Arlequin

Arlequin
manual

Arlequin ver 3.5 (released 24 February 2010)
The goal of Arlequin is to provide the average user in population genetics with quite a large set of basic methods and statistical tests, in order to extract information on genetic and demographic features of a collection of population samples.
The analyses Arlequin can perform on the data fall into two main categories: intra-population and inter-population methods.
Computes indices of genetic diversity, F-statistics and genetic distances between populations; exact test of HWE, LD and population differentiation; tests selective neutrality within populations; Mantel test; estimates gametic phase from multilocus genotypes; estimated demographic parameters form mismatch distribution

• program written in C++
• Windows version (XP, Vista, 7)
• LINUX

• DNA sequences
• RFLP
• SNP
• Microsattelite
• Standard data
• Allele frequency data

In haplotypic form:

• haplotypes (i.e. combination of alleles at one or more loci)
• haploid/diploid

in genotypic form:

• genotypes
• diploid
• known/unknown gametic phase
• recessive/no recessive alleles

Contain the description of the properties of the data, as well as the raw data themselves. The input files should have a “*.arp” extension (for ARlequin Project).

structured into two main sections:

• Profile section (mandatory)
• Data section (mandatory):
  • Haplotype list (optional)
  • Distance matrices (optional)
  • Samples (mandatory)
  • Genetic structure (optional)
  • Mantel tests (optional)

The following small example is a project file containing four populations. The data type is STANDARD genotypic data with unknown gametic phase:

[Profile] 
Title="Fake HLA data" 
  NbSamples=4 
  GenotypicData=1 
  GameticPhase=0 
  DataType=STANDARD 
  LocusSeparator=WHITESPACE 
  MissingData='?' 
[Data] 
[[Samples]] 
  SampleName="A sample of 6 Algerians" 
  SampleSize=6 
  SampleData={ 
   1 1 1104 0200 
       0700 0301 
   3 3 0302 0200 
       1310 0402 
   4 2 0402 0602 
       1502 0602 
  } 
  SampleName="A sample of 11 Bulgarians" 
  SampleSize=11 
  SampleData={ 
   1 1 1103 0301 
       0301 0200 
   2 4 1101 0301 
       0700 0200 
   3 1 1500 0502 
       0301 0200 
   4 1 1103 0301 
       1202 0301 
   5 1 0301 0200 
       1500 0601 
   6 3 1600 0502 
       1301 0603 
  } 
  SampleName="A sample of 12 Egyptians" 
  SampleSize=12 
  SampleData={ 
   1 2 1104 0301 
       1600 0502 
   3 1 1303 0301 
       1101 0502 
   4 3 1502 0601 
       1500 0602 
   6 1 1101 0301 
       1101 0301 
   8 4 1302 0502 
       1101 0609 
   9 1 1500 0302 
   0402 0602 
  } 
  SampleName="A sample of 8 French" 
  SampleSize=8 
  SampleData={ 
   219 1 0301 0200 
         0101 0501 
   239 2 0301 0200 
         0301 0200 
   249 1 1302 0604 
         1500 0602 
   250 3 1401 0503 
         1301 0603 
   254 1 1302 0604 
  } 
[[Structure]] 
  StructureName="My population structure" 
  NbGroups=2 
  Group={ 
   "A sample of 6 Algerians" 
   "A sample of 12 Egyptians" 
  } 
  Group={ 
   "A sample of 11 Bulgarians" 
   "A sample of 8 French" 
  } 

• Title (string within “”): Title=”title xy”
• Number of samples (int 1-1000): NbSamples =3
• Type of data (DNA, RFLP, MICROSAT, STANDARD, FREQUENCY): DataType = DNA
• Haplotypic/genotypic data (0/1): GenotypicData = 0

• Optionally (default value):
  • locus separator (WHITESPACE, TAB, NONE, …): LocusSeparator = TAB
  • gametic phase known/unknown (1/0): GameticPhase = 1
  • recessive/ co-dominant allele (1/0): RecessiveData = 1
  • code for recessive allele (string within “null”): RecessiveAllel =”xxx”
  • code for missing data (character within “?” or ‘?’): MissingData = ‘$’
  • frequencies as absolute/relative values (ABS/REL): Frequency = ABS
  • significant digits for haplotype frequency outputs (real number 1e-2 – 1e-7(1e-5)): FrequencyThreshold = 0.00001
  • convergence criterion for the EM algorithm (real number 1e-7 – 1e-12): EpsilonValue = 1e-10

define list of haplotypes (intern or extern)

• intern:

