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Bioinformatics Advance Access published online on December 7, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti178
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received May 19, 2004
Revised October 13, 2004
Accepted November 22, 2004

Article

Bayesian models for the analysis of genetic structure when populations are correlated

Rongwei Fu 1*, Dipak K. Dey 1, and Kent E. Holsinger 2

1 Department of Statistics, U-4120, University of Connecticut, Storrs, CT 06269-4120
2 Department of Ecology & Evolutionary Biology, U-3043, University of Connecticut, Storrs, CT 06269-3043

* To whom correspondence should be addressed.
Rongwei Fu, E-mail: fur{at}ohsu.edu


  Abstract

Motivation: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among population correlations can be very high, which could affect estimates of population genetic structure. In this study we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations.

Results: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a data set of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to overestimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework.


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