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Bioinformatics Advance Access published online on February 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn064
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identifying Trait Clusters by Linkage Profiles: Application in Genetical Genomics

Joshua N. Sampson 1,* and Steven G. Self 1,2

1Department of Biostatistics, University of Washington, Seattle, WA
2Statistical Center for HIV/AIDS Research and Prevention, Seattle, WA

*To whom correspondence should be addressed. Dr. Joshua N. Sampson, E-mail: joshua.sampson{at}yale.edu


  Abstract

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, Formula, between two LOD score profiles can, with high specificity and sensitivity, identify pairs of genes that have their transcription regulated by shared QTL. Furthermore, using theoretical and/or empirical methods, we can approximate the distribution of Formula 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 Formula through simulation and use Formula to cluster genes in a genetical genomics experiment examining Saccharomyces cerevisiae.

Results: Simulations show that Formula can have more power than the clustering methods currently used in genetical genomics. Combining experimental results with GO annotations show that genes within a purported cluster often share similar function.

Software: R-code included in on-line supplementary material

Contact: joshua.sampson{at}yale.edu

Associate Editor: Prof. John Quackenbush

Received on November 3, 2007; revised on February 11, 2008; accepted on February 17, 2008

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