Editing Principal component analysis (section)

=== Discriminant analysis of principal components ===

Discriminant analysis of principal components (DAPC) is a multivariate method used to identify and describe clusters of genetically related individuals. Genetic variation is partitioned into two components: variation between groups and within groups, and it maximizes the former. Linear discriminants are linear combinations of alleles which best separate the clusters. Alleles that most contribute to this discrimination are therefore those that are the most markedly different across groups. The contributions of alleles to the groupings identified by DAPC can allow identifying regions of the genome driving the genetic divergence among groups<ref>{{Cite journal|title = Discriminant analysis of principal components: a new method for the analysis of genetically structured populations.|last1=Liao|first1=T.|last2=Jombart|first2=S.|last3=Devillard|first3=F.|last4=Balloux|journal=BMC Genetics|date=2010|volume=11|pages=11:94|doi=10.1186/1471-2156-11-94|pmid = 20950446|pmc=2973851 |doi-access=free }}</ref>
In DAPC, data is first transformed using a principal components analysis (PCA) and subsequently clusters are identified using discriminant analysis (DA).

A DAPC can be realized on R using the package Adegenet. (more info: [https://adegenet.r-forge.r-project.org/ adegenet on the web])