Bayesian clustering algorithms ascertaining spatial population structure:: a new computer program and a comparison study

被引:586
作者
Chen, Chibiao
Durand, Eric
Forbes, Florence
Francois, Olivier
机构
[1] INRIA Rhone Alpes, F-38334 Montbonnot St Martin, St Ismier, France
[2] Univ Grenoble 1, Inst Natl Polytech Grenoble, TIMC, F-38031 Grenoble 1, France
来源
MOLECULAR ECOLOGY NOTES | 2007年 / 7卷 / 05期
关键词
Bayesian clustering; computer programs; population membership; relative performances; spatial assignment methods; spatial genetics;
D O I
10.1111/j.1471-8286.2007.01769.x
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
On the basis of simulated data, this study compares the relative performances of the Bayesian clustering computer programs STRUCTURE, GENELAND, GENECLUST and a new program named TESS. While these four programs can detect population genetic structure from multilocus genotypes, only the last three ones include simultaneous analysis from geographical data. The programs are compared with respect to their abilities to infer the number of populations, to estimate membership probabilities, and to detect genetic discontinuities and clinal variation. The results suggest that combining analyses using TESS and STRUCTURE offers a convenient way to address inference of spatial population structure.
引用
收藏
页码:747 / 756
页数:10
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