Original Article

Variance-components methods for linkage and association analysis of ordinal traits in general pedigrees

  1. G. Diao1,*,
  2. D. Y. Lin2

Article first published online: 13 NOV 2009

DOI: 10.1002/gepi.20453

Issue

Genetic Epidemiology

Genetic Epidemiology

Volume 34, Issue 3, pages 232–237, April 2010

Additional Information

How to Cite

Diao, G. and Lin, D. Y. (2010), Variance-components methods for linkage and association analysis of ordinal traits in general pedigrees. Genetic Epidemiology, 34: 232–237. doi: 10.1002/gepi.20453

Author Information

  1. 1

    Department of Statistics, George Mason University, Fairfax, Virginia

  2. 2

    Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina

*Department of Statistics, George Mason University, MS 4A7, 4400 University Drive, Fairfax, VA 22030–4444

Publication History

  1. Issue published online: 15 MAR 2010
  2. Article first published online: 13 NOV 2009
  3. Manuscript Accepted: 30 JUL 2009
  4. Manuscript Revised: 8 JUL 2009
  5. Manuscript Received: 22 APR 2009

Funded by

  • National Institutes of Health. Grant Number: GM31575

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Keywords:

  • complex diseases;
  • family studies;
  • IBD sharing;
  • LOD score;
  • maximum likelihood;
  • probit model;
  • SNPs

Abstract

Many complex human diseases such as alcoholism and cancer are rated on ordinal scales. Well-developed statistical methods for the genetic mapping of quantitative traits may not be appropriate for ordinal traits. We propose a class of variance-component models for the joint linkage and association analysis of ordinal traits. The proposed models accommodate arbitrary pedigrees and allow covariates and gene-environment interactions. We develop efficient likelihood-based inference procedures under the proposed models. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. An application to data from the Collaborative Study on the Genetics of Alcoholism is provided. Genet. Epidemiol. 34: 232–237, 2010.  © 2009 Wiley-Liss, Inc.

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