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PLoS One. 2010 Aug 26;5(8):e12307. doi: 10.1371/journal.pone.0012307.

Bayesian linkage analysis of categorical traits for arbitrary pedigree designs.

Author information

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA. agb36@cornell.edu

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.

PMID:
20865038
[PubMed - indexed for MEDLINE]
PMCID:
PMC2928726
Free PMC Article

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