[[HaplotypeDefinition]] #start the section of Haplotype definition 
  HaplListName="list1"  #give any name you whish to this list 
  HaplList={ 
   h1 A T              #on each line, the name of the haplotype is 
   h2 G C              # followed by its definition. 
   h3 A G 
   h4 A A 
   h5 G G 
  } 

• extern:

[[HaplotypeDefinition]] #start the section of Haplotype definition 
  HaplListName="list1"  #give any name you whish to this list 
  HaplList = EXTERN "hapl_file.hap" 

matrix of genetic distances between haplotypes can be specified (intern or extern)

• intern:

[[DistanceMatrix]]      #start the distance matrix definition section 
   MatrixName= "none"  # name of the distance matrix 
   MatrixSize= 4        # size = number of lines of the distance matrix 
   MatrixData={        
     h1 h2 h3 h4        # labels of the distance matrix (identifier of the 
     0.00000            # haplotypes) 
     2.00000 0.00000 
     1.00000 2.00000 0.00000 
     1.00000 2.00000 1.00000 0.00000 
   } 

• extern:

[[DistanceMatrix]]      #start the distance matrix definition section 
   MatrixName= "none"  # name of the distance matrix 
   MatrixSize= 4        # size = number of lines of the distance matrix 
   MatrixData= EXTERN "mat_file.dis" 

Defines haplotypic/genotypic content of the different samples

• name for each sample (string within “”): SampleName = “name xy”
• size of sample (int value): SampleSize = 732
• data itself (list of haplotypes or genotypes and their frequencies, entered with braces):

[[Samples]]              #start the samples definition section 
  SampleData={ 
   id1 1  ACGGTGTCGA 
   id2 2  ACGGTGTCAG 
   id3 8  ACGGTGCCAA 
   id4 10 ACAGTGTCAA 
   id5 1  GCGGTGTCAA 
  } 

frequency data:

SampleData={ 
  id1 1 
  id2 2 
  id3 8 
  id4 10 
  id5 1 
}

• haplotypic data: for each haplotype its identifier and sample frequency (no haplotype list has been defined: also allelic content of the haplotype)

• genotypic data: for each genotype its identifier, sample frequency, allelic content (on two separate lines). As list of genotypes or list of individuals.

Id1 2  ACTCGGGTTCGCGCGC  # the first pseudo-haplotype 
       ACTCGGGCTCACGCGC  # the second pseudo-haplotype 

or

my_id 4    0 0 1 1 0 1 
           0 1 0 0 1 1 

specifies the hierarchical genetic structure of the samples. It is possible to define groups of populations.

• start of the subsection:

  [[Structure]]

• name for the genetic structure (string within “”): StructureName = “A example”
• number of groups defined in the structure (int value): NbGroups = 5
• group definitions (list containing the names of the samples belonging to the group, entered within braces):

NbGroups=2
Group ={
  population1
  population2
  population3
}
Group ={
  population4
  population5
}

allows to specify some distance matrices. The goal is to compute a correlation between the Ymatrix and X1 or a partial correlation between the Ymatrix, X1 and X2. The Ymatrix can be either a pairwise population FST matrix or a custom matrix entered into the project by the user. X1 (and X2) have to be defined in the project.

• start of the subsection:

  [[Mantel]]

• size of the matrices (pos. int value): MatrixSize= 5
• number of matrices among which we compute the correlations (2/3): MatrixNumber= 2
• matrix that is used as genetic distance (“fst” (→Y=Fst)/ “log_fst” (→Y=log(Fst))/ “slatkinlinearfst” (→Y=Fst/(1-Fst))/ “log_slatkinlinearfst” (→Y=log(Fst/(1-Fst)))/ “nm” (→Y=(1-Fst)/(2 Fst))/ “custom” (→Y= user-specified in the project)): YMatrix = “fst”
• labels that identify the columns of the YMatrix (list containing the names of the lable name belonging to the group, entered within braces):

YMatrixLabels = {
  "Population1 " "Population4" "Population2"
  "Population8" "Population5"
}

• keyword that allows to define a matrix with witch the correlation with the YMatrix is computed:

DistMatMantel={
  0.00
  3.20 0.00
  0.47 0.76 0.00
  0.00 1.23 0.37 0.00
  0.22 0.37 0.21 0.38 0.00
}

• Labels defining the sub-matrix on witch the correlation is computed:

UsedYMatrixLabels={
  "Population1 "
  "Population5"
  "Population8"
}

Excoffier, L. and H.E. L. Lischer (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources. 10: 564-567.