Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

BMC Genetics

Methodology Article

Thibaut Jombart¹  François Balloux¹  Sébastien Devillard² 

[1] MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College Faculty of Medicine, St Mary's Campus, Norfolk Place, W2 1PG, London, UK;UMR 5558 - LBBE "Biométrie et Biologie évolutive" Bât. Grégor Mendel, Université de Lyon, Université Lyon1, 43 bd du 11 novembre 1918, 69622, Villeurbanne cedex, France;

关键词: Principal Component Analysis;    Discriminant Analysis;    Bayesian Information Criterion;    Seasonal Influenza;    Genetic Cluster;   

DOI  :  10.1186/1471-2156-11-94

 received in 2010-06-22, accepted in 2010-10-15,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations.ResultsWe introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza.ConclusionsAnalysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.

【 授权许可】

CC BY   
© Jombart et al; licensee BioMed Central Ltd. 2010