PCA-based population structure inference with generic clustering algorithms

• Published in: BMC bioinformatics Vol. 10; no. Suppl 1; p. S73
• Main Authors: Lee, Chih, Abdool, Ali, Huang, Chun-Hsi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.01.2009; BMC
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Cluster Analysis; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Genetics, Population - methods; Genome, Human; Genotype; Humans; Life Sciences; Microarrays; Population Groups - genetics; Genotype Data; Spectral Cluster; Markov Chain Monte Carlo; Cluster Algorithm; Real Dataset
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-10-S1-S73

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. Conclusion 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.