adegenet.package: The adegenet package

Description Details Author(s) References See Also


This package is devoted to the multivariate analysis of genetic markers data. These data can be codominant markers (e.g. microsatellites) or presence/absence data (e.g. AFLP), and have any level of ploidy. 'adegenet' defines three formal (S4) classes:
- genind: a class for data of individuals ("genind" stands for genotypes-individuals).
- genpop: a class for data of groups of individuals ("genpop" stands for genotypes-populations)
- genlight: a class for genome-wide SNP data


For more information about these classes, type "class ? genind", "class ? genpop", or "?genlight".

Essential functionalities of the package are presented througout 4 tutorials, accessible using adegenetTutorial(which="name-below"):
- basics: introduction to the package.
- spca: multivariate analysis of spatial genetic patterns.
- dapc: population structure and group assignment using DAPC.
- genomics: introduction to the class genlight for the handling and analysis of genome-wide SNP data.

Note: In older versions of adegenet, these tutorials were avilable as vignettes, accessible through the function vignette("name-below", package="adegenet"):
- adegenet-basics.
- adegenet-spca.
- adegenet-dapc.
- adegenet-genomics.

Important functions are also summarized below.

adegenet imports data to genind object from the following softwares:
- STRUCTURE: see read.structure
- GENETIX: see read.genetix
- FSTAT: see read.fstat
- Genepop: see read.genepop
To import data from any of these formats, you can also use the general function import2genind.

In addition, it can extract polymorphic sites from nucleotide and amino-acid alignments:
- DNA files: use read.dna from the ape package, and then extract SNPs from DNA alignments using DNAbin2genind.

- protein sequences alignments: polymorphic sites can be extracted from protein sequences alignments in alignment format (package seqinr, see as.alignment) using the function alignment2genind.

The function fasta2DNAbin allows for reading fasta files into DNAbin object with minimum RAM requirements.

It is also possible to read genotypes coded by character strings from a data.frame in which genotypes are in rows, markers in columns. For this, use df2genind. Note that df2genind can be used for any level of ploidy.

SNP data can be read from the following formats:
- PLINK: see function read.PLINK
- .snp (adegenet's own format): see function read.snp

SNP can also be extracted from aligned DNA sequences with the fasta format, using fasta2genlight

adegenet exports data from

Genotypes can also be recoded from a genind object into a data.frame of character strings, using any separator between alleles. This covers formats from many softwares like GENETIX or STRUCTURE. For this, see genind2df.

Also note that the pegas package imports genind objects using the function as.loci.

Several functions allow one to manipulate genind or genpop objects
- genind2genpop: convert a genind object to a genpop
- seploc: creates one object per marker; for genlight objects, creates blocks of SNPs.
- seppop: creates one object per population
- - tab: access the allele data (counts or frequencies) of an object (genind and genpop)
- x[i,j]: create a new object keeping only genotypes (or populations) indexed by 'i' and the alleles indexed by 'j'.
- makefreq: returns a table of allelic frequencies from a genpop object.
- repool merges genoptypes from different gene pools into one single genind object.
- propTyped returns the proportion of available (typed) data, by individual, population, and/or locus.
- selPopSize subsets data, retaining only genotypes from a population whose sample size is above a given level.
- pop sets the population of a set of genotypes.

Several functions allow to use usual, and less usual analyses:
- HWE.test.genind: performs HWE test for all populations and loci combinations
- dist.genpop: computes 5 genetic distances among populations.
- monmonier: implementation of the Monmonier algorithm, used to seek genetic boundaries among individuals or populations. Optimized boundaries can be obtained using optimize.monmonier. Object of the class monmonier can be plotted and printed using the corresponding methods.
- spca: implements Jombart et al. (2008) spatial Principal Component Analysis
- global.rtest: implements Jombart et al. (2008) test for global spatial structures
- local.rtest: implements Jombart et al. (2008) test for local spatial structures
- propShared: computes the proportion of shared alleles in a set of genotypes (i.e. from a genind object)
- propTyped: function to investigate missing data in several ways
- scaleGen: generic method to scale genind or genpop before a principal component analysis
- Hs: computes the average expected heterozygosity by population in a genpop. Classically Used as a measure of genetic diversity.
- find.clusters and dapc: implement the Discriminant Analysis of Principal Component (DAPC, Jombart et al., 2010).
- seqTrack: implements the SeqTrack algorithm for recontructing transmission trees of pathogens (Jombart et al., 2010) .
glPca: implements PCA for genlight objects.
- gengraph: implements some simple graph-based clustering using genetic data. - snpposi.plot and snpposi.test: visualize the distribution of SNPs on a genetic sequence and test their randomness. - adegenetServer: opens up a web interface for some functionalities of the package (DAPC with cross validation and feature selection).

=== GRAPHICS ===
- colorplot: plots points with associated values for up to three variables represented by colors using the RGB system; useful for spatial mapping of principal components.
- loadingplot: plots loadings of variables. Useful for representing the contribution of alleles to a given principal component in a multivariate method.
- scatter.dapc: scatterplots for DAPC results.
- compoplot: plots membership probabilities from a DAPC object.

- hybridize: implements hybridization between two populations.
- haploGen: simulates genealogies of haplotypes, storing full genomes.
glSim: simulates simple genlight objects.

=== DATASETS ===
- H3N2: Seasonal influenza (H3N2) HA segment data.
- dapcIllus: Simulated data illustrating the DAPC.
- eHGDP: Extended HGDP-CEPH dataset.
- microbov: Microsatellites genotypes of 15 cattle breeds.
- nancycats: Microsatellites genotypes of 237 cats from 17 colonies of Nancy (France).
- rupica: Microsatellites genotypes of 335 chamois (Rupicapra rupicapra) from the Bauges mountains (France).
- sim2pop: Simulated genotypes of two georeferenced populations.
- spcaIllus: Simulated data illustrating the sPCA.

For more information, visit the adegenet website using the function adegenetWeb.

Tutorials are available via the command adegenetTutorial.

To cite adegenet, please use the reference given by citation("adegenet") (or see references below).


Thibaut Jombart <>
Developers: Zhian N. Kamvar <>, Caitlin Collins <>, Ismail Ahmed <>, Federico Calboli, Tobias Erik Reiners, Peter Solymos, Anne Cori,
Contributed datasets from: Katayoun Moazami-Goudarzi, Denis Laloë, Dominique Pontier, Daniel Maillard, Francois Balloux.


Jombart T. (2008) adegenet: a R package for the multivariate analysis of genetic markers Bioinformatics 24: 1403-1405. doi: 10.1093/bioinformatics/btn129

Jombart T. and Ahmed I. (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. doi: 10.1093/bioinformatics/btr521

Jombart T, Devillard S and Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11:94. doi:10.1186/1471-2156-11-94

Jombart T, Eggo R, Dodd P, Balloux F (2010) Reconstructing disease outbreaks from genetic data: a graph approach. Heredity. doi: 10.1038/hdy.2010.78.

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity, 101, 92–103.

See adegenet website:

Please post your questions on 'the adegenet forum':

See Also

adegenet is related to several packages, in particular:
- ade4 for multivariate analysis
- pegas for population genetics tools
- ape for phylogenetics and DNA data handling
- seqinr for handling nucleic and proteic sequences
- shiny for R-based web interfaces

adegenet documentation built on July 18, 2021, 1:06 a.m.