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Bioinformatics Advance Access published online on February 4, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti261
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received October 23, 2004
Revised December 13, 2004
Accepted December 31, 2004

Article

Inference of missing SNPs and information quantity measurements for haplotype blocks

Shih-Chieh Su 1, C.-C. Jay Kuo 1, and Ting Chen 2*

1 Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA
2 Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA

* To whom correspondence should be addressed.
Ting Chen, E-mail: tingchen{at}usc.edu


  Abstract

Motivation: Missing data in genotyping single nucleotide polymorphism (SNP) spots are common. High-throughput genotyping methods usually have a high rate of missing data. For example, the published human chromosome 21 data by Patil et al. (2001) contains about 20% missing SNPs. Inferring missing SNPs using the haplotype block structure is promising but difficult because the haplotype block boundaries are not well-defined. Here we propose a global algorithm to overcome this difficulty.

Results: First, we propose to use entropy as a measure of haplotype diversity. We show that the entropy measure combined with a dynamic programming algorithm produces better haplotype block partitions than other measures. Second, based on the entropy measure, we propose a two-step iterative partition-inference (IPI) algorithm for the inference of missing SNPs. At the first step, we apply the dynamic programming algorithm to partition haplotypes into blocks. At the second step, we use an iterative process similar to the expectation-maximization (EM) algorithm to infer missing SNPs in each haplotype block so as to minimize the block entropy. The algorithm iterates these two steps until the total block entropy is minimized. We test our algorithm in several experimental data sets. The results show that the global approach significantly improves the accuracy of the inference.

Availability: Upon request.


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A. Roberts, L. McMillan, W. Wang, J. Parker, I. Rusyn, and D. Threadgill
Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows
Bioinformatics, July 1, 2007; 23(13): i401 - i407.
[Abstract] [Full Text] [PDF]


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