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Bioinformatics Advance Access published online on September 4, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm386
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Simulating association studies: a data-based resampling method for candidate regions or whole genome scans

Fred A. Wright 1,2,3, Hanwen Huang 1, Xiaojun Guan 4, Kevin Gamiel 4, Clark Jeffries 4,5, William T. Barry 1, Fernando Pardo-Manuel 2,6, Patrick F. Sullivan 2,6, Kirk C. Wilhelmsen 2,6 and Fei Zou 1,2,3

1Department of Biostatistics2Center for Genome Sciences and 3Center for Environmental Bioinformatics, University of North Carolina, Chapel Hill, 27599, USA4Renaissance Computing Institute, Europa Drive, Chapel Hill, North Carolina5School of Pharmacy and 6Department of Genetics, UNC Chapel Hill

*To whom correspondence should be addressed: Dr. Fred A. Wright, E-mail: fwright{at}bios.unc.edu


  Abstract

Motivation: Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realis-tic patterns of linkage disequilibrium and allele frequencies for typed SNPs.

Methods: We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project.

Results: Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.

Availability: the web-based tool is available at http://www.hapsample.org

login name: guest

password: Hap!123

Contact: fwright{at}bios.unc.edu

Associate Editor: Prof. Keith Crandall

Received on August 25, 2007; revised on June 21, 2007; accepted on July 20, 2007

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