The patterns of population differentiation in a Brassica rapa core collection

With the recent advances in high throughput profiling techniques the amount of genetic and phenotypic data available has increased dramatically. Although many genetic diversity studies combine morphological and genetic data, metabolite profiling has yet to be integrated into these studies. For our study we selected 168 accessions representing the different morphotypes and geographic origins of Brassica rapa. Metabolite profiling was performed on all plants of this collection in the youngest expanded leaves, 5 weeks after transplanting and the same material was used for molecular marker profiling. During the same season a year later, 26 morphological characteristics were measured on plants that had been vernalized in the seedling stage. The number of groups and composition following a hierarchical clustering with molecular markers was highly correlated to the groups based on morphological traits (r = 0.420) and metabolic profiles (r = 0.476). To reveal the admixture levels in B. rapa, comparison with the results of the programme STRUCTURE was needed to obtain information on population substructure. To analyze 5546 metabolite (LC–MS) signals the groups identified with STRUCTURE were used for random forests classification. When comparing the random forests and STRUCTURE membership probabilities 86% of the accessions were allocated into the same subgroup. Our findings indicate that if extensive phenotypic data (metabolites) are available, classification based on this type of data is very comparable to genetic classification. These multivariate types of data and methodological approaches are valuable for the selection of accessions to study the genetics of selected traits and for genetic improvement programs, and additionally provide information on the evolution of the different morphotypes in B. rapa. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-010-1516-1) contains supplementary material, which is available to authorized users

Comparative Methods for Association Studies: A Case Study on Metabolite Variation in a Brassica rapa Core Collection

Background Association mapping is a statistical approach combining phenotypic traits and genetic diversity in natural populations with the goal of correlating the variation present at phenotypic and allelic levels. It is essential to separate the true effect of genetic variation from other confounding factors, such as adaptation to different uses and geographical locations. The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration. Methodology/Principal Findings A core collection of 168 Brassica rapa accessions of different morphotypes and origins was explored to find genetic association between markers and metabolites: tocopherols, carotenoids, chlorophylls and folate. A widely used linear model with modifications to account for population structure and kinship was followed for association mapping. In addition, a machine learning algorithm called Random Forest (RF) was used as a comparison. Comparison of results across methods resulted in the selection of a set of significant markers as promising candidates for further work. This set of markers associated to the metabolites can potentially be applied for the selection of genotypes with elevated levels of these metabolites. Conclusions/Significance The incorporation of the kinship correction into the association model did not reduce the number of significantly associated markers. However incorporation of the STRUCTURE correction (Q matrix) in the linear regression model greatly reduced the number of significantly associated markers. Additionally, our results demonstrate that RF is an interesting complementary method with added value in association studies in plants, which is illustrated by the overlap in markers identified using RF and a linear mixed model with correction for kinship and population structure. Several markers that were selected in RF and in the models with correction for kinship, but not for population structure, were also identified as QTLs in two bi-parental DH populations

Regulatory network of secondary metabolism in Brassica rapa:insight into the glucosinolate pathway

Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables

A naturally occurring splicing site mutation in the Brassica rapa FLC1 gene is associated with variation in flowering time

FLOWERING LOCUS C (FLC), encoding a MADS-domain transcription factor in Arabidopsis, is a repressor of flowering involved in the vernalization pathway. This provides a good reference for Brassica species. Genomes of Brassica species contain several FLC homologues and several of these colocalize with flowering-time QTL. Here the analysis of sequence variation of BrFLC1 in Brassica rapa and its association with the flowering-time phenotype is reported. The analysis revealed that a G→A polymorphism at the 5’ splice site in intron 6 of BrFLC1 is associated with flowering phenotype. Three BrFLC1 alleles with alternative splicing patterns, including two with different parts of intron 6 retained and one with the entire exon 6 excluded from the transcript, were identified in addition to alleles with normal splicing. It was inferred that aberrant splicing of the pre-mRNA leads to loss-of-function of BrFLC1. A CAPS marker was developed for this locus to distinguish Pi6+1(G) and Pi6+1(A). The polymorphism detected with this marker was significantly associated with flowering time in a collection of 121 B. rapa accessions and in a segregating Chinese cabbage doubled-haploid population. These findings suggest that a naturally occurring splicing mutation in the BrFLC1 gene contributes greatly to flowering-time variation in B. rapa

Biased Gene Fractionation and Dominant Gene Expression among the Subgenomes of Brassica rapa

Polyploidization, both ancient and recent, is frequent among plants. A “two-step theory" was proposed to explain the meso-triplication of the Brassica “A" genome: Brassica rapa. By accurately partitioning of this genome, we observed that genes in the less fractioned subgenome (LF) were dominantly expressed over the genes in more fractioned subgenomes (MFs: MF1 and MF2), while the genes in MF1 were slightly dominantly expressed over the genes in MF2. The results indicated that the dominantly expressed genes tended to be resistant against gene fractionation. By re-sequencing two B. rapa accessions: a vegetable turnip (VT117) and a Rapid Cycling line (L144), we found that genes in LF had less non-synonymous or frameshift mutations than genes in MFs; however mutation rates were not significantly different between MF1 and MF2. The differences in gene expression patterns and on-going gene death among the three subgenomes suggest that “two-step" genome triplication and differential subgenome methylation played important roles in the genome evolution of B. rapa

Molecular mapping and cloning of genes and QTLs in Brassica rapa

In this chapter an overview is given of QTL studies performed in the species Brassica rapa. First we provide an overview of the types of molecular markers that have been used in time, and the genetic maps that have been constructed from a broad range of populations, both in terms of population type and morphotypes used for the crosses. Since the publication of the B. rapa genome sequence, numbers of molecular markers have increased exponentially. Second, an overview is given of QTL studies published in the last few years, and thereafter the QTL studies that resulted in cloning of the causal genes are presented. A brief overview is given of association mapping studies performed in B. rapa, and the chapter is concluded with proposed strategies to identify genes underlying both qualitative and quantitative traits