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BMC Genomics. 2009; 10: 184.
Published online 2009 April 24. doi:  10.1186/1471-2164-10-184
PMCID: PMC2683869
QTL global meta-analysis: are trait determining genes clustered?
Hanni Salih1 and David L Adelsoncorresponding author2
1Department of Animal Science and Interdisciplinary Faculty of Genetics, Texas A&M University, 2471 TAMU, Kleberg Center, College Station, TX, USA
2School of Molecular and Biomedical Science, The University of Adelaide, 108 Oliphant, Adelaide, SA, Australia
corresponding authorCorresponding author.
Hanni Salih: h_s1lih/at/neo.tamu.edu; David L Adelson: david.adelson/at/adelaide.edu.au
Received September 24, 2008; Accepted April 24, 2009.
Abstract
Background
A key open question in biology is if genes are physically clustered with respect to their known functions or phenotypic effects. This is of particular interest for Quantitative Trait Loci (QTL) where a QTL region could contain a number of genes that contribute to the trait being measured.
Results
We observed a significant increase in gene density within QTL regions compared to non-QTL regions and/or the entire bovine genome. By grouping QTL from the Bovine QTL Viewer database into 8 categories of non-redundant regions, we have been able to analyze gene density and gene function distribution, based on Gene Ontology (GO) with relation to their location within QTL regions, outside of QTL regions and across the entire bovine genome. We identified a number of GO terms that were significantly over represented within particular QTL categories. Furthermore, select GO terms expected to be associated with the QTL category based on common biological knowledge have also proved to be significantly over represented in QTL regions.
Conclusion
Our analysis provides evidence of over represented GO terms in QTL regions. This increased GO term density indicates possible clustering of gene functions within QTL regions of the bovine genome. Genes with similar functions may be grouped in specific locales and could be contributing to QTL traits. Moreover, we have identified over-represented GO terminology that from a biological standpoint, makes sense with respect to QTL category type.
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