Bayesian linkage analysis of categorical traits for arbitrary pedigree designs. Academic Article uri icon

Overview

abstract

  • BACKGROUND: Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data. METHODOLOGY: We have developed a Bayesian method for Linkage analysis of Ordinal and Categorical traits (LOCate) that can analyze complex genealogical structure for family groups and incorporate missing data. LOCate uses a Gibbs sampling approach to assess linkage, incorporating a simulated tempering algorithm for fast mixing. While our treatment is Bayesian, we develop a LOD (log of odds) score estimator for assessing linkage from Gibbs sampling that is highly accurate for simulated data. LOCate is applicable to linkage analysis for ordinal or nominal traits, a versatility which we demonstrate by analyzing simulated data with a nominal trait, on which LOCate outperforms LOT, an existing method which is designed for ordinal traits. We additionally demonstrate our method's versatility by analyzing a candidate locus (D2S1788) for panic disorder in humans, in a dataset with a large amount of missing data, which LOT was unable to handle. CONCLUSION: LOCate's accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.

publication date

  • August 26, 2010

Research

keywords

  • Genetic Linkage
  • Quantitative Trait, Heritable

Identity

PubMed Central ID

  • PMC2928726

Scopus Document Identifier

  • 77957886388

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0012307

PubMed ID

  • 20865038

Additional Document Info

volume

  • 5

issue

  • 8