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A linear complexity phasing method for thousands of genomes

Abstract

Human-disease etiology can be better understood with phase information about diploid sequences. We present a method for estimating haplotypes, using genotype data from unrelated samples or small nuclear families, that leads to improved accuracy and speed compared to several widely used methods. The method, segmented haplotype estimation and imputation tool (SHAPEIT), scales linearly with the number of haplotypes used in each iteration and can be run efficiently on whole chromosomes.

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Figure 1: Illustration of the model and the associated graphs for a simple example.
Figure 2: Accuracy and computational burden as a function of the number of conditioning states.

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Acknowledgements

We thank all individuals who provided their blood for genetic analyses. This work was funded by Agence Nationale de Recherches sur le SIDA et les Hépatites, by Peptinov and by Centre de Recherches et d'investigations épidermiques et sensorielles. J.M. acknowledges support from the Medical Research Council (G0801823). This study used data generated by the Wellcome Trust Case Control Consortium; a full list of the investigators who contributed to the generation of the data is available at http://www.wtccc.org.uk/. This study also used trio genotype data from a Vietnamese cohort, which was provided by A. Alcais (Institut National de la Santé et de la Recherche Médicale, France), E. Schurr (McGill Centre for the Study of Host Resistance, Canada) and Institut de Génomique (Centre National de Génotypage, France).

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Contributions

O.D. derived the algorithm and carried out all the experiments. J.-F.Z. supervised the research. J.M. gave advice on experiments and the interpretation of results. O.D., J.-F.Z. and J.M. wrote the paper.

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Correspondence to Jean-François Zagury.

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The authors declare no competing financial interests.

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Supplementary Figures 1–3, Supplementary Tables 1–3 and Supplementary Note (PDF 855 kb)

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Delaneau, O., Marchini, J. & Zagury, JF. A linear complexity phasing method for thousands of genomes. Nat Methods 9, 179–181 (2012). https://doi.org/10.1038/nmeth.1785

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