Distancias genéticas entre perfiles moleculares obtenidos desde marcadores multilocus multialélicos
In order to express the magnitude of the genetic identity (similarity) or its complement (distance) between individuals genotyped with microsatellites (SSR), which are multilocusmultiallele markers, is necessary to choose a metric in agreement with the multivariate nature of the marker data. M...
Guardado en:
| Publicado en: | Revista de la Facultad de Ciencias Agrarias |
|---|---|
| Autores principales: | , |
| Materias: | |
| Acceso en línea: | https://bdigital.uncu.edu.ar/fichas.php?idobjeto=6522 |
| Sumario: | In order to express the magnitude of the
genetic identity (similarity) or its complement
(distance) between individuals genotyped with
microsatellites (SSR), which are multilocusmultiallele
markers, is necessary to choose
a metric in agreement with the multivariate
nature of the marker data. Most of the metrics
of genetic distances were designed to express,
as a single quantity, the genetic difference between
two populations and they are expressed
as function of population allele frequencies.
Such metrics can also be used to calculate
distances between individual profiles, but the
allele frequencies are not longer continuous.
On the other hand, geometric distances obtained
as complement of similarity indexes for
binary data indicating allele presence/absence
in each individual, are commonly used for
pairwise individual comparisons. However,
they do not take into account the nested
allele within locus structure of SSR data. The
objective of this work was to simultaneously
evaluate the performance of both metric types
to order and classify individuals in a multivariate
basis generated by the use of SSR loci.
We applied 11 different distance metrics to a
dataset involving 17 SSR loci obtained from
17 entries of a soya [Glycine max (L.) Merr.]
germoplasm, and evaluated the consensus in
the results obtained from the classification of
the 17 molecular profiles from severl metrics.
The results suggest that most of the evaluated
metrics yield similar information about marker
profiles in the context of pairwise individual
comparisons. We provide a kernel-based
metric classification. |
|---|
