QTLminer: identifying genes regulating quantitative traits
© Alberts and Schughart; licensee BioMed Central Ltd. 2010
Received: 7 May 2010
Accepted: 15 October 2010
Published: 15 October 2010
Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.
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 , 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 .
Results and Discussion
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, the amount of non-synonymous single nucleotide polymorphisms (nsSNPs) within the gene is displayed. nsSNPs result in amino acid changes in the corresponding protein. These changes may modify its structure and may thus be causative for the phenotypic differences which were mapped. Furthermore, the user can select three GeneNetwork expression data sets. For each of the data sets, gene expression and information about cis-regulation will be added. In this way, the user can see whether candidate genes are expressed in the tissue under study. Genes that are only expressed in the tissue of interest and not in others might even be better candidates. The user should however be aware that high expression is not always required, i.e. lowly expressed genes may also have a strong influence on traits. In addition, information about cis-regulation is added. A gene is cis-regulated if its expression maps close to its own genomic position. Any cis-regulated gene in the QTL region is a good candidate to regulate the trait, since cis-regulation indicates a difference in gene expression levels, which may regulate the trait. Alberts et al.  have shown that 'ghost' cis-eQTLs (expression QTLs) can be detected if there are SNPs or other sequence variants in the probe regions that cause a difference in hybridization signal. These cases might also be interesting since the sequence variants in the probe regions might result in changes in the protein structure which can regulate the trait.
To demonstrate the utility of the program, we took a QTL hotspot on mouse distal chromosome 1 as example. This region, also called Qrr1, contains many QTLs that control neural and behavioral phenotypes, including motor behavior, escape latency, emotionality, and seizure susceptibility (Szs1) . Mozhui et al. have further investigated this region and revealed a highly complex gene expression regulatory interval in Qrr1, composed of multiple loci modulating the expression of functionally cognate sets of genes. In the distal part of Qrr1, they have identified the gene Fmn2 as a strong candidate. To re-analyze this interval using QTLminer, open a web browser and go to http://genenetwork.helmholtz-hzi.de and click 'Search' and 'QTLminer'. In the form that appears, choose Chromosome 1, view from 173 Mb to 177 Mb. Select two mouse strains for which nsSNPs should be analyzed. Choose three GeneNetwork data sets and click 'Analyze QTL interval'. Three hippocampus data sets in BXD, CXB and LXS mice are chosen by default.
An additional feature of QTLminer is the visualization of the haplotypes within a QTL region. In the haplotype plot (Figure 1B), individuals are sorted according to their quantitative trait value, and their haplotypes are indicated by colors. The gene Kcnj9 is one of the genes with a strong trans eQTL in the Qrr1 interval in hippocampus. To obtain BXD haplotypes for this gene, search the BXD hippocampus data set in GeneNetwork for Kcnj9, click probeset 1450712 at, click the button 'Interval Mapping' and fill in Chromosome 1, 173 until 177 Mb. Select 'Haplotype Analyst' and click 'Remap'. It is immediately obvious that mice with a C57BL/6J (red) allele at the QTL location have a low trait value, whereas mice with a DBA/2J (green) allele have a high trait value. This visualization may be used for fine mapping QTLs. Suppose that a QTL separates two groups of individuals with low and high trait value for most individuals, but some individuals (which were not yet studied) have a recombination within the interval. Then these individuals may be used for further genotyping and in this way, a QTL region can be further narrowed down.
QTLminer automatically integrates gene annotation, Gene Ontology terms, KEGG pathway information, gene expression and cis-regulation data for all genes within a QTL interval. With only a few mouse clicks on the GeneNetwork website, the most promising candidate genes within a given QTL region are quickly highlighted.
Availability and requirements
Project name: QTLminer
Project home page: http://genenetwork.helmholtz-hzi.de (click Search - QTLminer)
Operating system(s): Platform independent
Programming language: Python
Other requirements: none
Any restrictions to use by non-academics: none
We thank Robert W. Williams for very helpful comments and suggestions.
Funding: This work was supported by the Helmholtz Centre for Infection Research and by GeNeSys (German Network for Systems Genetics), a Helmholtz Virtual Institute, financed by the Helmholtz Association.
- Brem RB, Yvert G, Clinton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296: 752–755. 10.1126/science.1069516View ArticlePubMedGoogle Scholar
- Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI, Vellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC, Williams RW, Cooke MP, de Haan G: Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat Genet 2005, 37: 225–232. 10.1038/ng1497View ArticlePubMedGoogle Scholar
- Foss EJ, Radulovic D, Shaffer SA, Ruderfer DM, Bedalov A, Goodlett DR, Kruglyak L: Genetic basis of proteome variation in yeast. Nat Genet 2007, 39: 1369–1375. 10.1038/ng.2007.22View ArticlePubMedGoogle Scholar
- Keurentjes JJ, Fu J, de Vos CH, Lommen A, Hall RD, Bino RJ, van der Plas LH, Jansen RC, Vreugdenhil D, Koornneef M: The genetics of plant metabolism. Nat Genet 2006, 38: 842–849. 10.1038/ng1815View ArticlePubMedGoogle Scholar
- Alberts R, Terpstra P, Li Y, Breitling R, Nap JP, Jansen RC: Sequence polymorphisms cause many false cis eQTLs. PLoS ONE 2007, 2: e622. 10.1371/journal.pone.0000622View ArticlePubMedPubMed CentralGoogle Scholar
- Mozhui K, Ciobanu DC, Schikorski T, Wang X, Lu L, Williams RW: Dissection of a QTL hotspot on mouse distal chromosome 1 that modulates neurobehavioral phenotypes and gene expression. PLoS Genet 2008, 4: e1000260. 10.1371/journal.pgen.1000260View ArticlePubMedPubMed CentralGoogle Scholar
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