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Bioinformatics Advance Access published online on November 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm552
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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.

Identification of Linked Regions using High-Density SNP Genotype Data in Linkage Analysis

Guohui Lin a,*, Zhanyong Wang b, Lusheng Wang b, Yu-Lung Lau c and Wanling Yang c,{dagger}

aDepartment of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada; bDepartment of Computer Science, City University of Hong Kong, Kowloon, Hong Kong; and cDepartment of Paediatrics & Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, 21 Sassoon Road, Hong Kong.

{dagger}To whom correspondence should be addressed. Dr. Wanling Yang, E-mail: yangwl{at}hkucc.hku.hk


  Abstract

Motivation: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary.

Results: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the Chi-square model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data.

Download: http://paed.hku.hk/uploadarea/yangwl/html/index.html

Associate Editor: Prof. Keith Crandall

*These authors contributed equally to this work

Received on August 10, 2007; revised on October 13, 2007; accepted on October 30, 2007

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