Bayesian Linkage Analysis of Categorical Traits for Arbitrary Pedigree Designs

Abra Brisbin, Myrna M Weissman, Abby J Fyer, Steven P Hamilton, James A Knowles, Carlos D Bustamante and Jason G Mezey

PLOS ONE, 2010, vol. 5, issue 8, 1-8

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.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0012307

DOI: 10.1371/journal.pone.0012307

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