title: Inferring weak population structure with the assistance of sample group information.
abstract: Genetic clustering algorithms require a certain amount of data to produce informative results. In the common situation that individuals are sampled at several locations, we show how sample group information can be used to achieve better results when the amount of data is limited. New models are developed for the structure program, both for the cases of admixture and no admixture. These models work by modifying the prior distribution for each individual's population assignment. The new prior distributions allow the proportion of individuals assigned to a particular cluster to vary by location. The models are tested on simulated data, and illustrated using microsatellite data from the CEPH Human Genome Diversity Panel. We demonstrate that the new models allow structure to be detected at lower levels of divergence, or with less data, than the original structure models or principal components methods, and that they are not biased towards detecting structure when it is not present. These models are implemented in a new version of structure which is freely available online at http://pritch.bsd.uchicago.edu/structure.html.
authors.affiliation: Department of Human Genetics, Department of Statistics, and Howard Hughes Medical Institute, University of Chicago, Chicago, IL 60637, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA, Environmental Research Institute, Department of Microbiology, University College Cork, Cork, Ireland.
authors.forename: Melissa J
authors.initials: MJ
authors.lastname: Hubisz
authors.forename: Daniel
authors.initials: D
authors.lastname: Falush
authors.forename: Matthew
authors.initials: M
authors.lastname: Stephens
authors.forename: Jonathan K
authors.initials: JK
authors.lastname: Pritchard