Assessing population genetic structure via the maximisation of genetic distance

<p>Abstract</p> <p>Background</p> <p>The 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.</p> <p>Methods</p&g...

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Main Authors: Toro Miguel A, Rodríguez-Ramilo Silvia T, Fernández Jesús
Format: Article
Language:deu
Published: BMC 2009-11-01
Series:Genetics Selection Evolution
Online Access:http://www.gsejournal.org/content/41/1/49
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author Toro Miguel A
Rodríguez-Ramilo Silvia T
Fernández Jesús
author_facet Toro Miguel A
Rodríguez-Ramilo Silvia T
Fernández Jesús
author_sort Toro Miguel A
collection DOAJ
description <p>Abstract</p> <p>Background</p> <p>The 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.</p> <p>Methods</p> <p>In 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 <it>simulated annealing </it>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.</p> <p>Results</p> <p>The 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 <it>F</it><sub><it>ST </it></sub>≥ 0.03, but only STRUCTURE estimates the correct number of clusters for <it>F</it><sub><it>ST </it></sub>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.</p> <p>Conclusion</p> <p>This 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.</p>
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spelling doaj.art-23753560cc9e4a189f2b0251d16458482022-12-22T01:50:05ZdeuBMCGenetics Selection Evolution0999-193X1297-96862009-11-014114910.1186/1297-9686-41-49Assessing population genetic structure via the maximisation of genetic distanceToro Miguel ARodríguez-Ramilo Silvia TFernández Jesús<p>Abstract</p> <p>Background</p> <p>The 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.</p> <p>Methods</p> <p>In 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 <it>simulated annealing </it>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.</p> <p>Results</p> <p>The 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 <it>F</it><sub><it>ST </it></sub>≥ 0.03, but only STRUCTURE estimates the correct number of clusters for <it>F</it><sub><it>ST </it></sub>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.</p> <p>Conclusion</p> <p>This 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.</p>http://www.gsejournal.org/content/41/1/49
spellingShingle Toro Miguel A
Rodríguez-Ramilo Silvia T
Fernández Jesús
Assessing population genetic structure via the maximisation of genetic distance
Genetics Selection Evolution
title Assessing population genetic structure via the maximisation of genetic distance
title_full Assessing population genetic structure via the maximisation of genetic distance
title_fullStr Assessing population genetic structure via the maximisation of genetic distance
title_full_unstemmed Assessing population genetic structure via the maximisation of genetic distance
title_short Assessing population genetic structure via the maximisation of genetic distance
title_sort assessing population genetic structure via the maximisation of genetic distance
url http://www.gsejournal.org/content/41/1/49
work_keys_str_mv AT toromiguela assessingpopulationgeneticstructureviathemaximisationofgeneticdistance
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AT fernandezjesus assessingpopulationgeneticstructureviathemaximisationofgeneticdistance