@phdthesis{Tong2019,
  author    = {Tong, Hao},
  title     = {Dissection of genetic architecture of intermediate phenotypes and predictions in plants},
  school      = {Universit{\"a}t Potsdam},
  pages     = {127},
  year      = {2019},
  abstract  = {Determining the relationship between genotype and phenotype is the key to understand the plasticity and robustness of phenotypes in nature. While the directly observable plant phenotypes (e.g. agronomic, yield and stress resistance traits) have been well-investigated, there is still a lack in our knowledge about the genetic basis of intermediate phenotypes, such as metabolic phenotypes. Dissecting the links between genotype and phenotype depends on suitable statistical models. The state-of-the-art models are developed for directly observable phenotypes, regardless the characteristics of intermediate phenotypes. This thesis aims to fill the gaps in understanding genetic architecture of intermediate phenotypes, and how they tie to composite traits, namely plant growth. The metabolite levels and reaction fluxes, as two aspects of metabolic phenotypes, are shaped by the interrelated chemical reactions formed in genome-scale metabolic network. Here, I attempt to answer the question: Can the knowledge of underlying genome-scale metabolic network improve the model performance for prediction of metabolic phenotypes and associated plant growth? To this end, two projects are investigated in this thesis. Firstly, we propose an approach that couples genomic selection with genome-scale metabolic network and metabolic profiles in Arabidopsis thaliana to predict growth. This project is the first integration of genomic data with fluxes predicted based on constraint-based modeling framework and data on biomass composition. We demonstrate that our approach leads to a considerable increase of prediction accuracy in comparison to the state-of-the-art methods in both within and across environment predictions. Therefore, our work paves the way for combining knowledge on metabolic mechanisms in the statistical approach underlying genomic selection to increase the efficiency of future plant breeding approaches. Secondly, we investigate how reliable is genomic selection for metabolite levels, and which single nucleotide polymorphisms (SNPs), obtained from different neighborhoods of a given metabolic network, contribute most to the accuracy of prediction. The results show that the local structure of first and second neighborhoods are not sufficient for predicting the genetic basis of metabolite levels in Zea mays. Furthermore, we find that the enzymatic SNPs can capture most the genetic variance and the contribution of non-enzymatic SNPs is in fact small. To comprehensively understand the genetic architecture of metabolic phenotypes, I extend my study to a local Arabidopsis thaliana population and their hybrids. We analyze the genetic architecture in primary and secondary metabolism as well as in growth. In comparison to primary metabolites, compounds from secondary metabolism were more variable and show more non-additive inheritance patterns which could be attributed to epistasis. Therefore, our study demonstrates that heterozygosity in local Arabidopsis thaliana population generates metabolic variation and may impact several tasks directly linked to metabolism. The studies in this thesis improve the knowledge of genetic architecture of metabolic phenotypes in both inbreed and hybrid population. The approaches I proposed to integrate genome-scale metabolic network with genomic data provide the opportunity to obtain mechanistic insights about the determinants of agronomically important polygenic traits.},
  language  = {en}
}