Abstract
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.
Highlights
The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time
We introduce the Discriminant Analysis of Principal Components (DAPC), a new methodological approach which retains all assets of DA without being burdened by its limitations
Along with the assignment of individuals to clusters, our method provides a visual assessment of betweenpopulation genetic structures, permitting to infer complex patterns such as hierarchical clustering or clines
Summary
The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. 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. Currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. One of the most widely applied approaches is the inference of population structuring with Bayesian clustering methods such as STRUCTURE [1,2] and BAPS [3,4]. To take full advantage of the increase in size and complexity of genetic datasets, fast and flexible exploratory tools are needed
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