When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?
Abstract: Allele-sharing statistics for a genetic locus measure the dissimilarity between two populations as a mean of the dissimilarity between random pairs of individuals, one from each population. Owing to within-population variation in genotype, allele-sharing dissimilarities can have the property that they have a nonzero value when computed between a population and itself. We consider the mathematical properties of allele-sharing dissimilarities in a pair of populations, treating the allele frequencies in the two populations parametrically. Examining two formulations of allele-sharing dissimilarity, we obtain the distributions of within-population and between-population dissimilarities for pairs of individuals. We then mathematically explore the scenarios in which, for certain allele-frequency distributions, the within-population dissimilarity – the mean dissimilarity between randomly chosen members of a population – can exceed the dissimilarity between two populations. Such scenarios assist in explaining observations in population-genetic data that members of a population can be empirically more genetically dissimilar from each other on average than they are from members of another population. For a population pair, however, the mathematical analysis finds that at least one of the two populations always possesses smaller within-population dissimilarity than the value of the between-population dissimilarity. We illustrate the mathematical results with an application to human population-genetic data.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself? ; volume:22 ; number:1 ; year:2023 ; extent:24
Statistical applications in genetics and molecular biology ; 22, Heft 1 (2023) (gesamt 24)
- Creator
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Liu, Xiran
Ahsan, Zarif
Martheswaran, Tarun K.
Rosenberg, Noah A.
- DOI
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10.1515/sagmb-2023-0004
- URN
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urn:nbn:de:101:1-2023121113053324637496
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:28 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Liu, Xiran
- Ahsan, Zarif
- Martheswaran, Tarun K.
- Rosenberg, Noah A.
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