Kim K, Abbott R M, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson S M, Gillam B, Chen W Z, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi K, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R F, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H R, Lee S, Lin J, ujmic Z, Kim W, Talag J, Zuccolo A, Fan C Z, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy M J, McMahan L, Buren P V, Vaughn M W, Ying K, Yeh C T, Emrich S J, Jia Y, Kalyanaraman A, Hsia A P, Barbazuk W B, Baucom R S, Brutnell T P, Carpita N C, Chaparro C, Chia J M, Deragon J M, Estill J C, Fu Yan, Jeddeloh J A, Han Y J, Lee H, Li P H, Lisch D R, Liu S Z, Liu Z J, Nagel D H, McCann M C, SanMiguel P, Myers A M, Nettleton D, Nguyen J, Penning B W, Ponnala L, Schneider K L, Schwartz D C, Sharma A, Soderlund C, Springer N M, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber T K, Yang LX, Yu Y, Zhang L F, Zhou S G, Zhu Q H, Bennetzen J L, Dawe R K, Jiang J M, Jiang N, Presting G G, Wessler S R, Aluru S, Martienssen R A, Clifton S W, McCombie W R, Wing R A, Wilson R K. The B73 maize genome: complexity, diversity, and dynamics. Science, 2009, 326: 1112-1115
[10] Darvasi A, Soller M. A simple method to calculate resolving power and confidence interval of QTL map location. Behav Genet, 1997, 27(2): 125-132
[11] Lee M, Beavis W D, Grant D, Katt M, Blair D, Hallauer A. Expanding the genetic map of maize with the intermated B73 × Mo17 (IBM) population. Plant Mol Biol, 2002. 48: 453-461
[12] Sharopova N, McMullen M D, Schultz L, Schroeder S, Sanchez-Villeda H, Gardiner J, Bergstrom D, Houchins K, Hancock S M, Musket T, Duru N, Polacco M, Edwards K, Ruff T, Register J C, Brouwer C, Thompson R, Velasco R, Chin E, Lee M, Woodman-Clikeman W, Long M J, Liscum E, Cone K, Davis G, Coe E H. Development and mapping of SSR markers for maize. Plant Mol Biol, 2002, 48: 463-481
[13] Veyrieras J B, Goffinet B, Charcosset A. MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinformatics, 2007, 8: 49
[14] Goffinet B, G.erber S. Quantitative Trait Loci: A Meta-analysis. Genetics, 2000, 155: 463-473
[15] Arcade A, Labourdette A, Falque M, Mangin B, Chardon F, Charcosset A, Joets J. BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics, 2004, 20: 2324-2326
[16] Sen T Z, Andorf C M, Schaeffer M L, Harper L C, Sparks M E, Duvick J, Brendel V P, Cannon E, Campbell D A, Lawrence C J. MaizeGDB becomes ‘sequence-centric’. Database, 2009: bap020
[17] Beavis WD, Smith OS, Grant D, Fincher R. Identification of quantitative trait loci using a small sample of topcrossed and F4 progeny from maize. Crop Sci, 1994, 34: 882-896
[18] Veldboom L R, Lee M, Woodman W L. Molecular marker-facilitated studies in an elite maize population: I. Linkage analysis and determination of QTL for morphological traits. Theor Appl Genet, 1994, 88: 7-16
[19] Ajmone-Marsan P, Monfredini G, Ludwig W F, Melchinger A E, Franceschini P, Pagnotto G, Motto M. In an elite cross of maize a major quantitative trait locus contols one-fourth of the genetic variation for grain yield. Theor Appl Genet, 1995, 90: 415-424
