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Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (09): 1457-1467.doi: 10.3724/SP.J.1006.2010.01457

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS • Previous Articles     Next Articles

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

 LI Wen-Juan1,2,LIU Zhi-Zhai2,3,SHI Yun-Su2,SONG Yan-Chun2,WANG Tian-Yu2*,XU Chen-Wu1*,LI Yu2   

  1. 1 Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Yangzhou University, Yangzhou 225009, China; 2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 3Southwest University, Chongqing 400716, China
  • Received:2010-01-15 Revised:2010-04-20 Online:2010-09-12 Published:2010-07-05
  • Contact: WANG Tian-Yu,E-mail:wangtianyu@263.net; 徐辰武, E-mail: qtls@yzu.edu.cn E-mail:liwenjuan526@gmail.com

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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

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