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

Chibiao Chen (1, 2) , Eric Durand (3) , Florence Forbes (2) , Olivier Francois (3)

1 TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
3 TIMB

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.

Mots clés: Bayesian clustering; computer programs; population membership; relative performances; spatial assignment methods; spatial genetics

Domaines: Génétique des populations [q-bio.PE]

Chibiao Chen, Eric Durand, Florence Forbes, Olivier Francois. Bayesian clustering algorithms ascertaining spatial population structure: A new computer program and a comparison study. Molecular Ecology Notes, 2007, 7 (5), pp.747-756. ⟨10.1111/j.1471-8286.2007.01769.x⟩. ⟨hal-00370267⟩