Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3

Authors

  • Marsha A. Wilcox,

    Corresponding author
    1. Genetics Program, Division of Graduate Medical Sciences, Department of Medicine, Boston University School of Medicine; Departments of Epidemiology and Biostatistics, Boston University School of Public Health; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
    • Genetics Program, Boston University School of Medicine, 715 Albany St., L-320, Boston, MA 02118
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    • The first three authors contributed equally to this work.

  • Elizabeth W. Pugh,

    1. Center for Inherited Disease Research, Johns Hopkins Medical School, Baltimore, Maryland
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    • The first three authors contributed equally to this work.

  • Heping Zhang,

    1. Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut
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    • The first three authors contributed equally to this work.

  • Xiaoyun Zhong,

    1. Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut
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  • Douglas F. Levinson,

    1. Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
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  • Giulia C. Kennedy,

    1. Genomics Collaborations Department, Affymetrix, Santa Clara, California
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  • Ellen M. Wijsman

    1. Division of Medical Genetics, Department of Medicine, Department of Biostatistics, and Department of Genome Sciences, University of Washington, Seattle, Washington
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Abstract

The papers in presentation groups 1–3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was ∼1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use. Genet. Epidemiol. 29:(Suppl. 1): S7–S28, 2005. © 2005 Wiley-Liss, Inc.

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