Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies
Wojcik Jérôme and
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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
Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-18
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.
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