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Genetics, Vol. 159, 699-713, October 2001, Copyright © 2001

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Noah A. Rosenberga, Terry Burkeb, Kari Eloe, Marcus W. Feldmana, Paul J. Freidlinc, Martien A. M. Groenend, Jossi Hillelc, Asko Mäki-Tanilae, Michèle Tixier-Boichardf, Alain Vignalg, Klaus Wimmersh, and Steffen Weigendi
a Department of Biological Sciences, Stanford University, Stanford, California 94305,
b Department of Animal and Plant Sciences, Sheffield University, S10 2TN, United Kingdom,
c Department of Genetics, The Hebrew University of Jerusalem, Faculty of Agriculture, Rehovot 76100, Israel,
d Institute of Animal Sciences, Wageningen Agricultural University, 6700 AH Wageningen, The Netherlands,
e Agricultural Research Centre, Institute of Animal Production, FIN-31600 Jokioinen, Finland,
f Institut National de la Recherche Agronomique, Centre de Recherches de Jouy-en-Josas, 78 352 Jouy-en-Josas Cedex, France,
g Institut National de la Recherche Agronomique, Centre INRA de Toulouse, 31326 Castanet Tolosan, France,
h Institute of Animal Breeding Science, Rheinische Friedrich-Wilhelms-Universitat, D-53012 Bonn, Germany
i Institute for Animal Science and Animal Behaviour, Mariensee, 31535 Neustadt, Germany

Corresponding author: Noah A. Rosenberg, Program in Molecular and Computational Biology, University of Southern California, 1042 W. 36th Pl., DRB 155, Los Angeles, CA 90089-1113., noahr{at}usc.edu (E-mail)

Communicating editor: G. B. GOLDING

We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.




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