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Linkage analysis without defined pedigrees

Authors

  • Aaron G. Day-Williams,

    1. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
    2. Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
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  • John Blangero,

    1. Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas
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  • Thomas D. Dyer,

    1. Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas
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  • Kenneth Lange,

    1. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
    2. Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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  • Eric M. Sobel

    Corresponding author
    1. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
    • Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095-7088
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Abstract

The need to collect accurate and complete pedigree information has been a drawback of family-based linkage and association studies. Even in case-control studies, investigators should be aware of, and condition on, familial relationships. In single nucleotide polymorphism (SNP) genome scans, relatedness can be directly inferred from the genetic data rather than determined through interviews. Various methods of estimating relatedness have previously been implemented, most notably in PLINK. We present new fast and accurate algorithms for estimating global and local kinship coefficients from dense SNP genotypes. These algorithms require only a single pass through the SNP genotype data. We also show that these estimates can be used to cluster individuals into pedigrees. With these estimates in hand, quantitative trait locus linkage analysis proceeds via traditional variance components methods without any prior relationship information. We demonstrate the success of our algorithms on simulated and real data sets. Our procedures make linkage analysis as easy as a typical genomewide association study. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc. 35:360-370, 2011

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