@article{jombart_discriminant_2010,
	title = {Discriminant analysis of principal components: a new method for the analysis of genetically structured populations},
	volume = {11},
	issn = {1471-2156},
	shorttitle = {Discriminant analysis of principal components},
	url = {https://doi.org/10.1186/1471-2156-11-94},
	doi = {10.1186/1471-2156-11-94},
	abstract = {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.},
	number = {1},
	urldate = {2022-03-21},
	journal = {BMC Genetics},
	author = {Jombart, Thibaut and Devillard, Sébastien and Balloux, François},
	month = oct,
	year = {2010},
	keywords = {Bayesian Information Criterion, Discriminant Analysis, Genetic Cluster, Principal Component Analysis, Seasonal Influenza},
	pages = {94},
	file = {Full Text PDF:C\:\\Users\\qcarrade\\Zotero\\storage\\UJNL8LLW\\Jombart et al. - 2010 - Discriminant analysis of principal components a n.pdf:application/pdf;Snapshot:C\:\\Users\\qcarrade\\Zotero\\storage\\PQSPT3I7\\1471-2156-11-94.html:text/html},
}

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