学位专题

封面
声明
中文摘要
英文摘要
目录
List of abbreviations
Chapter 1: General Introduction
1.1 Brassica napus and its global importance
1.1.1 Brassica species and their economic importance
1.1.2 History of Brassica napus
1.2 Regulation of flowering time in plants
1.2.1 Arabidopsis thaliana as a model for flowering time
1.2.2 Floral pathways
1.2.3 Floral integrators
1.3 Flowering in rapeseed
1.3.1 Regulation of flowering in rapeseed
1.3.2 Flowering time implication in rapeseed breeding and cultivation
1.4 Genetic markers for marker-assisted selection
1.4.1 Bridging the gap between phenotype and genotype
1.4.2 Association mapping a unique approach for genomic prediction/selection
1.4.3 Factors influencing LD
1.4.4 A Mixed Linear Model approach (MLM)
1.4.5 Haplotype blocks
1.5 Objectives and significance of the study
1.5.1 Objectives
1.5.2 Significance
Chapter 2: Identification of genetic variation of vegetative traits by genome-wide association approaches in oilseed rape
2.1 Introduction
2.2 Materials and Methods
2.2.1 Plant materials, field experiments, and phenotyping
2.2.2. SNP genotyping and quality control
2.2.3. Statistical analysis of phenotype
2.2.4. Population structure, genetic relatedness analysis, and GWAS
2.2.5 Haplotype-based GWAS
2.2.6 QTL comparison of flowering time among different mapping populations with previously reported loci
2.2.7 Isolation of RNA and RNA-seq data analysis
2.2.8 Identification of candidate genes
2.3 Results
2.3.1 Phenotypic variations and correlations for vegetative traits of flowering time in an OSR diverse core collection panel
2.3.2 SNP GWAS of vegetative traits
2.3.3 Haplotype-based GWAS of vegetative traits
2.3.4 Identification of QTLs based on SNP and Hap-GWAS for vegetative traits in OSR
2.3.5 QTL clusters
2.3.6 Integration of SNP and Hap-GWAS for vegetative traits of B. napus
2.3.7 Transcriptomics Analysis
2.3.8 Prioritization of candidate genes by the integration of SNP and Hap-GWAS with transcriptomics study
2.4 Discussion
2.4.1 Importance of diverse germplasm resources for vegetative traits
2.4.2 Implications of GWAS in rapeseed breeding
2.4.3 Pleiotropic effects of vegetative traits in B. napus
2.4.4 Integration of SNP and hap-GWAS is a powerful approach to explain the more stable loci related to vegetative traits of OSR
2.5 Conclusion
Chapter 3: Identification of loci controlling days to flowering, full and final flowering by genome-wide association study \(GWAS\)and RNA-Seq in Brassica napus
3.1 Introduction
3.2 Materials and methods
3.2.1 Plant materials, field experiments, and phenotyping
3.2.2 Isolation of RNA and RNA-seq data analysis
3.2.3 Constructing a Gene Co-expression Network
3.3 Results
3.3.1 Phenotypic variations and correlations analysis of DTF, Full and Final flowering of rapeseed
3.3.2 SNP genotyping
3.3.3 SNP based GWAS for DTF, Full and Final flowering of Brassica napus
3.3.4 Haplotype-based GWAS for DTF, Full and Final flowering of oilseed rape
3.3.5 QTLs based on SNPs and hap-GWAS
3.3.6 GWAS detected QTL clusters
3.3.7 Integration of SNP and hap-GWAS for flowering time traits of rapeseed
3.3.8 Transcriptomic Analysis
3.3.9 Integration of co-localized regions of flowering time traits with transcriptomics analysis narrowed down the number of flowering time genes
3.3.10 Construction of gene co-expression networks
3.4 Discussion
3.4.1 Application of genomics breeding in OSR
3.4.2 Comparison of SNP and Haplotype GWAS
3.4.3 A high degree of pleiotropy for the flowering time of rapeseed suggested by the co-localized QTLs of SNP and hap-GWAS
3.4.4 The integration of SNP and Hap-GWAS identified stable and novel regions for the flowering time of rapeseed
3.4.5 Identification of “key pleiotropic candidate genes” by the combination of GWAS, transcriptomics and network analysis
3.5 Conclusion
Chapter 4: SNP and haplotype-based genome-wide association studies combination uncovers flowering intervals loci in Brassica
4.1 Introduction
4.2 Materials and Methods
4.2.1 Plant materials, field experiments, and phenotyping
4.3 Results
4.3.1 Phenotypic variations and correlations for vegetative traits of flowering time in an OSR diverse core collection panel
4.3.2 GWAS for four flowering interval traits revealed favorable QTLs based on SNPs
4.3.3 GWAS for five flowering time growth interval traits exposed auspicious QTLs and QTL hotspots based on haplotypes
4.3.4 GWAS revealed QTL hotspots in B. napus
4.3.5 Pleiotropy of the QTLs identified by both SNP and Haplotype GWAS
4.3.6 Integration of SNP and hap-GWAS with the co-localization of flowering intervals genomic loci
4.3.7 Prediction of candidate genes for flowering time growth interval
4.4 Discussion
4.4.1 Application of GWAS in genomic breeding of oilseed rape
4.4.2 Integration of SNP and hap-GWAS played an influential role in excavating the genetic loci for flowering interval traits
4.4.3 Importance of haplotype-based GWAS
4.4.4 Pleiotropic effects of SNP and Haplotype based GWAS on flowering interval traits on B. napus
4.4.5 Prioritize the candidate genes for flowering interval traits of Brassica napus in the flowering time pathway
4.5 Conclusion
Chapter 5: General Discussion
5.1 Co-localization of QTLs of DTF and FP between SNP and hap-GWAS in B. napus
5.2 The integration of SNP and hap-GWAS identified stable and novel regions for simultaneously regulating DTF and FP of rapeseed to avoid SSR disease
5.3 Identification of "key pleiotropic flowering time candidate genes" for the DTF and FP in B. napus
5.4 Identification of integrated genomic regions linked to the maximum number of flowering traits of rapeseed
Chapter 6: Conclusions
参考文献
Appendices
Curriculum Vitae