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Bioinformatics Advance Access published online on October 25, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti732
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 17, 2005
Revised October 4, 2005
Accepted October 19, 2005

Article

Haplotype-based linkage disequilibrium mapping via direct data mining

Jing Li 1* and Tao Jiang 2

1 Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA
2 Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA; Center for Advanced Study, Tsinghua University, Beijing, China; Shanghai Center for Bioinformatics Technology, Shanghai, China

* To whom correspondence should be addressed.
Jing Li, E-mail: jingli{at}eecs.case.edu


  Abstract

Motivation: With the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers and information on haplotype structures and frequencies, a great challenge is how to take advantage of haplotype information in the association mapping of complex diseases in case-control studies.

Results: We present a novel approach for association mapping based on directly mining haplotypes (i.e., phased genotype pairs) produced from case-control data or case-parent data via a density-based clustering algorithm, which can be applied to whole-genome screens as well as candidate-gene studies in small genomic regions. The method directly explores the sharing of haplotype segments in affected individuals that are rarely present in normal individuals. The measure of sharing between two haplotypes is defined by a new similarity metric that combines the length of the shared segments and the number of common alleles around any marker position of the haplotypes, which is robust against recent mutations/genotype errors and recombination events. The effectiveness of the approach is demonstrated by using both simulated datasets and real datasets. The results show that the algorithm is accurate for different population models and for different disease models, even for genes with small effects, and it outperforms some recently developed methods.

Availability: The software, HapMiner, is available on the authors' website at http://vorlon.case.edu/~jxl175/HapMiner.html.


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