Sort Alleles into Isoloci

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Description

catalanAlleles uses genotypes present in a "genambig" object to sort alleles from one locus into two or more isoloci in an allopolyploid or diploidized autopolyploid species. Alleles are determined to belong to different isoloci if they are both present in a fully homozygous genotype. If necessary, heterozygous genotypes are also examined to resolve remaining alleles.

Usage

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catalanAlleles(object, samples = Samples(object), locus = 1,
               n.subgen = 2, SGploidy = 2, verbose = FALSE)

Arguments

object

A "genambig" object containing the dataset to analyze. All individuals should be the same ploidy, although the function does not access the Ploidies slot. Missing data are allowed. For the locus to be examined, no genotype should have fewer than n.subgen alleles or more than n.subgen*SGploidy alleles.

samples

Optional argument indicating samples to be analyzed. Can be integer or character, as with other polysat functions.

locus

An integer or character string indicating which locus to analyze. Cannot be a vector greater than length 1. (The function will only analyze one locus at a time.)

n.subgen

The number of isoloci (number of subgenomes). For example, 2 for an allotetraploid, and 3 for an allohexaploid (three diploid genomes).

SGploidy

The ploidy of each genome. Only one value is allowed; all genomes must be the same ploidy. 2 indicates that each subgenome is diploid (as in an allotetraploid, or an allohexaploid with three diploid genomes).

verbose

Boolean. Indicates whether results, and if applicable, problematic genotypes, should be printed to the console.

Details

catalanAlleles implements and extends an approach used by Catalan et al. (2006) that sorts alleles from a duplicated microsatellite locus into two or more isoloci (homeologous loci on different subgenomes). First, fully homozygous genotypes are identified and used in analysis. If a genotype has as many alleles as there are subgenomes (for example, a genotype with two alleles in an allotetraploid species), it is assumed to be fully homozygous and the alleles are assumed to belong to different subgenomes. If some alleles remain unassigned after examination of all fully homozygous genotypes, heterozygous genotypes are also examined to attempt to assign those remaining alleles.

For example, in an allotetraploid, if a genotype contains one unassigned allele, and all other alleles in the genotype are known to belong to one isolocus, the unassigned allele can be assigned to the other isolocus. Or, if two alleles in a genotype belong to one isolocus, one allele belongs to the other isolocus, and one allele is unassigned, the unassigned allele can be assigned to the latter isolocus. The function follows such logic (which can be extended to higher ploidies) until all alleles can be assigned, or returns a text string saying that the allele assignments were unresolvable.

It is important to note that this method assumes no null alleles and no homoplasy across isoloci. If the function encounters evidence of either it will not return allele assignments. Null alleles and homoplasy are real possibilities in any dataset, which means that this method simply will not work for some microsatellite loci.

(Null alleles are those that do not produce a PCR amplicon, usually because of a mutation in the primer binding site. Alleles that exhibit homoplasy are those that produce amplicons of the same size, despite not being identical by descent. Specifically, homoplasy between alleles from different isoloci will interfere with the Catalan method of allele assignment.)

Value

A list containing the following items:

locus

A character string giving the name of the locus.

SGploidy

A number giving the ploidy of each subgenome. Identical to the SGploidy argument.

assignments

If assignments cannot be made, a character string describing the problem. Otherwise, a matrix with n.subgen rows and a labeled column for each allele, with a 1 if the allele belongs to that subgenome and a 0 if it does not.

Note

Aside from homoplasy and null alleles, stochastic effects may prevent the minimum combination of genotypes needed to resolve all alleles from being present in the dataset. For a typical allotetraploid dataset, 50 to 100 samples will be needed, whereas an allohexaploid dataset may require over 100 samples. In simulations, allo-octoploid datasets with two tetraploid genomes were unresolvable even with 10,000 samples due to the low probability of finding full homozygotes. Additionally, loci are less likely to be resolvable if they have many alleles or if one isolocus is monomorphic.

Although determination of allele copy number by is not needed (or expected) for catalanAlleles as it was in the originally published Catalan method, it is still very important that the genotypes be high quality. Even a single scoring error can cause the method to fail, including allelic dropout, contamination between samples, stutter peaks miscalled as alleles, and PCR artifacts miscalled as alleles. Poor quality loci (those that require some “artistic” interpretation of gels or electropherograms) are unlikely to work with this method. Individual genotypes that are of questionable quality should be discarded before running the function.

Author(s)

Lindsay V. Clark

References

Catalan, P., Segarra-Moragues, J. G., Palop-Esteban, M., Moreno, C. and Gonzalez-Candelas, F. (2006) A Bayesian approach for discriminating among alternative inheritance hypotheses in plant polyploids: the allotetraploid origin of genus Borderea (Dioscoreaceae). Genetics 172, 1939–1953.

See Also

alleleCorrelations, mergeAlleleAssignments, recodeAllopoly, simAllopoly

Examples

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# make the default simulated allotetraploid dataset
mydata <- simAllopoly()

# resolve the alleles
myassign <- catalanAlleles(mydata)

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