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Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation
Emily K. Latcha b✉, Guha Dharmarajana b, Jeffrey C. Glaubitzc d, Olin E. Rhodesa b
- aDepartment of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, 47907, USA
- bPurdue University
- cLaboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
- dUniversity of Wisconsin, Madison
- ✉Corresponding author
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Abstract
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TL;DRThis work used simulated data to evaluate the performance of three Bayesian clustering software programs at levels of population differentiation below FST=0.1, and suggested that FST must be at least 0.05 to reach an assignment accuracy of greater than 97%. (Semantic Scholar)