ArticleGenetics, selection, evolution : GSE2009
Assessing population genetic structure via the maximisation of genetic distance.
Article in Genetics, selection, evolution : GSE, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Genetic characteristics of endemic Prunus tomentosa in China based on microsatellites.BMC plant biology · 2025Article
- Article
- Variability of Cutaneous Leishmaniasis Lesions Is Not Associated with Genetic Diversity ofThe American journal of tropical medicine and hygiene · 2017Article
- High-density molecular characterization and association mapping in Ethiopian durum wheat landraces reveals high diversity and potential for wheat breeding.Plant biotechnology journal · 2016Article
- Improving the inference of population genetic structure in the presence of related individuals.Genetics research · 2014Article
- Large-scale patterns of genetic variation in a female-biased dispersing passerine: the importance of sex-based analyses.PloS one · 2014Article
- Genetic structure and diversity of the endangered fir tree of Lebanon (Abies cilicica Carr.): implications for conservation.PloS one · 2014Article
- Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods.BMC bioinformatics · 2011Article
- Genetic diversity and population structure ofInsectes sociauxArticle
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3 authors.
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Abstract
backgroundThe inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.
methodsIn this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.
resultsThe simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for F(ST) >or= 0.03, but only STRUCTURE estimates the correct number of clusters for F(ST) as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.
conclusionThis new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.
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