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Mol Ecol. 2010 Mar;19(5):968-84. doi: 10.1111/j.1365-294X.2010.04528.x. Epub 2010 Feb 8.

Genome-wide single nucleotide polymorphisms reveal population history and adaptive divergence in wild guppies.

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Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen D72076, Germany.


Adaptation of guppies (Poecilia reticulata) to contrasting upland and lowland habitats has been extensively studied with respect to behaviour, morphology and life history traits. Yet population history has not been studied at the whole-genome level. Although single nucleotide polymorphisms (SNPs) are the most abundant form of variation in many genomes and consequently very informative for a genome-wide picture of standing natural variation in populations, genome-wide SNP data are rarely available for wild vertebrates. Here we use genetically mapped SNP markers to comprehensively survey genetic variation within and among naturally occurring guppy populations from a wide geographic range in Trinidad and Venezuela. Results from three different clustering methods, Neighbor-net, principal component analysis (PCA) and Bayesian analysis show that the population substructure agrees with geographic separation and largely with previously hypothesized patterns of historical colonization. Within major drainages (Caroni, Oropouche and Northern), populations are genetically similar, but those in different geographic regions are highly divergent from one another, with some indications of ancient shared polymorphisms. Clear genomic signatures of a previous introduction experiment were seen, and we detected additional potential admixture events. Headwater populations were significantly less heterozygous than downstream populations. Pairwise F(ST) values revealed marked differences in allele frequencies among populations from different regions, and also among populations within the same region. F(ST) outlier methods indicated some regions of the genome as being under directional selection. Overall, this study demonstrates the power of a genome-wide SNP data set to inform for studies on natural variation, adaptation and evolution of wild populations.

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