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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...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928726/ https://www.ncbi.nlm.nih.gov/pubmed/20865038 http://dx.doi.org/10.1371/journal.pone.0012307 |
Sumario: | 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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