This is an inter-individual distance measure similar to
Bruvo.distance, except that where genotypes have different
numbers of alleles, virtual alleles are equal to those from the longer
and/or shorter genotype, rather than being equal to infinity.
Bruvo2.distance(genotype1, genotype2, maxl = 7, usatnt = 2,
missing = -9, add = TRUE, loss = TRUE)A decimal between 0 and 1, with 0 indicating complete identity of two
genotypes, and 1 indicating maximum dissimilarity. NA is
returned if one or both genotypes are missing or if maxl is exceeded.
A numeric vector representing the genotype of one individual at one
locus. This type of vector is produced by the Genotype
method for "genambig" objects.
The second genotype for the distance calculation, in the same format as
genotype1.
The maximum number of alleles that either genotype can have. If it is
exceeded, NA is returned. This argument exists to prevent
computations that would take in excess of an hour; see
Bruvo.distance.
The microsatellite repeat type for the locus. 2 for dinucleotide
repeats, 3 for dinucleotide repeats, 1 if the alleles are
already coded as repeat numbers, etc. See Usatnts.
The symbol that indicates missing data for a given sample and locus.
See Missing.
TRUE if the model of genome addition is being used, and
FALSE if it is not. If this model is used, the shorter genotype
will have virtual alleles added from the same genotype.
TRUE if the model of genome loss is being used, and FALSE
if it is not. If this model is used, the shorter genotype will have
virtual alleles added from the longer genotype.
Lindsay V. Clark
Bruvo et al. (2004) describe multiple methods for calculating
genetic distances between individuals of different ploidy. (See
“Special cases” starting on page 2102 of the paper.) The
original Bruvo.distance function in polysat uses the method
described for systems with complex changes in ploidy, adding virtual
alleles equal to infinity to the shorter genotype to make it the same
length as the longer genotype. This method, however, can exaggerate
distances between individuals of different ploidy, particularly when
used with meandistance.matrix2 as opposed to
meandistance.matrix.
Bruvo2.distance calculates distances between individuals under
the models of genome addition and genome loss. If add = TRUE and
loss = TRUE, the distance produced is equal to that of equation 6
in the paper.
If add = TRUE and loss = FALSE, the distance calculated is
that under genome addition only. Likewise if add = FALSE and
loss = TRUE the distance is calculated under genome loss only.
The latter distance should be greater than the former. If both were averaged
together, they would give the identical result to that produced when
add = TRUE and loss = TRUE. All three distances will be
less than that produced by Bruvo.distance.
If both genotypes have the same number of alleles, they are passed to
Bruvo.distance for the calculation. This also happens if
add = FALSE and loss = FALSE. Otherwise, if the genotypes have
different numbers of alleles, all possible genotypes with virtual
alleles are enumerated and passed to Bruvo.distance one by one, and
the results averaged.
The number of different genotypes simulated under genome loss or genome addition is \(l^d\), where \(l\) is the length of the genotype from which virtual alleles are being taken, and \(d\) is the difference in length between the longer and shorter genotype. For example, under genome addition for a diploid individual with alleles 1 and 2 being compared to a tetraploid individual, the genotypes 1211, 1212, 1221, and 1222 will each be used once to represent the diploid individual.
Bruvo, R., Michiels, N. K., D'Sousa, T. G., and Schulenberg, H. (2004) A simple method for calculation of microsatellite genotypes irrespective of ploidy level. Molecular Ecology 13, 2101--2106.
Lynch.distance, Bruvo.distance,
meandistance.matrix2
Bruvo2.distance(c(102,104), c(104,104,106,110))
Bruvo2.distance(c(102,104), c(104,104,106,110), add = FALSE)
Bruvo2.distance(c(102,104), c(104,104,106,110), loss = FALSE)
Run the code above in your browser using DataLab