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Bioinformatics 2005 21(Suppl 2):ii180-ii181; doi:10.1093/bioinformatics/bti1118
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Association Cluster Detector: a tool for heuristic detection of significance clusters in whole-genome scans

Tomàs Marquès-Bonet 1,{dagger}, Oscar Lao 1,{dagger}, Robert Goertsches 2, Manuel Comabella 2, Xavier Montalban 2 and Arcadi Navarro 1,*

1Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra Dr. Aiguader 80, 08003 Barcelona, Spain
2Unitat de Neuroimmunologia Clínica, Hospital Universitari Vall d'Hebron Escuela de Enfermeria 2a planta, Psg Vall d'Hebron 119–129, 08035 Barcelona, Spain

*To whom correspondence should be addressed.

Summary: Whole genome scans analyze large sets of genetic markers, mainly single nucleotide polymorphisms, over the entire genome in order to find variants and regions associated with complex traits so these can be further investigated. Analyzing the results of such scans becomes difficult due to multiple testing problems and to the genomic distributions of recombination, linkage disequilibrium and true associations, which generate an extremely complex network of dependences between markers. Here we present Association Cluster Detector (ACD), a simple tool aiming to ease the analysis of the results of whole genome scans. ACD facilitates correction for multiple tests using several standard procedures and implements a sliding-window heuristic method that helps in detecting potentially interesting candidate regions by exploiting the property of non-random distribution of significantly associated markers.

Availability: The tool can be downloaded from http://www.upf.es/cexs/recerca/bioevo/softanddata.htm

Contact: arcadi.navarro{at}upf.edu


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