Bioinformatics Advance Access originally published online on February 29, 2008
Bioinformatics 2008 24(7):958-964; doi:10.1093/bioinformatics/btn064
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Identifying trait clusters by linkage profiles: application in genetical genomics
1Department of Biostatistics, University of Washington and 2Statistical Center for HIV/AIDS Research and Prevention, Seattle, WA, USA
*To whom correspondence should be addressed.
| Abstract |
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Motivation: Genes often regulate multiple traits. Identifying clusters of traits influenced by a common group of genes helps elucidate regulatory networks and can improve linkage mapping.
Methods: We show that the Pearson correlation coefficient,
, between two LOD score profiles can, with high specificity and sensitivity, identify pairs of genes that have their transcription regulated by shared quantitative trait loci (QTL). Furthermore, using theoretical and/or empirical methods, we can approximate the distribution of
under the null hypothesis of no common QTL. Therefore, it is possible to calculate P-values and false discovery rates for testing whether two traits share common QTL. We then examine the properties of
through simulation and use
to cluster genes in a genetical genomics experiment examining Saccharomyces cerevisiae.
Results: Simulations show that
can have more power than the clustering methods currently used in genetical genomics. Combining experimental results with Gene Ontology (GO) annotations show that genes within a purported cluster often share similar function.
Software: R-code included in online Supplementary Material.
Contact: joshua.sampson{at}yale.edu
Supplementary information: Supplementary materials are available at Bioinformatics online.
Associate Editor: John Quackenbush
Received on November 3, 2007; revised on January 11, 2008; accepted on February 17, 2008