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Inferring weak population structure with the assistance of sample group information
Author(s) -
HUBISZ MELISSA J.,
FALUSH DANIEL,
STEPHENS MATTHEW,
PRITCHARD JONATHAN K.
Publication year - 2009
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/j.1755-0998.2009.02591.x
Subject(s) - biology , cluster analysis , divergence (linguistics) , population structure , sample (material) , population , microsatellite , sample size determination , evolutionary biology , artificial intelligence , data mining , statistics , computer science , genetics , mathematics , linguistics , chemistry , demography , allele , chromatography , sociology , gene , philosophy
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.