Fast, exact linkage analysis for categorical traits on arbitrary pedigree designs

Authors

  • Abra Brisbin,

    Corresponding author
    1. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York
    2. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
    • Harwick 7, Mayo Clinic, 200 First Street SW, Rochester, MN 55905
    Search for more papers by this author
  • Jenifer Cruickshank,

    1. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York
    2. Department of Biological Sciences, State University of New York, Oswego, New York
    Search for more papers by this author
  • N. Sydney Moïse,

    1. Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York
    Search for more papers by this author
  • Teresa Gunn,

    1. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York
    2. McLaughlin Research Institute, Great Falls, Montana
    Search for more papers by this author
  • Carlos D. Bustamante,

    1. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York
    2. Department of Genetics, Stanford University School of Medicine, Stanford, California
    Search for more papers by this author
  • Jason G. Mezey

    1. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York
    2. Department of Genetic Medicine, Weill Cornell Medical College, New York, New York
    Search for more papers by this author

Abstract

Multi-symptom diseases without a consistent continuous measurement of severity may be best understood with a categorical interpretation. In this paper, we present LOCate v.2, a fast, exact algorithm for linkage analysis of all types of categorical traits, both ordinal and nominal. Our method is able to incorporate missing data and analyze complex genealogical structure, including inbreeding loops. LOCate v.2 computes exact likelihoods efficiently through an elimination algorithm, similar to that used by Superlink for binary traits. We compare LOCate v.2 to LOT and QTLlink, two existing methods of linkage analysis for ordinal traits. We find that LOCate v.2 outperforms both methods when used to analyze simulated nominal traits. In addition, LOCate v.2 performs as well as QTLlink on simulated ordinal traits, and better than LOT due to the necessity of cutting large pedigrees for analysis in LOT. To demonstrate the versatility of LOCate v.2, we conduct an ordinal and nominal linkage analysis of ventricular arrhythmias in a large, inbred pedigree of German Shepherd dogs. We find that a trichotomous ordinal or nominal interpretation strengthens the evidence in favor of linkage to a region on chromosome 6, and provides new evidence of linkage to a region on chromosome 11. LOCate v.2 is a unified, fast, and robust method for linkage analysis of ordinal and nominal traits which will be valuable to researchers interested in investigating any type of categorical trait. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc. 35:371-380, 2011

Ancillary