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Journal of Computational Biology
Robustness of Inference of Haplotype Block Structure

To cite this article:
Russell Schwartz, Bjarni V. Halldórsson, Vineet Bafna, Andrew G. Clark, Sorin Istrail. Journal of Computational Biology. February 2003, 10(1): 13-19. doi:10.1089/106652703763255642.

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Russell Schwartz
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213
Bjarni V. Halldórsson
Celera Genomics, Informatics Research, Rockville, MD 20850
Vineet Bafna
The Center for Advancement of Genetics, Rockville, MD 20850
Andrew G. Clark
Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
Sorin Istrail
Celera Genomics, Informatics Research, Rockville, MD 20850

In this report, we examine the validity of the haplotype block concept by comparing block decompositions derived from public data sets by variants of several leading methods of block detection. We first develop a statistical method for assessing the concordance of two block decompositions. We then assess the robustness of inferred haplotype blocks to the specific detection method chosen, to arbitrary choices made in the block-detection algorithms, and to the sample analyzed. Although the block decompositions show levels of concordance that are very unlikely by chance, the absolute magnitude of the concordance may be low enough to limit the utility of the inference. For purposes of SNP selection, it seems likely that methods that do not arbitrarily impose block boundaries among correlated SNPs might perform better than block-based methods.

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