The distinction between model and nonmodel organisms is becoming increasingly blurred. High-throughput, second-generation sequencing approaches are being applied to organisms based on their interesting ecological, physiological, developmental, or evolutionary properties and not on the depth of genetic information available for them. Here, we illustrate this point using a low-cost, efficient technique to determine the fine-scale phylogenetic relationships among recently diverged populations in a species. This application of restriction site-associated DNA tags (RAD tags) reveals previously unresolved genetic structure and direction of evolution in the pitcher plant mosquito, Wyeomyia smithii, from a southern Appalachian Mountain refugium following recession of the Laurentide Ice Sheet at 22,000-19,000 B.P. The RAD tag method can be used to identify detailed patterns of phylogeography in any organism regardless of existing genomic data, and, more broadly, to identify incipient speciation and genome-wide variation in natural populations in general.genomics | restriction site-associated DNA tag | second-generation sequencing | Wyeomyia smithii

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BackgroundPopulation structure inference using the software STRUCTURE has become an integral part of population genetic studies covering a broad spectrum of taxa including humans. The ever-expanding size of genetic data sets poses computational challenges for this analysis. Although at least one tool currently implements parallel computing to reduce computational overload of this analysis, it does not fully automate the use of replicate STRUCTURE analysis runs required for downstream inference of optimal K. There is pressing need for a tool that can deploy population structure analysis on high performance computing clusters.ResultsWe present an updated version of the popular Python program StrAuto, to streamline population structure analysis using parallel computing. StrAuto implements a pipeline that combines STRUCTURE analysis with the Evanno Δ K analysis and visualization of results using STRUCTURE HARVESTER. Using benchmarking tests, we demonstrate that StrAuto significantly reduces the computational time needed to perform iterative STRUCTURE analysis by distributing runs over two or more processors.ConclusionStrAuto is the first tool to integrate STRUCTURE analysis with post-processing using a pipeline approach in addition to implementing parallel computation – a set up ideal for deployment on computing clusters. StrAuto is distributed under the GNU GPL (General Public License) and available to download from http://strauto.popgen.org.

Abstract. Dominance and epistatic effects are predicted to be larger in life-history than in morphological traits. We test these predictions using published results from line cross analyses. We find that dominance is found in more than 95% of traits, regardless of the type of trait, but that the magnitude of the effect in relation to the additive effect is much greater in life-history than in morphological traits. Epistatic effects were detected more often in life-history than in morphological traits (79% and 67%, respectively). We also test for a difference in the magnitude of the effects by comparing the ratio of the nonadditive components separately to the additive component. For both dominance and epistatic components, the ratio of the nonadditive component to additive effects in life-history traits is approximately twice as large as that for morphological traits.

The major drivers of the extensive biodiversity of the Neotropics are proposed to be geological and tectonic events together with Pliocene and Pleistocene environmental and climatic change. Geographical barriers represented by the rivers Amazonas/Solimões, the Andes and the coastal mountain ranges in eastern Brazil have been hypothesized to lead to diversification within the primary malaria vector, Anopheles (Nyssorhynchus) darlingi Root, which primarily inhabits rainforest. To test this biogeographical hypothesis, we analyzed 786 single nucleotide polymorphisms (SNPs) in 12 populations of An. darlingi from across the complex Brazilian landscape. Both model-based (STRUCTURE) and non-model-based (Principal Components and Discriminant Analysis) analysis of population structure detected three major genetic clusters that correspond with newly described Neotropical biogeographical regions: 1) Atlantic Forest province (= southeast population); 2) Parana Forest province (= West Atlantic forest population, with one Chacoan population - SP); and 3) Brazilian dominion population (= Amazonian population with one Chacoan population - TO). Significant levels of pairwise genetic divergences were found among the three clusters, allele sharing among clusters was negligible, and geographical distance did not contribute to differentiation. We infer that the Atlantic forest coastal mountain range limited dispersal between the Atlantic Forest province and the Parana Forest province populations, and that the large, diagonal open vegetation region of the Chacoan dominion dramatically reduced dispersal between the Parana and Brazilian dominion populations. We hypothesize that the three genetic clusters may represent three putative species.