The genetics of the metabolome in Brassica rapa

In this thesis the metabolic variation in Brassica rapa is described based on results of metabolic profiling of a core collection of 168 accessions representing the different crop types and geographical origin and a Doubled Haploid population. In Chapter 2 we describe the genetic and phenotypic variation of this core collection to explore the possibility of following association mapping methods to identify genes involved in metabolic regulation. We explored through a genome wide and candidate gene approach different association mapping methods in a core collection in Chapters 3 and 4 respectively and in Chapter 5 we combined the QTL analysis of targeted and untargeted metabolites profiled through LC-MS with expression QTLs following a genetical genomics approach aiming to detect genes underlying the metabolite QTL. The genetic diversity evaluated through the screening of AFLP and SSR markers was correlated with classification of accessions using morphological and metabolic trait values. The relationship between accessions in groups was compared using hierarchical clustering and the STRUCTURE program. Using Random Forests classification a set of metabolites was selected that differentiated the different sub groups as determined by STRUCTURE (Chapter 2). Based on the classification into subpopulations using the STRUCTURE program we included the subpopulations as a correction term in our statistical model for association studies (Chapter 3). Additionally, because of the increasing amount of data that will be soon available through sequencing technology we tested the use of Random Forests in the search for marker-trait association for the isoprenoids pathway. Using the results obtained with the linear models as implemented in TASSEL and the results obtained in Random Forests we found a set of 16 significant markers with potential use for marker assisted selection in breeding for several isoprenoidsThe determination of map positions through synteny prediction and genetic mapping of a group of genes from the glucosinolate pathway lead us to identify Myb28 and MAM as candidate genes mapping under a previously detected major QTL for glucosinolates We followed an association mapping approach to investigate their role in the variation in glucosinolates in the core collection by profiling 37 SSR markers, which included markers linked to these candidate genes and markers distributed along different positions in linkage group A03 (Chapter 4). Interestingly, not only MAM and Myb28, but the AOP and GS-OH genes involved in side chain modification and Myb29 in transcriptional regulation were also associated with glucosinolate levels. A genetical genomics approach was followed to identify candidate genes for variation inmetabolites of six biosynthetic pathways: carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids, based on the co-localization analysis and comparison between metabolic (m)QTLs and expression (e)QTLs (Chapter 5). A Doubled Haploid (DH) population was profiled for metabolite content and variation through targeted and LC-MS untargeted approaches. Additionally, the same population was profiled for transcript variation with a newly developed 105K Cogenics array assembled using mainly EST sequences from three species: B. napus, B. rapa and B. oleracea. Co-localization of eQTLs and mQTLs for several isoprenoids (tocopherols and carotenoids) and glucosinolates lead us to the identification of candidate genes for these pathways. However, further work is needed to identify the gene or genes underlying a major cluster of QTLs for 112 centrotypes derived from the LC-MS untargeted data. The results obtained through this combined approach and considerations that need to be taken into account when performing these types of studies with regard to identification of paralogues and the use of a multi Brassica species microarray for transcript profiling in Brassica rapa are discussed.In the final Chapter, the combined use of core collections encompassing the genetic diversity within B. rapa and biparental DH populations to unravel the genetic regulation of the metabolome are discussed.