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Supplementary data for published paper

Accuracy of Haplotype Reconstruction from Haplotype-Tagging Single-Nucleotide Polymorphisms

Julian Forton, Dominic Kwiatkowski, Kirk Rockett, Gaia Luoni, Martin Kimber, and Jeremy Hull

Am. J. Hum. Genet., 76:438-448, 2005

Many investigators are now using haplotype-tagging SNPs (htSNPs) as a way of screening regions of the genome for association with disease. A common approach is to genotype htSNPs in a study population, and to use this information to draw inferences about each individual's haplotypic makeup including SNPs that were not directly genotyped. To test the validity of this approach we simulated the exercise of typing htSNPs in a large sample of individuals, and compared the true and inferred haplotypes. The accuracy of haplotype inference varied, depending on the method of selecting htSNPs, the LD structure of the region, and the amount of missing data. At the stage of selecting htSNPs, haplotype block-based methods required a larger number of htSNPs than unstructured methods, but gave lower levels of error in haplotype inference, particularly when there was a significant amount of missing data. We present a web-based utility that allows investigators to compare the likely error rates of different sets of htSNPs, and to arrive at an economical set of htSNPs that provides acceptable levels of accuracy in haplotype inference.

A brief overview of the error rate application

Browse regional patterns of LD for the 4 study datasets (relating to manuscript fig 4):

Browse and actively interrogate the error rate analyses for the 4 study datasets (relating to manuscript fig 7):




Use the error rate application to optimise htSNP selection for your own haplotype data