Accurate Inference of Subtle Population Structure (and Other Genetic Discontinuities) Using Principal Coordinates

Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.PCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies

Genetic differentiation and admixture between sibling allopolyploids in the Dactylorhiza majalis complex

Allopolyploidization often happens recurrently, but the evolutionary significance of its iterative nature is not yet fully understood. Of particular interest are the gene flow dynamics and the mechanisms that allow young sibling polyploids to remain distinct while sharing the same ploidy, heritage and overlapping distribution areas. By using eight highly variable nuclear microsatellites, newly reported here, we investigate the patterns of divergence and gene flow between 386 polyploid and 42 diploid individuals, representing the sibling allopolyploids Dactylorhiza majalis s.s. and D. traunsteineri s.l. and their parents at localities across Europe. We make use in our inference of the distinct distribution ranges of the polyploids, including areas in which they are sympatric (that is, the Alps) or allopatric (for example, Pyrenees with D. majalis only and Britain with D. traunsteineri only). Our results show a phylogeographic signal, but no clear genetic differentiation between the allopolyploids, despite the visible phenotypic divergence between them. The results indicate that gene flow between sibling Dactylorhiza allopolyploids is frequent in sympatry, with potential implications for the genetic patterns across their entire distribution range. Limited interploidal introgression is also evidenced, in particular between D. incarnata and D. traunsteineri. Altogether the allopolyploid genomes appear to be porous for introgression from related diploids and polyploids. We conclude that the observed phenotypic divergence between D. majalis and D. traunsteineri is maintained by strong divergent selection on specific genomic areas with strong penetrance, but which are short enough to remain undetected by genotyping dispersed neutral markers.UE FWF; P22260UE: Y66