An efficient method to find potentially universal population genetic markers, applied to metazoans.
BMC Evol Biol
; 10: 276, 2010 Sep 13.
Article
em En
| MEDLINE
| ID: mdl-20836842
ABSTRACT
BACKGROUND:
Despite the impressive growth of sequence databases, the limited availability of nuclear markers that are sufficiently polymorphic for population genetics and phylogeography and applicable across various phyla restricts many potential studies, particularly in non-model organisms. Numerous introns have invariant positions among kingdoms, providing a potential source for such markers. Unfortunately, most of the few known EPIC (Exon Primed Intron Crossing) loci are restricted to vertebrates or belong to multigenic families.RESULTS:
In order to develop markers with broad applicability, we designed a bioinformatic approach aimed at avoiding multigenic families while identifying intron positions conserved across metazoan phyla. We developed a program facilitating the identification of EPIC loci which allowed slight variation in intron position. From the Homolens databases we selected 29 gene families which contained 52 promising introns for which we designed 93 primer pairs. PCR tests were performed on several ascidians, echinoderms, bivalves and cnidarians. On average, 24 different introns per genus were amplified in bilaterians. Remarkably, five of the introns successfully amplified in all of the metazoan genera tested (a dozen genera, including cnidarians). The influence of several factors on amplification success was investigated. Success rate was not related to the phylogenetic relatedness of a taxon to the groups that most influenced primer design, showing that these EPIC markers are extremely conserved in animals.CONCLUSIONS:
Our new method now makes it possible to (i) rapidly isolate a set of EPIC markers for any phylum, even outside the animal kingdom, and thus, (ii) compare genetic diversity at potentially homologous polymorphic loci between divergent taxa.
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Base de dados:
MEDLINE
Assunto principal:
Marcadores Genéticos
/
Biologia Computacional
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article