Dissecting Genetic Information Of Complex Traits And Their Applications In Crop Breeding

This study focus on the analysis of the genetic information about crop complex traits by using approaches of bioinformatics and biostatistics. In order to explicate the methods of bioinformatics and biostatistics on identifing genes and SNPs which relevant to crop complex traits, we used cotton genome data and maize genome data as examples to detaile these two methods.In the NBS resistance gene family identification and analysis in cotton, frsitly, the available whole whole genome sequence of the diploid cotton (Gossypium raimondii) were used to identify candidate NBS disease resistance genes of cotton, and then the characterization of functional domain and motifs, the distribution across the genome, the duplication events among them, and the phylogenetic relationship of them were analyzed. Futher more, using the transcriptome and EST data, as well as RT-PCR technology, the expression patterens of these genes was also investigated. In order to develop the resistance gene relative molecular markers, using these gene sequences as template, we also designed PCR primers for them.By analyzing cotton NBS resistance genes, we not only confirmed some features of NBS-encoding genes that already observed in other plant species, such as the independent evolutionary mode of TIR type resistance genes, the correlation between NBS resistance genes and disease resistance QTL, but also elucidated some features that in other plants have not been observed and may be specific for cotton, such as the polymorphism of the N-terminal, the high proportion of non-standard NBS resistant genes. The results reported in the present study should helpful for the cloning of cotton NBS resistant genes, as well as offer some valuable sights to understanding the cotton NBS-encoding gene family.In the QTS detection and analysis in maize, the corn genome-wide SNP markers and QTXNetwork software were used to detect the QTS that raltive to Cob length, Cob diameter and Ear row number. In this research, the results of the three phenotypic QTS Location and analysis which analyzed by a large number of SNP data with5populations and25different circumstances shows the effect of additive by environment plays an important role to the ear characters of maize. The results show that most of the QTS locations only detected in a single environment, indicating there is obvious interactions between gene and environment on the ear characters of maize. In addition, this study also shows that interactions between genes or genes and environments also have important roles to the ear characters of maize. In this study, to the detected three maize ear traits, a large part of its heritability are gene-environment interaction effects, and the additive effect only account a very small proportion. For example, to the ear length of maize,34.67%of the heritability is determined by the interaction of gene and environmental, and only0.88%of the heritability is determined by the additive effects.In the QTS detection and analysis in maize, mixed linear model was used. Mixed linear model model can differentiate genetic effects as additive effects, additive by environment effects, and interacting effects. The using of mixed linear model model in this research will provide a reference for the study of complex traits in other crops, as well as the results of this study will helpful for the molecular assisted breeding in cotton.