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Sifting the wheat from the chaff: prioritizing GWAS results by identifying consistency across analytical methods

Authors

  • Christopher Oldmeadow,

    Corresponding author
    1. School of Medicine and Public Health, University of Newcastle, Newcastle upon Tyne, United Kingdom
    2. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
    • School of Medicine and Public Health, University of Newcastle, Newcastle upon Tyne, UK
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  • Carlos Riveros,

    1. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
    2. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, University of Newcastle, Newcastle upon Tyne, United Kingdom
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  • Elizabeth G. Holliday,

    1. School of Medicine and Public Health, University of Newcastle, Newcastle upon Tyne, United Kingdom
    2. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
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  • Rodney Scott,

    1. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
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  • Pablo Moscato,

    1. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
    2. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, University of Newcastle, Newcastle upon Tyne, United Kingdom
    3. Australian Research Council Centre of Excellence in Bioinformatics, Australia
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  • Jie Jin Wang,

    1. Centre for Vision Research, Department of Ophthalmology, Westmead Millennium Institute, University of Sydney, Sydney, Australia
    2. Centre for Eye Research Australia and Department of Ophthalmology, University of Melbourne, Melbourne, Australia
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  • Paul Mitchell,

    1. Centre for Vision Research, Department of Ophthalmology, Westmead Millennium Institute, University of Sydney, Sydney, Australia
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  • Gabrielle H.S. Buitendijk,

    1. Department Ophthalmology, Department Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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  • Johannes R. Vingerling,

    1. Department Ophthalmology, Department Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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  • Caroline C.W. Klaver,

    1. Department Ophthalmology, Department Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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  • Ronald Klein,

    1. Department of Ophthalmology, University of Wisconsin, Madison, Wisconsin
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  • John Attia

    1. School of Medicine and Public Health, University of Newcastle, Newcastle upon Tyne, United Kingdom
    2. Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, New South Wales, Australia
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Abstract

The curse of multiple testing has led to the adoption of a stringent Bonferroni threshold for declaring genome-wide statistical significance for any one SNP as standard practice. Although justified in avoiding false positives, this conservative approach has the potential to miss true associations as most studies are drastically underpowered. As an alternative to increasing sample size, we compare results from a typical SNP-by-SNP analysis with three other methods that incorporate regional information in order to boost or dampen an otherwise noisy signal: the haplotype method (Schaid et al. [2002] Am J Hum Genet 70:425–434), the gene-based method (Liu et al. [2010] Am J Hum Genet 87:139–145), and a new method (interaction count) that uses genome-wide screening of pairwise SNP interactions. Using a modestly sized case-control study, we conduct a genome-wide association studies (GWAS) of age-related macular degeneration, and find striking agreement across all methods in regions of known associated variants. We also find strong evidence of novel associated variants in two regions (Chromosome 2p25 and Chromosome 10p15) in which the individual SNP P-values are only suggestive, but where there are very high levels of agreement between all methods. We propose that consistency between different analysis methods may be an alternative to increasingly larger sample sizes in sifting true signals from noise in GWAS. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc. 35:745-754, 2011

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