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

Overview

MeSH

  • Algorithms
  • Bayes Theorem
  • Humans
  • Models, Genetic
  • Pedigree
  • Software

MeSH Major

  • Genetic Linkage
  • Quantitative Trait, Heritable

abstract

  • Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data. 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. 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

  • 2010

has subject area

  • Algorithms
  • Bayes Theorem
  • Genetic Linkage
  • Humans
  • Models, Genetic
  • Pedigree
  • Quantitative Trait, Heritable
  • Software

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2928726

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0012307

PubMed ID

  • 20865038

Additional Document Info

start page

  • e12307

volume

  • 5

number

  • 8