Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
Bargsten, J.W. ; Nap, J.P.H. ; Sanchez Perez, G.F. ; Dijk, A.D.J. van - \ 2014
BMC Plant Biology 14 (2014). - ISSN 1471-2229
genome-wide association - protein function prediction - arabidopsis-thaliana - nucleotide polymorphisms - enrichment analysis - flowering time - complex traits - oryza-sativa - rice - architecture
Background Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait. Results The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait. Conclusions Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.
Impaired barrier function by dietary fructo-oligosaccharides (FOS) in rats is accompanied by increased colonic mitochondrial gene expression
Rodenburg, G.C.H. ; Keijer, J. ; Kramer, E.H.M. ; Vink, C. ; Meer, R. van der; Bovee-Oudenhoven, I.M.J. - \ 2008
BMC Genomics 9 (2008). - ISSN 1471-2164
glucagon-like peptide-2 - chain fatty-acids - ubiquitin-proteasome system - permeability changes - tight junctions - intestinal permeability - enrichment analysis - intracellular ph - mucosal injury - protein-kinase
Background Dietary non-digestible carbohydrates stimulate the gut microflora and are therefore presumed to improve host resistance to intestinal infections. However, several strictly controlled rat infection studies showed that non-digestible fructo-oligosaccharides (FOS) increase, rather than decrease, translocation of Salmonella towards extra-intestinal sites. In addition, it was shown that FOS increases intestinal permeability already before infection. The mechanism responsible for this adverse effect of FOS is unclear. Possible explanations are altered mucosal integrity due to changes in tight junctions or changes in expression of defense molecules such as antimicrobials and mucins. To examine the mechanisms underlying weakening of the intestinal barrier by FOS, a controlled dietary intervention study was performed. Two groups of 12 rats were adapted to a diet with or without FOS. mRNA was collected from colonic mucosa and changes in gene expression were assessed for each individual rat using Agilent rat whole genome microarrays. Results Among the 997 FOS induced genes we observed less mucosal integrity related genes than expected with the clear permeability changes. FOS did not induce changes in tight junction genes and only 8 genes related to mucosal defense were induced by FOS. These small effects are unlikely the cause for the clear increase in intestinal permeability that is observed. FOS significantly increased expression of 177 mitochondria-related genes. More specifically, induced expression of genes involved in all five OXPHOS complexes and the TCA cycle was observed. These results indicate that dietary FOS influences intestinal mucosal energy metabolism. Furthermore, increased expression of 113 genes related to protein turnover, including proteasome genes, ribosomal genes and protein maturation related genes, was seen. FOS upregulated expression of the peptide hormone proglucagon gene, in agreement with previous studies, as well as three other peptide hormone genes; peptide YY, pancreatic polypeptide and cholecystokinin. Conclusion We conclude that altered energy metabolism may underly colonic barrier function disruption due to FOS feeding in rats.
A framework to identify physiological responses in microarray based gene expression studies: selection and interpretation of biologically relevant genes
Rodenburg, G.C.H. ; Heidema, A.G. ; Boer, J.M.A. ; Bovee-Oudenhoven, I.M.J. ; Feskens, E.J.M. ; Mariman, E.C.M. ; Keijer, J. - \ 2008
Physiological genomics 33 (2008). - ISSN 1094-8341 - p. 78 - 90.
random forest - enrichment analysis - classification - tools - powerful - profiles - networks - pathways - patterns - resource
In whole genome microarray studies major gene expression changes are easily identified, but it is a challenge to capture small, but biologically important, changes. Pathway based programs can capture small effects, but may have the disadvantage to be restricted to functionally annotated genes. A structured approach towards the identification of major and small changes for interpretation of biological effects is needed. We present a structured approach, a framework, that addresses different considerations in 1) the identification of informative genes in microarray datasets and 2) the interpretation of their biological relevance. The steps of this framework include gene ranking, gene selection, gene grouping and biological interpretation. Random forests (RF), which takes gene-gene interaction into account, is examined to rank and select genes. For human, mouse and rat whole genome arrays, less than half of the probes on the array is annotated. Consequently, pathway analysis tools ignore half of the information present in the microarray dataset. The framework described takes all genes into account. RF is a useful tool to rank genes by taking interactions into account. Applying a permutation approach, we were able to define an objective threshold for gene selection. RF combined with Self-organizing maps identified genes with coordinated but small gene expression responses that were not fully annotated, but corresponded to the same biological process. The presented approach provides a flexible framework for biological interpretation of microarray datasets. It includes all genes in the dataset, takes gene-gene interactions into account and provides an objective threshold for gene selection.