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Activity Number: 332
Type: Contributed
Date/Time: Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #310329
Title: Hierarchical Mixed Membership Models for Honey Bee Genomes
Author(s): Tanzy Love*+
Companies: Carnegie Mellon University
Address: Department of Statistics, Pittsburgh, PA, 15213,
Keywords: mixed membership ; soft clustering ; honey bees ; bayesian ; dirichlet process prior
Abstract:

In the New World, all existing Apis mellifera (honey bees) are introduced from existing (different) populations in the Old World. One effect of this is the prevalence of European honey bees in the north and African "killer" honey bees in south (these frighten some by moving). Using 1136 SNPs genotyped in 341 individuals, Whitfield et al. (2006) characterized both native and introduced populations of honey bees over time. In their paper, they clustered native genomes into four genetic classes, and then compared introduced genomes to these clusters to measure the change in the honey bee gene pools in the Americas over time. We propose a mixed membership model for honey bee genomes where any particular bee's genome is a mixture of several pure types. Using a Dirichlet process prior, we can estimate the true number of pure types along with the mixture in each of the observed individuals.


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