Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing.

We explored genetic variation by sequencing a selection of 84 tomato accessions and related wild species representative of the Lycopersicon, Arcanum, Eriopersicon and Neolycopersicon groups, which has yielded a huge amount of precious data on sequence diversity in the tomato clade. Three new reference genomes were reconstructed to support our comparative genome analyses. Comparative sequence alignment revealed group-, species- and accession-specific polymorphisms, explaining characteristic fruit traits and growth habits in the various cultivars. Using gene models from the annotated Heinz 1706 reference genome, we observed differences in the ratio between non-synonymous and synonymous SNPs (dN/dS) in fruit diversification and plant growth genes compared to a random set of genes, indicating positive selection and differences in selection pressure between crop accessions and wild species. In wild species, the number of single-nucleotide polymorphisms (SNPs) exceeds 10 million, i.e. 20-fold higher than found in most of the crop accessions, indicating dramatic genetic erosion of crop and heirloom tomatoes. In addition, the highest levels of heterozygosity were found for allogamous self-incompatible wild species, while facultative and autogamous self-compatible species display a lower heterozygosity level. Using whole-genome SNP information for maximum-likelihood analysis, we achieved complete tree resolution, whereas maximum-likelihood trees based on SNPs from ten fruit and growth genes show incomplete resolution for the crop accessions, partly due to the effect of heterozygous SNPs. Finally, results suggest that phylogenetic relationships are correlated with habitat, indicating the occurrence of geographical races within these groups, which is of practical importance for Solanum genome evolution studies.

Low-coverage resequencing detects meiotic recombination pattern and features in tomato RILs.

Traditional plant breeding relies on meiotic recombination for mixing of parental alleles to create novel allele combinations. Detailed analysis of recombination patterns in model organisms shows that recombination is tightly regulated within the genome, but frequencies vary extensively along chromosomes. Despite being a model organism for fruit developmental studies, high-resolution recombination patterns are lacking in tomato. In this study, we developed a novel methodology to use low-coverage resequencing to identify genome-wide recombination patterns and applied this methodology on 60 tomato Recombinant Inbred Lines (RILs). Our methodology identifies polymorphic markers from the low-coverage resequencing population data and utilizes the same data to locate the recombination breakpoints in individuals by using a variable sliding window. We identified 1,445 recombination sites comprising 112 recombination prone regions enriched for AT-rich DNA motifs. Furthermore, the recombination prone regions in tomato preferably occurred in gene promoters over intergenic regions, an observation consistent with Arabidopsis thaliana, Zea mays and Mimulus guttatus. Overall, our cost effective method and findings enhance the understanding of meiotic recombination in tomato and suggest evolutionarily conserved recombination associated genomic features.

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles.

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0166-2) contains supplementary material, which is available to authorized users.

Diversity and linkage disequilibrium analysis within a selected set of cultivated tomatoes.

Within the Dutch genomics initiative the "Centre for Biosystems Genomics" (CBSG) a major research effort is directed at the identification and unraveling of processes and mechanisms affecting fruit quality in tomato. The basis of this fruit quality program was a diverse set of 94 cultivated tomato cultivars, representing a wide spectrum of phenotypes for quality related traits. This paper describes a diversity study performed on these cultivars, using information of 882 AFLP markers, of which 304 markers had a known map position. The AFLP markers were scored as much as possible in a co-dominant fashion. We investigated genome distribution and coverage for the mapped markers and conclude that it proved difficult to arrive at a dense and uniformly distributed coverage of the genome with markers. Mapped markers and unmapped markers were used to investigate population structure. A clear substructure was observed which seemed to coincide with a grouping based on fruit size. Finally, we studied amount and decay of linkage disequilibrium (LD) along the chromosomes. LD was observed over considerable (genetic) distances. We discuss the feasibility of marker-trait association studies and conclude that the amount of genetic variation in our set of cultivars is limited, but that there exists scope for association studies.