Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study
Université Joseph Fourier · Techniques of Informatics and Microelectronics for Integrated Systems Architecture · +2 more institutions
Abstract
Abstract 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.
Citation impact
- FWCI
- 17.45
- Percentile
- 100%
- References
- 28
Authors
4- CCChibiao ChenCorresponding
Université Joseph Fourier, Techniques of Informatics and Microelectronics for Integrated Systems Architecture, Centre Inria de l'Université Grenoble Alpes, Translational Innovation in Medicine and Complexity
- ÉDÉric Durand
Université Joseph Fourier, Techniques of Informatics and Microelectronics for Integrated Systems Architecture
- FFFlorence Forbes
Centre Inria de l'Université Grenoble Alpes
- OFOlivier François
Université Joseph Fourier, Techniques of Informatics and Microelectronics for Integrated Systems Architecture
Topics & keywords
- Cluster analysis
- Inference
- Bayesian probability
- Classification of discontinuities
- Computer science
- Population
- Data mining
- Population structure