title: A Bayesian hierarchical model for allele frequencies.
abstract: 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.
authors: Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors: J R
authors: JR
authors: Lockwood
authors: K
authors: K
authors: Roeder
authors: B
authors: B
authors: Devlin
mesh_terms: D000483:Alleles; D001499:Bayes Theorem; D003198:Computer Simulation; D005787:Gene Frequency; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic