title: PCA-based population structure inference with generic clustering algorithms.
abstract: BACKGROUND
  Handling genotype data typed at hundreds of thousands of loci is very time-consuming and it is no exception for population structure inference. Therefore, we propose to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms.
  
  
  RESULTS
  We investigated K-means, soft K-means and spectral clustering and made comparison to STRUCTURE, a model-based algorithm specifically designed for population structure inference. Moreover, we investigated methods for predicting the number of subpopulations in a population. The results on four simulated datasets and two real datasets indicate that our approach performs comparably well to STRUCTURE. For the simulated datasets, STRUCTURE and soft K-means with BIC produced identical predictions on the number of subpopulations. We also showed that, for real dataset, BIC is a better index than likelihood in predicting the number of subpopulations.
  
  
  CONCLUSIONS
  Our approach has the advantage of being fast and scalable, while STRUCTURE is very time-consuming because of the nature of MCMC in parameter estimation. Therefore, we suggest choosing the proper algorithm based on the application of population structure inference.
authors.affiliation: Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA. chih.lee@uconn.edu
authors.forename: Chih
authors.initials: C
authors.lastname: Lee
authors.forename: Ali
authors.initials: A
authors.lastname: Abdool
authors.forename: Chun-Hsi
authors.initials: CH
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005828:Genetics, Population / Q000379:methods*; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D044382:Population Groups / Q000235:genetics*