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Originally published as Genetics Published Articles Ahead of Print on December 22, 2008.
Genetics, Vol. 181, 1013-1019, March 2009, Copyright © 2009
doi:10.1534/genetics.108.098194
Characterization of Demographic Expansions From Pairwise Comparisons of Linked Microsatellite Haplotypes
Miguel Navascués*,
,1,
Olivier J. Hardy and
Concetta Burgarella*,
* Équipe Éco-Évolution Mathématique, CNRS UMR 7625 Écologie et Évolution, Université Pierre et Marie Curie and École Normale Supérieure, 75230 Paris, France, Service d'Éco-Éthologie Évolutive, Université Libre de Bruxelles, 1050 Bruxelles, Belgium and Unidad de Genética Forestal, Departamento de Sistemas y Recursos Forestales, Instituto Nacional para la Investigación Agraria y Alimentaria (CIFOR-INIA), 28040 Madrid, Spain
1 Corresponding author: Équipe Éco-Évolution Mathématique, UMR 7625 Écologie et Évolution, École Normale Supérieure, 46 rue d'Ulm, 75230 Paris Cedex 05, France.
E-mail: m.navascues{at}gmail.com
This work extends the methods of demographic inference based on the distribution of pairwise genetic differences between individuals (mismatch distribution) to the case of linked microsatellite data. Population genetics theory describes the distribution of mutations among a sample of genes under different demographic scenarios. However, the actual number of mutations can rarely be deduced from DNA polymorphisms. The inclusion of mutation models in theoretical predictions can improve the performance of statistical methods. We have developed a maximum-pseudolikelihood estimator for the parameters that characterize a demographic expansion for a series of linked loci evolving under a stepwise mutation model. Those loci would correspond to DNA polymorphisms of linked microsatellites (such as those found on the Y chromosome or the chloroplast genome). The proposed method was evaluated with simulated data sets and with a data set of chloroplast microsatellites that showed signal for demographic expansion in a previous study. The results show that inclusion of a mutational model in the analysis improves the estimates of the age of expansion in the case of older expansions.
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