Science Serving Maryland's Coasts

Research Publications: UM-SG-RS-2006-17


A genomic scan for divergent selection in a secondary contact zone between Atlantic and Gulf of Mexico oysters, Crassostrea virginica.




Murray, MC; Hare, MP


Molecular Ecology 15(13):4229-4242




The degree of population structure within species often varies considerably among loci. This makes it difficult to determine whether observed variance reflects neutral-drift stochasticity or locus-specific selection at one or more loci. This uncertainty is exacerbated when evolutionary equilibrium cannot be assumed and/or admixture potentially inflates genomic variance. Thus, the value of a 'genome scan', where locus-specific summary statistics are compared with a simulated neutral distribution among loci, may be limited in secondary contact zones if the null distribution is sensitive to the timing of secondary contact. Of particular interest here is the wide variance previously observed in locus-specific patterns of population structure between Atlantic and Gulf of Mexico populations of eastern oyster, Crassostrea virginica. To test the robustness of an equilibrium null model, we compared among-locus distributions of F-ST simulated under migration-drift equilibrium and several nonequilibrium secondary contact histories. We then tested for evidence of divergent selection between two oyster populations on either side of a secondary contact zone using 215 amplified fragment length polymorphism (AFLP) loci. Constant-migration equilibrium and nonequilibrium secondary-contact simulations produced equivalent distributions of F-ST when anchored by the global mean F-ST observed in oysters, 0.0917. The 99th quantile of simulated neutral F-ST encompassed most of the variation among oyster loci. Three AFLP loci exhibited F-ST values higher than this threshold. Although no locus was significant after correcting for multiple tests, our results show in geographically clinal organisms: AFLPs can efficiently characterize the genomic distribution of F-ST; equilibrium models can be used to evaluate outliers; these procedures help focus research on genomic regions of interest.

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