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doi:10.1534/genetics.106.055350
A more recent version of this article appeared on August 1, 2006.
REGULAR RESEARCH PAPERS |
An integrated likelihood method for estimating genetic differentiation between populations
Toshihide Kitakado 1*, Shuichi Kitada 1, Hirohisa Kishino 2 and Hans Julius Skaug 3
1 Tokyo University of Marine Science and Technology
2 University of Tokyo
3 University of Bergen
* To whom correspondence should be addressed. E-mail: kitakado{at}s.kaiyodai.ac.jp.
Submitted on January 3, 2006
Revised on March 29, 2006
Accepted on 18 May 2006
The aim of this paper is to develop an integrated likelihood (IL) approach to estimate the genetic differentiation between populations. The conventional maximum likelihood (ML) and pseudolikelihood (PL) methods that use sample counts of alleles may cause severe underestimations of FST, which means overestimations of 
=4Nm, when the number of sampling localities is small. To reduce such bias in the estimation of genetic differentiation, we propose an IL method in which the mean allele frequencies over populations are regarded as nuisance parameters and are eliminated by integration. To maximize the IL function, we have developed two algorithms, a Monte Carlo EM algorithm and a Laplace approximation. Our simulation studies show that the method proposed here outperforms the conventional ML and PL methods in terms of unbiasedness and precision. The IL method was applied to real data for Pacific herring and African elephants.
Key Words: Dirichlet-multinomial, FST, Genetic differentiation, Integrated likelihood, Metapopulations
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