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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308437 |
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Title:
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On Detecting Stabilizing or Divergent Selection Using Patterns of Variation at SNP Loci
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Author(s):
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Feng Guo*+ and Dipak Dey and Kent Holsinger
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut
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Address:
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753 C Mix Ave, Hamden, CT, 06514,
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Keywords:
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Bayesian approach ; Hierarchical model ; MCMC ; SNP ; Wright's Fst
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Abstract:
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We propose several Bayesian hierarchical models to estimate the genetic variations among populations, which is conveniently measured by the Wright's Fst, using single nucleotide polymorphism data from the HapMap project. The posterior distributions of the model parameters are estimated using MCMC simulations and the optimal model is selected using DIC and LPML. To detect loci for which among population variations are not well explained by the common Fst, we use Kullback-Leibler divergence measure (KLD) to measure the divergence between the posterior distributions and the common Fst and calibrate the KLD results using a coin flip experiment. With this method, we identify 15 SNP loci with unusually large values of Fst and we find 10 out of the 15 are located either within identified genes or nearby.
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