Testing Differentiation in Diploid Populations

Abstract

We examine the power of different exact tests of differentiation for diploid populations. Since there is not necessarily random mating within populations, the appropriate hypothesis to construct exact tests is that of independent sampling of genotypes. There are two categories of tests, FST-estimator tests and goodness of fit tests. In this latter category, we distinguish “allelic statistics”, which account for the nature of alleles within genotypes, from “genotypic statistics” that do not. We show that the power of FST-estimator tests and of allelic goodness of fit tests are similar when sampling is balanced, and higher than the power of genotypic goodness of fit tests. When sampling is unbalanced, the most powerful tests are shown to belong to the allelic goodness of fit group.

Keywords

Goodness of fitBiologyPloidyStatisticsEstimatorAlleleGenotypeGeneticsSampling (signal processing)Statistical hypothesis testingEvolutionary biologyMathematicsGeneComputer science

MeSH Terms

AllelesAnimalsDiploidyGeneticsPopulationGenotypeHumansModelsGenetic

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Publication Info

Year: 1996
Type: article
Volume: 144
Issue: 4
Pages: 1933-1940
Citations: 1307
Access: Closed

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APA Style

                            
                                
                                    Jérôme Goudet, 
                                
                                    Michel Raymond, 
                                
                                    Thierry de Meeûs
                                
                                et al.
                            
                            (1996). 
                            Testing Differentiation in Diploid Populations. 
                            Genetics
                            , 144
                            (4)
                            , 1933-1940.
                            https://doi.org/10.1093/genetics/144.4.1933
                        

Identifiers

DOI: 10.1093/genetics/144.4.1933
PMID: 8978076
PMCID: PMC1207740

Data Quality

Data completeness: 86%