[20] Agrama H A S, Moussa M E. Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.). Euphytica, 1996, 91: 89-97
[21] Ajmone-Marsan P, Monfredini G, Brandolini A, Melchinger A E, Garay G, Motto M. Identification of QTL of grain yield in an elite hybrid of maize: repeatability of map position and effects in independent samples derived from the same population. Maydica, 1996, 41: 49-57
[22] Ribaut J M, Hoisington D A, Deutsch J A, Jiang C, Gonzalez-de-Leo D. Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interva. Theor Appl Genet, 1996, 92: 905-914
[23] Veldboom LR, Lee M. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. grain yield components. Crop Sci, 1996, 36: 1310-1319
[24] Ribaut J M, Jiang C, Gonzalez-de-Leon D, Edmeades G O, Hoisington D A. Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies. Theor Appl Genet, 1997, 94: 887-896
[25] Austin D, Lee M. Detection of quantitative trait loci for grain yield components in maize across generations in stress and nonstress environments. Crop Sci, 1998, 38: 1296-1308
[26] Khairallah M, Bohn M, Jiang C Z, Deutsh J A, Jewell D C, Mihm J A, Melchinger A E, Gonzalez de Leon D, Hoisington D. Molecular mapping of QTL for southwestern corn borer resistance, plant height and flowering in tropical maize. Plant Breed, 1998, 117: 309-318
[27] Frova C, Krajewski P, di Fonzo N, Villa M, Sari-Gorla M. Genetic analysis of drought tolerance in maize by molecular markers I. Yield components. Theor Appl Genet, 1999, 99: 280-288
[28] Sari-Gorla M, Krajewski P, di Fonzo N, Villa M, Frova C. Genetic analysis of drought tolerance in maize by molecular markers.II. Plant height and flowering. Theor Appl Genet, 1999, 99: 289-295
[29] Li X H, Liu X D, Li M S, Zhang S H. Identification of quantitative trait loci for anthesis-silking interval and yield components under drought stress in maize. Acta Bot Sin, 2003, 45(7): 852-857
[30] Gao S-B(高世斌), Feng Z-L(冯质雷), Li W-C(李晚忱), Rong T-Z(荣廷昭). Mapping QTLs for root and yield under drought stress in maize. Acta Agron Sin (作物学报), 2005, 31(6): 718-722 (in Chinese with English abstract)
[31] Gao S-B(高世斌), Zhao M-J(赵茂俊), Pan G-T(潘光堂), Li W-C(李晚忱), Rong T-Z(荣廷昭). Identification of QTLs controlling flowering parameters of maize under drought stress and non-stress environment. Southwest China J Agric Sci (西南农业学报), 2005, 18(5): 593-597 (in Chinese with English abstract)
[32] Lu G H, Tang J H, Yan J B, Ma X Q, Li J S, Chen S J, Ma J C, E L Z, Zhang Y R, Dai J R. Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol, 2006, 48(10): 1233-1243
[33] Wu J-W(吴建伟), Liu C(刘成), Shi Y-S(石云素), Song Y-C(宋燕春), Chi S-M(池书敏), Ma S-Y(马峙英), Wang T-Y(王天宇), Li Y(黎裕). QTL analysis of flowering related traits in maize under different water regions. J Maize Sci (玉米科学), 2008, 16(5): 61-65 (in Chinese with English abstract)
[34] Wei F S, Zhang J W, Zhou S G, He R F, Schaeffer M, Collura K, Kudrna D, Faga B P, Wissotski M, Rock S M, Graves T A, Fulton R S, Coe E, Schnable P S, Schwartz D C, Ware D, Clifton S W, Wilson R K, Wing R A. The physical and genetic framework of the maize B73 genome. PLos Genet, 2009, 5(11): e1000715

