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A Bayesian hierarchical model for allele frequencies
Author(s) -
Lockwood John R.,
Roeder Kathryn,
Devlin B.
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/1098-2272(200101)20:1<17::aid-gepi3>3.0.co;2-q
Subject(s) - bayesian probability , allele frequency , divergence (linguistics) , population , computer science , linkage (software) , bayes' theorem , allele , bayesian hierarchical modeling , statistics , biology , mathematics , artificial intelligence , genetics , linguistics , philosophy , demography , sociology , gene
Genetic epidemiological methodologies, such as linkage analysis, often require accurate estimates of allele frequencies. When studies involve multiple sub‐populations with different evolutionary histories, accurate estimates can be difficult to obtain because the number of subjects per sub‐population tends to be limited. Given allele counts for a collection of loci and sub‐populations, we propose a Bayesian hierarchical model that extends existing empirical Bayesian approaches by allowing for explicit inclusion of prior information about both allele frequencies and inter‐population divergence. We describe how such information can be derived from published data and then incorporated into the model via prior distributions for model parameters. By analysis of simulated data, we highlight how the hierarchical model, as implemented in the publicly available program AllDist, combines prior information with the observed data to refine allele frequency estimates. Genet. Epidemiol. 20:17–33, 2001. © 2001 Wiley‐Liss, Inc.