2010 Articles
Bayesian Linkage Analysis Of Categorical Traits For Arbitrary Pedigree Designs
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.
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- Brisbin et al. - 2010 - Bayesian Linkage Analysis of Categorical Traits fo.pdf application/pdf 537 KB Download File
Also Published In
- Title
- PLoS ONE
- DOI
- https://doi.org/10.1371/journal.pone.0012307
More About This Work
- Academic Units
- Epidemiology
- Psychiatry
- Published Here
- February 1, 2022