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Genetic Analysis Of Maize Rough Dwarf Virus Resistance

Posted on:2011-04-05Degree:DoctorType:Dissertation
Country:ChinaCandidate:L Y ShiFull Text:PDF
GTID:1113330338961257Subject:Biochemistry and Molecular Biology
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Maize Rough Dwarf Disease (MRDD) caused by maize rough dwarf virus (MRDV) is a worldwide disease. It is one of the most serious diseases in maize-growing area of China. Developing and cultivating resistant hybrids is an effective approach to control MRDD. However, a few resistance germplasm are identified so far. In this study, identification of resistant materials, development of molecular markers and QTL analysis were investigated. The results as follows:1. 200 maize inbred lines for MRDD resistance was evaluated by inoculating small brown plantthopper into net-boxes and the inbred lines of Shen137, Duohuang29, Jinhuang96B, Zhongzi01, Hai9-21, P138, CA339, Qi319, Dan3130,9046,835, Huangye4, Qi318, R18, SH15, CA335, X178, Liao68, Jinhuang59, Zixuanxi were identified.2. Resistant (Shen137, Duohuang29, Zhongzi01, P138, CA339, Qi319, Dan3130, Qi318, CA335, X178) and susceptible(Ye107, Ye478, Ji4112,803, K22, Luyuan92, Ye3189, Liao5114, U8112, B73) DNA bulks were composed by using genomic DNAs of 10 resistant and 10 susceptible inbred lines, respectively. Polymorphic AFLP markers were screened between two bulks and then transformed into SCAR (sequence characterized amplified region) markers. These SCAR markers associated with MRDD resistance was analyzed with disease incidence of 152 inbred lines. Results showed that SCAR69 and SCAR74 were validated to be highly associated with MRDD and could be used for MAS of MRDD resistance in maize.3. Two recombined inbred line populations (RIL) derived from Ye 107×Huangzao4, X178×B73, were evalutated for MRDV resistance in replicated field trials in different sites. Three resistance evaluating index (ID, SI, and CSI) were used for QTL analysis. Linkage maps were constructed using SSRs, SNPs and SCARs, and resistant QTL analyzed using MIM in MapQTL software. Results showed that in X178×B73 RIL population, a major resistance QTL was detected on chromosome bin 8.03, accounting for about 28% of the phenotypic variance. In Huangzao4×Ye 107 RIL population,7 resistant QTLs were detected on chromosome bin 3.04 (SNP610-SNP1438), bin 4.03 (SNP1287-SNP581), bin 6.05, bin7.02/03 (SNP637-SNP686), bin 8.06 (SNP619-SNP68). These QTLs accounted for lower than 10% of the phenotypic variance.4. Translation initiation factors, particularly the eukaryotic initiation factor 4E (eIF4E), were found to be essential determinants of the outcome of plant infections by RNA viruses. In this study, we isolated an eIF4E orthologue and analyzed its expression patterns in two maize inbred lines X178 and Ye478. ZmeIF4E contains five exons encoding a protein with 218 amino acid residues. Quantitative RT-PCR showed that the ZmeIF4E gene was regulated in response to three plant hormones, namely ethylene, salicylic acid and jasmonates and its gene expression varied widely in two inbred lines. Furthermore, sequence analysis of the ZmeIF4E promoter region showed that base substitutions and insertion/deletion polymorphisms were present in four cis-acting elements, including DOFCOREZM, EECCRCAH1, GT1GAMSCAM4, and GT1CONSENSUS. These cis-acting elements may be responsible for diverse gene-expression patterns in two different inbred lines. Association analysis revealed that one SNP polymorphism in EECCRCAH1 was significantly associated with maize rough dwarf virus disease index obtained in 2007 and 2008 in Hebei, China. In addition, one SNP polymorphism in the GT-1 motif was found to affect MRDV resistance in 2007 in Hebei, China. We found that these two SNP polymorphism sites have complementary or synergistic effects on ZmeIF4E gene expression. Collectively, these results imply that these two regulatory motifs in the ZmeIF4E promoter are involved in MRDV resistance, indicating that eIF4E activity plays vital roles in pathogen infections and control of recessive resistance.
Keywords/Search Tags:Maize rough dwarf disease, Marker assisted selection, QTLanalysis, Candidate genes, Functional analysis
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