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

By Thibaut Jombart, Sébastien Devillard, François Balloux. BMC Genetics, 2010

500 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Genetics

View Paper: https://www.semanticscholar.org/paper/d07e265b685269dd2d324246525edc77db5d441e

Citations in the corpus

Fast hierarchical Bayesian analysis of population structure (2018, unknown): “However, the ability of methods to fit a clustering model to these alignments has not increased in step with these increases in alignment size (6,37), with even modestly sized datasets requiring gene-by-gene or distance based approaches to determine clusters (5,52,53).”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, unknown): “This is the case of Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA, see [11,12] for a supervised version), Partial Least Squares Regression (PLS, [13], see also [14-16] for discrimination purposes), to explain most of the variance/covariance structure of the data using linear ”
Inference of Population Structure using Dense Haplotype Data (2012, unknown): “[2], [11]–[17]).”
Directional genetic differentiation and relative migration (2013, unknown): “2000), ADEGENET (Jombart 2008), or DAPC (Jombart et al. 2010).”
Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality (2015, unknown): “…utility of minimum spanning networks with reticulation, we used two clonal data sets: the H3N2 ﬂu virus data from the adegenet package using years of each epidemic as the population factor, and Phytophthora ramorum data from Nurseries and Oregon forests (Jombart et al., 2010; Kamvar et al., 2014a).”