Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Authors

  • Lin T. Guey,

    1. Applied Quantitative Genotherapeutics, Pfizer Biotherapeutics, Cambridge, Massachusetts
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  • Jasmina Kravic,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Olle Melander,

    1. Lund University Diabetes Centre, Clinical Research Centre, Malmö University Hospital, Lund University, Sweden
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  • Noël P. Burtt,

    1. The Broad Institute of Harvard and the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts
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  • Jason M. Laramie,

    1. Applied Quantitative Genotherapeutics, Pfizer Biotherapeutics, Cambridge, Massachusetts
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  • Valeriya Lyssenko,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Anna Jonsson,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Eero Lindholm,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Tiinamaija Tuomi,

    1. Department of Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
    2. Folkhälsan Research Center, Helsinki, Finland
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  • Bo Isomaa,

    1. Folkhälsan Research Center, Helsinki, Finland
    2. Malmska Municipal Health Center and Hospital, Jakobstad, Finland
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  • Peter Nilsson,

    1. Department of Clinical Sciences, Medicine, Lund University, Malmö, Sweden
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  • Peter Almgren,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Sekar Kathiresan,

    1. The Broad Institute of Harvard and the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts
    2. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts
    3. Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts
    4. Department of Medicine, Harvard Medical School, Boston, Massachusetts
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  • Leif Groop,

    1. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, Malmö, Sweden
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  • Albert B. Seymour,

    1. Applied Quantitative Genotherapeutics, Pfizer Biotherapeutics, Cambridge, Massachusetts
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  • David Altshuler,

    1. The Broad Institute of Harvard and the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts
    2. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts
    3. Department of Medicine, Harvard Medical School, Boston, Massachusetts
    4. Department of Genetics, Molecular Biology, Harvard Medical School, Boston, Massachusetts
    5. Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
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  • Benjamin F. Voight

    Corresponding author
    1. The Broad Institute of Harvard and the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts
    2. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts
    3. Department of Medicine, Harvard Medical School, Boston, Massachusetts
    • The Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge MA 02144.
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Abstract

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol. 35: 236-246, 2011.  © 2011 Wiley-Liss, Inc.

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