| Drought is a single most important factor which limits maize production throughout the world. In the recent years, more and more prominent water crisis and frequent drought disasters have led the breeders to pay attention to drought resistance in maize breeding. The fast development of molecular marker technologies and statistical methods have provided convenience for the systematic analysis of typical quantitative trait of drought resistances. As a result, numerous QTLs in maize have been identified. Cloning technologies accelerated the analyses of drought-related genes in crops, and quite a few referred the functional genes or transcription factors related to drought resistance, such as LEA, MYB, DREB, and so on. But, up to now, few documents focused on the applying of MAS or gene cloning to improve drought resistance in maize. The main reasons might be the scattering distribution of drought resistance QTLs as well as fewer effective functional genes. The development of bioinformatics and the accomplishment of maize genome sequence project provided numerous information about functional genes, QTLs mapping and genome sequence. This provides a opportunity to mine the elite candidate genes or QTLs for drought resistance in maize. However, the problem is how to select efficient methods to construct the networks among QTLs and genome sequences.We collected the QTLs related to drought-resistance in maize. These QTLs controlled a total of 23 drought-related traits including yield, yield components, flowering-related traits and plant height, such as grain yield, kernel weight, ear number plant, ear length, ear diameter, cob diameter, kernel depth, kernel rows, dry matter content, ear number per 10 plants, kernel number per 10 plants, ear setting, ear weight, kernel weight per ear, kernel number per ear, kernel weight per 100 kernel, row length, kernel thickness, anthesis date, days to silk, anthesis-silking interval, ear height and plant height. Combined with the drought-resistant genes in maize, all these collected QTLs were integrated via meta-analysis to mine the candidate genes or chromosome regions related to drought-resistance in maize based on the bioinformatics technologies and maize genome sequence. The results were as follows:(1) Referred to the high density linkage map of IBM2 2008 Neighbors, we integrated more than 360 collected QTLs related to drought resistance in maize on a consensus map through the software package of MetaQTL. Results from the overview analysis indicated that all these QTLs distributed unevenly among 10 chromosomes of maize and in chromosomes of 1, 3, 4,6and 7, where the regions contained QTLs of drought-resistance with the peak height more than 3 lay. Detected by the MetaQTL, a total of 79 Meta-QTL (abbreviated MQTL) were identified. All these MQTLs possessed narrow confidence intervals, which agreed well with the results of overview analysis, and provided informative information for the further studies of these MQTLs.(2) Summary statistics of all the genes pertinent to drought-resistance contained in the linkage map of IBM2 2008 Neighbors showed that 69 out of these genes lay in or neighbored to the chromosome regions of MQTLs. Some of these MQTLs contained 3 to 6 drought-resistant genes, which suggested the candidate chromosome regions related to drought-resistance in maize.(3) Based upon the correlation of genetic linkage map and physical map via molecular markers, the chromosomal regions of MQTL were projected to the corresponding regions of physical map, through which all the protein sequences encoded by the genes in these regions were downloaded. Results from the systematic analysis of conserved domains revealed that many genes or transcription factors (TF) were contained in these chromosomal regions of MQTLs, including the TF families of MYB and bZIP, the TF of DREB, and many members of LEA gene family. All these results proved that the identified MQTLs related closely to the drought resistance in maize. The identified MQTLs could be further used to mine new drought-resistant genes or genome regions for the improvement of drought resistance in maize.
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