Genomics and computational biology

We are exploiting new sequencing technologies based on massively parallel sequencing to further our understanding of genetic variation in oilseed rape to link genotype to phenotype and support crop improvement through breeding programs.  Oilseed rape provides an interesting example of how this new technology can be applied to a polyploid crop. This work is being done in collaboration with Martin Trick, JIC.

Transcriptome sequencing of oilseed rape

Currently, we are using Illumina mRNA-Seq to identify sequence variation and measure transcriptome abundance in oilseed rape.  Analysis of sequence variation within oilseed rape populations can be used to identify single nucleotide polymorphisms (SNPs) which can be used for the construction of genetic maps and for association studies. Polymorphisms detected between the two parental genomes (B. rapa and B. oleracea) of oilseed rape, are termed inter-homoeologue polymorphisms (IHPs).

Computational tools have been developed to score the SNPs in mapping populations and link the SNPs to genome of origin using association with IHPs. Transcript abundance is used to construct gene co-expression networks, identify relationships with agronomically important traits and to study gene expression changes associated with polyploidization.

Network and systems analysis in oilseed rape

This is one part of the tri-national ERANET Plant Genomics project  ASSYST (Associative expression and systems analysis of complex traits in oilseed rape).  Gene expression data has been generated for well-defined populations of segregating Brassica napus genotypes, this will be integrated with quantitative metabolite and phenotype data using a systems-genetics approach.  We are constructing gene co-expression networks for oilseed rape populations. This generates a biological network within which genes which have a similar expression pattern are connected.  These networks have two key properties; they contain clusters of highly connected genes (known as “modules”) and a small number of genes have a large number of connections (termed “hubs”).  

Genes associated with a particular biological process or biological pathway tend to cluster together into the same module, the genes that have a large number of connections are candidates for key regulators.  We have captured a variety of biological pathways in oilseed rape using knowledge from the closely related model plant, Arabidopsis. These pathways are merged with the gene co-expression networks enabling us to focus on that part of the network associated with the biological pathway of interest. These networks can then be visualised and interrogated using Cytoscape.

Another approach is to use WGCNA (Weighted Gene Correlation Network Analysis) to identify modules in the network associated with various phenotypic traits which have been measured in the oilseed rape population. We are particularly interested in traits such as seedling growth and seed quality characters with respect to biosynthesis of oil, protein and fibre components.