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Table of contents
Chapter 1 Recovering power in association mapping panels with variable levels of linkage disequilibrium
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
Statistical models for association mapping and power evaluation
Analytical evaluation of the impact of panel characteristics on power
Kinship estimation
Simulation based evaluation of the impact of the estimation of K on false positive control and power
Genetic material and genotyping data
Specific parameterization
RESULTS
Diversity and Linkage Disequilibrium in maize panels
Relationship between MAF, Fst, CorK and power
Variation of analytical power and CorK along chromosomes
Simulation based assessment of kinship estimation on false positive control and power
DISCUSSION AND CONCLUSIONS
Analytical investigation of potential power along the genome with usual model (MK_Freq)
Simulation based comparison of type I risk and power of statistical models associated with different estimations of K
Acknowledgments
LITERATURE
Chapter 2 Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
Genetic material and genotyping data
Diversity analysis
Linkage Disequilibrium (LD)
Phenotypic data
Phenotypic characterization of the genetic groups within each panel
Statistical model for association mapping
RESULTS
Diversity and structure analysis
Linkage disequilibrium
Phenotypic variation
Phenotypic characterization of the genetic groups within each panel
Association mapping results
DISCUSSION AND CONCLUSION
Genetic Diversity organization
Trait variation within and among genetic groups
Association mapping results
Conclusions
Acknowledgments
LITERATURE
Chapter 3 ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
Genetic material
Field data
Genotyping, diversity and relationship matrix
Statistical model
Optimization criteria and CD
Optimization algorithm
Observed prediction reliability and robustness of the optimization to variation of heritability
Link between the PEV and the observed prediction error
Genetic properties of optimized calibration sets
RESULTS
Trait variation
Description of the diversity and of the genomic relationship matrix
Observed prediction reliability and robustness of the optimization to variation of heritability
Link between the PEV and the observed prediction error
Genetic properties of optimized calibration sets
DISCUSSION
Acknowledgments
LITERATURE
General discussion
Increasing power in association mapping
Using molecular information to maximize GS efficiency: optimizing the sampling of the calibration set
Diversity analysis and association mapping in the Dent and Flint Cornfed panels .
Towards an integrated approach in plant breeding
LITERATURE
APPENDICES
Appendix I: supplemental chapter 1
Appendix II: supplemental chapter 2
Appendix III: supplemental chapter 3
