Estimation of Effective Population Size and Migration Rate From One- and Two-Locus Identity Measures

Standard methods for inferring demographic parameters from genetic data are based mainly on one-locus theory. However, the association of genes at different loci (e.g., two-locus identity disequilibrium) may also contain some information about demographic parameters of populations. In this article,...

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Bibliographic Details
Published in:Genetics (Austin) 2001-02, Vol.157 (2), p.911-925
Main Authors: Vitalis, Renaud, Couvet, Denis
Format: Article
Language:English
Subjects:
Alleles
Animals
Bias
Bias (Epidemiology)
Demographics
Drosophila
Emigration and Immigration
Evolution
Gene Frequency
Genetic Markers
Genetic Variation
Genetics
Genetics, Population
Haploidy
Humans
Inbreeding
Life Sciences
Migration
Models, Genetic
Models, Statistical
Models, Theoretical
Mutation
Polymorphism, Genetic
Population
Populations and Evolution
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Summary:Standard methods for inferring demographic parameters from genetic data are based mainly on one-locus theory. However, the association of genes at different loci (e.g., two-locus identity disequilibrium) may also contain some information about demographic parameters of populations. In this article, we define one- and two-locus parameters of population structure as functions of one- and two-locus probabilities for the identity in state of genes. Since these parameters are known functions of demographic parameters in an infinite island model, we develop moment-based estimators of effective population size and immigration rate from one- and two-locus parameters. We evaluate this method through simulation. Although variance and bias may be quite large, increasing the number of loci on which the estimates are derived improves the method. We simulate an infinite allele model and a K allele model of mutation. Bias and variance are smaller with increasing numbers of alleles per locus. This is, to our knowledge, the first attempt of a joint estimation of local effective population size and immigration rate.
ISSN:0016-6731
1943-2631
1943-2631
DOI:10.1093/genetics/157.2.911