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Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies

Author

Listed:
  • Guedj Mickael

    (Laboratoire Statistique et Génome)

  • Robelin David

    (Laboratoire Statistique et Génome)

  • Hoebeke Mark

    (Laboratoire Statistique et Génome)

  • Lamarine Marc

    (Serono Pharmaceutical Research Institute)

  • Wojcik Jérôme

    (Serono Pharmaceutical Research Institute)

  • Nuel Gregory

    (Laboratoire Statistique et Genome)

Abstract

Genetic epidemiology aims at identifying biological mechanisms responsible for human diseases. Genome-wide association studies, made possible by recent improvements in genotyping technologies, are now promisingly investigated. In these studies, common first-stage strategies focus on marginal effects but lead to multiple-testing and are unable to capture the possibly complex interplay between genetic factors.We have adapted the use of the local score statistic, already successfully applied to analyse long molecular sequences. Via sum statistics, this method captures local and possible distant dependences between markers. Dedicated to genome-wide association studies, it is fast to compute, able to handle large datasets, circumvents the multiple-testing problem and outlines a set of genomic regions (segments) for further analyses. Applied to simulated and real data, our approach outperforms classical Bonferroni and FDR corrections for multiple-testing. It is implemented in a software termed LHiSA for Local High-scoring Segments for Association and available at: http://stat.genopole.cnrs.fr/software/lhisa.

Suggested Citation

  • Guedj Mickael & Robelin David & Hoebeke Mark & Lamarine Marc & Wojcik Jérôme & Nuel Gregory, 2006. "Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-18, September.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:22
    DOI: 10.2202/1544-6115.1192
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    Cited by:

    1. Lagnoux, Agnès & Mercier, Sabine & Vallois, Pierre, 2019. "Probability density function of the local score position," Stochastic Processes and their Applications, Elsevier, vol. 129(10), pages 3664-3689.

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