Skip Navigation


Bioinformatics Advance Access originally published online on February 29, 2008
Bioinformatics 2008 24(7):958-964; doi:10.1093/bioinformatics/btn064
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/7/958    most recent
btn064v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Sampson, J. N.
Right arrow Articles by Self, S. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sampson, J. N.
Right arrow Articles by Self, S. G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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 and 2Statistical Center for HIV/AIDS Research and Prevention, Seattle, WA, USA

*To whom correspondence should be addressed.


  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 quantitative trait loci (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 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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.