Paper Info
Title
Association Cluster Detector: a tool for heuristic detection of significance clusters in whole-genome scans.
Abstract
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.The tool can be downloaded from http://www.upf.es/cexs/recerca/bioevo/softanddata.htm
Year
DOI
Venue
2005
10.1093/bioinformatics/bti1118
ECCB/JBI
Keywords
Field
DocType
multiple test,whole genome,association cluster detector,complex trait,whole genome scan,whole-genome scan,acd facilitates correction,multiple testing problem,complex network,heuristic detection,simple tool,entire genome,significance cluster,multiple testing,genetics,linkage disequilibrium,single nucleotide polymorphism,sliding window
Genome,Cluster (physics),Heuristic,Linkage disequilibrium,Computer science,Multiple comparisons problem,Complex network,Single-nucleotide polymorphism,Bioinformatics,Detector
Conference
Volume
Issue
ISSN
21 Suppl 2
2
1367-4811
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Tomàs Marquès-Bonet121.11
Oscar Lao211.11
Robert Goertsches300.68
Manuel Comabella411.39
Xavier Montalban521.42
Arcadi Navarro674.95