%0 Journal Article
%T QTLminer: identifying genes regulating quantitative traits
%A Rudi Alberts
%A Klaus Schughart
%J BMC Bioinformatics
%D 2010
%I BioMed Central
%R 10.1186/1471-2105-11-516
%X QTLminer is a bioinformatics tool that automatically performs QTL region analysis. It is available in GeneNetwork and it integrates information such as gene annotation, gene expression and sequence polymorphisms for all the genes within a given genomic interval.QTLminer substantially speeds up discovery of the most promising candidate genes within a QTL region.Quantitative trait locus (QTL) mapping is a powerful method to identify genes regulating complex traits. By combining molecular marker data of genetically related individuals with phenotypic trait values, genomic QTLs are identified that likely contain genetic regulators of the trait. This strategy has both been applied to 'classical' traits like body weight, blood pressure or disease susceptibility, as well as to traits measured using high-throughput technologies: mRNA abundances measured by microarrays [1,2], and protein or metabolite abundances measured by mass spectrometry [3,4]. QTLs generally span a genomic region containing tens to hundreds of genes. Identification of the most promising regulating genes within QTL intervals, which can then be functionally tested, still remains a major challenge. QTLminer has been implemented in the GeneNetwork [5], a large resource with genotypes, phenotypes and gene expression profiles for multiple organisms and genetic reference populations. It automatically analyses a QTL region and integrates information about the candidate genes, so that the best candidate genes can be quickly identified.QTLminer was implemented in Python as part of the GeneNetwork [5].QTLminer takes a QTL interval as input, which is defined by the chromosome and the start and end positions in megabases. The program automatically generates a list of genes within the interval and retrieves additional information for each gene. The first part comprises annotation data such as gene name, description, genomic position, Gene Ontology (GO) terms and KEGG pathways in which the gene is implicated. Next, th
%U http://www.biomedcentral.com/1471-2105/11/516