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于元分析和生物信息学分析的玉米抗旱相关性状QTL一致性区间定位

Detection of Consensus Genomic Region of QTLs Relevant to Drought-Tolerance in Maize by QTL Meta-Analysis and Bioinformatics Approach

PDF

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

关于本刊

在线期刊

作者中心

相关单位

联系我们

[1]	刘恩波, 陈静, 李红星, 于宁宁, 任佰朝, 赵斌, 刘鹏, 张吉旺. 遮阴改变源-库平衡和调节碳水化合物代谢进而抑制夏玉米幼穗发育[J]. 作物学报, 2026, 52(6): 1891-1901.
[2]	梁进宇, 尹嘉德, 王红丽, 张国平, 侯慧芝, 董博, 马明生. 基于无人机高光谱和机器学习的旱地饲用玉米叶片氮含量估测[J]. 作物学报, 2026, 52(6): 1788-1801.
[3]	孙淑凤, 许振南, 黄嘉鑫, 翁建峰, 李新海. 玉米MAPK家族全基因组鉴定及其对拟轮枝镰孢菌感染的响应[J]. 作物学报, 2026, 52(5): 1291-1308.
[4]	张宁宁, 滕雨菲, 任娜娜, 魏兴卓, 闫书豪, 樊可心, 王永宏, 陈文康, 张兴华, 朱万超, 徐淑兔, 薛吉全. 201份玉米自交系抗旱表型评价及可塑性分析[J]. 作物学报, 2026, 52(5): 1309-1325.
[5]	杨欣雨, 崔文涛, 迪力尼格尔·阿力木, 汪凯翔, 吴鹏昊, 任姣姣. 玉米穗上叶片数全基因组关联分析和全基因组选择[J]. 作物学报, 2026, 52(5): 1573-1590.
[6]	韩亚鑫, 何冠华, 张小琼, 张登峰, 李永祥, 刘旭洋, 王天宇, 黎裕, 邹华文, 李春辉. 基于RNA-Seq和BSA-Seq联合分析挖掘玉米侧根密度基因资源[J]. 作物学报, 2026, 52(5): 1341-1352.
[7]	张鸿蓉, 王菲儿, 李盼, 仇海龙, 朱静, 赵连豪, 南运有, 何蔚, 樊志龙, 胡发龙, 柴强, 殷文. 减量20%灌水与25%有机肥替代化肥提高青贮玉米产量的光合特性[J]. 作物学报, 2026, 52(5): 1487-1500.
[8]	杨扬, 常诗惠, 田红丽, 易红梅, 王璐, 任洁, 范亚明, 刘亚维, 王凤格, 赵久然. 不同生态区国审玉米品种的遗传多样性分析[J]. 作物学报, 2026, 52(5): 1352-1364.
[9]	蔡宏玮, 于爱忠, 姜科强, 王鹏飞, 王玉珑, 霍建喆, 庞小能, 尹波, 尚永盼. 干旱灌区有机肥替代部分化肥促进甜玉米产量提升的关键机制[J]. 作物学报, 2026, 52(4): 1166-1180.
[10]	田红丽, 杨扬, 范亚明, 易红梅, 郭丹丹, 王凤格, 赵久然. 适于玉米品种鉴定的一套三等位变异SNP新型标记组合[J]. 作物学报, 2026, 52(4): 993-1005.
[11]	杨亚莉, 徐明睿, 马越飞, 海艺蕊, 刘凯栋, 刘万茂, 孙颖. 玉米根尖及整根响应缺铁的转录组比较研究[J]. 作物学报, 2026, 52(4): 1006-1021.
[12]	张超, 郭欢, 李忠玲, 岳淑宁, 赵娜. 基于BSA-seq技术定位玉米籽粒花青素关联基因[J]. 作物学报, 2026, 52(3): 780-789.
[13]	郭向阳, 涂亮, 王栋, 刘鹏飞, 王安贵, 易强, 任洪, 李刚, 祝云芳, 吴迅, 蒋喻林, 田丰, 陈泽辉. 热带Suwan种质在我国玉米种质改良中的创新与利用[J]. 作物学报, 2026, 52(3): 655-664.
[14]	孟成, 王哲. 玉米ZmPFK基因家族全基因组鉴定及响应胁迫表达分析[J]. 作物学报, 2026, 52(3): 764-779.
[15]	李新浩, 邢梦柯, 周梓惠, 李思烨, 任昊, 王洪章, 赖华江. 外源褪黑素通过协调光反应与暗反应增强玉米苗期的耐热性[J]. 作物学报, 2026, 52(3): 839-856.