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Bayesian Correction for SNP Ascertainment Bias

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3885))

Abstract

Genomewide analysis of linkage disequilibrium (LD) is commonly based in the maximum likelihood estimator. This estimator of LD suffers of a well known bias toward disequilibrium that becomes particularly serious in small samples with SNPs that are not very common in the population. Algorithms able to identify LD patterns, such as haplotype blocks or LD decay maps do a non-random selection of SNPs to be included in the analysis in order to remove this bias. However, they introduce ascertainment bias that can mask the real decay of disequilibrium in the population, with several consequences on the validity and reproducibility of genetic studies. In this work, we use a new Bayesian estimator of LD that greatly reduces the effect of ascertainment bias in the inference of LD decay. We also provide a software that use the Bayesian estimator to compute pairwise LD from SNP samples.

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Author information

Authors and Affiliations

  1. Software Engineering Department, University of Granada, Granada, 18071, Spain

    María M. Abad-Grau

  2. Department of Biostatistics, Boston University, Boston, MA, 02118, USA

    Paola Sebastiani

Authors
  1. María M. Abad-Grau
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  2. Paola Sebastiani
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, Catalonia, 08193, Bellaterra, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, 186-0004, Tokyo, Kunitachi, Japan

    Yasuo Narukawa

  3. Department of Computer Science and Mathematics Intelligent Technologies for Advanced Knowledge Acquisition Research Group,, University Rovira i Virgili, Av. Països Catalans, 26., 43007, Tarragona, Catalonia, Spain

    Aïda Valls

  4. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

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© 2006 Springer-Verlag Berlin Heidelberg

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Abad-Grau, M.M., Sebastiani, P. (2006). Bayesian Correction for SNP Ascertainment Bias. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_26

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  • DOI: https://doi.org/10.1007/11681960_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32780-6

  • Online ISBN: 978-3-540-32781-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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