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

Content Provider	Springer Nature : BioMed Central
Author	Jombart, Thibaut Devillard, Sébastien Balloux, François
Abstract	Background The 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. Results We 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. Conclusions Analysis 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.
Related Links	https://bmcgenomdata.biomedcentral.com/counter/pdf/10.1186/1471-2156-11-94.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	27306844
DOI	10.1186/1471-2156-11-94
Journal	BMC Genomic Data
Issue Number	1
Volume Number	11
Language	English
Publisher	BioMed Central
Publisher Date	2010-10-15
Access Restriction	Open
Subject Keyword	Life Sciences Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Genetics and Population Dynamics Principal Component Analysis Discriminant Analysis Bayesian Information Criterion Seasonal Influenza Genetic Cluster
Content Type	Text
Resource Type	Article
Subject	Health Informatics Genetics
Journal Impact Factor	1.9/2023
5-Year Journal Impact Factor	1.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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