Genes for seed quality : integrating physiology and genetical genomics to mine for seed quality genes in tomato

Seed quality in tomato is associated with many complex physiological and genetical traits. The performance of seeds is determined by three interlinked and interactive components that constitute a performance triangle of genetics, physiological quality and the environment. So far, there has been little or no discussion about the genetic analysis of seed and seedling traits in tomato at a systems level. To the best of our knowledge, the present study is the first systemic analysis of the genetics of seed and seedling traits, adding to a growing body of information on tomato seed quality. With the aim of improving the production of high-quality tomato seeds, a multidisciplinary study (physiology, genetics and genomics) was undertaken to develop and evaluate methods for improving the percentage, rate and uniformity of germination and early seedling development, and for increasing the range of environmental conditions for germination. Primarily, we explored the natural variation present in a Solanum lycopersicum x Solanum pimpinellifolium RIL population to dissect the molecular-genetic mechanisms controlling seed quality. Although previous solutions to issues associated with seed quality phenotypes seemed promising, none have utilized the integration of genomic, phenotypic and metabolic datasets to understand seed quality in tomato.Thus, the integration of metabolic and genomic analysis contributed to a comprehensive biological understanding of observed phenotypic differences between RILs of S. lycopersicumx S. pimpinellifolium. Here we describe, for the first time, the use of a generalized genetical genomics (GGG) model in tomato seeds that incorporates genetics, as well as environmental effects, and we applied this approach to map traditional quantitative trait loci (Genetic QTLs) and QTLs that are the result of interaction between the genetics and environmental changes (Genetic x Environmental QTLs). This model uses chosen environmental perturbations (different seed developmental stages, i.e. dry and 6h imbibed seeds) in combination with the analysis of genetic variation present in the RIL population, to study the change of metabolites over the multiple environments and to identify genotype-by-environment interactions. This thesis gives an account of the integration of genotyping, phenotyping and a molecular phenotype using metabolomics in generating a novel understanding of seed phenotypes and their interaction with the environment. In summary, the integration of phenotypic and metabolomics data has facilitated the identification of potential biomarkers for better understanding of the complex nature of tomato seed quality.