Sympatric Recombination in Zoonotic Cryptosporidium Leads to Emergence of Populations with Modified Host Preference.

Genetic recombination plays a critical role in the emergence of pathogens with phenotypes such as drug resistance, virulence, and host adaptation. Here, we tested the hypothesis that recombination between sympatric ancestral populations leads to the emergence of divergent variants of the zoonotic parasite Cryptosporidium parvum with modified host ranges. Comparative genomic analyses of 101 isolates have identified seven subpopulations isolated by distance. They appear to be descendants of two ancestral populations, IIa in northwestern Europe and IId from southwestern Asia. Sympatric recombination in areas with both ancestral subtypes and subsequent selective sweeps have led to the emergence of new subpopulations with mosaic genomes and modified host preference. Subtelomeric genes could be involved in the adaptive selection of subpopulations, while copy number variations of genes encoding invasion-associated proteins are potentially associated with modified host ranges. These observations reveal ancestral origins of zoonotic C. parvum and suggest that pathogen import through modern animal farming might promote the emergence of divergent subpopulations of C. parvum with modified host preference.

Human impact on the recent population history of the elusive European wildcat inferred from whole genome data.

BACKGROUND: The extent and impact of evolutionary change occurring in natural populations in response to rapid anthropogenic impact is still poorly understood on the genome-wide level. Here, we explore the genetic structure, demographic history, population differentiation, and domestic introgression based on whole genome data of the endangered European wildcat in Germany, to assess potential genomic consequences of the species' recent spread across human-dominated cultural landscapes. RESULTS: Reconstruction of demographic history and introgression rates based on 47 wildcat and 37 domestic cat genomes suggested late introgression between wild and domestic cat, coinciding with the introduction of domestic cat during the Roman period, but overall relatively low rates of hybridization and introgression from domestic cats. Main population divergence found between an eastern and central German wildcat clade was found to be of rather recent origin (200 y), and thus the likely consequence of anthropogenic persecution and resulting isolation in population refugia. We found similar effective population sizes and no substantial inbreeding across populations. Interestingly, highly differentiated genes between wild cat populations involved in the tryptophan-kynurenine-serotonin pathway were revealed, which plays a role in behavioral processes such as stress susceptibility and tolerance, suggesting that differential selection acted in the populations. CONCLUSIONS: We found strong evidence for substantial recent anthropogenic impact on the genetic structure of European wildcats, including recent persecution-driven population divergence, as well as potential adaptation to human-dominate environments. In contrast, the relatively low levels of domestic introgression and inbreeding found in this study indicate a substantial level of "resistance" of this elusive species towards major anthropogenic impacts, such as the omnipresence of domestic cats as well as substantial habitat fragmentation. While those findings have strong implications for ongoing conservation strategies, we demand closer inspection of selective pressures acting on this and other wildlife species in anthropogenic environments.

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions.

The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ-density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal. 99: 2053-2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B: 599-609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ-selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ's in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.

Multi-dimensional diffusion process of allele frequencies in population genetics.

One of the ultimate goals of population genetics is to theoretically describe the behavior of allele frequency. Diffusion theory has been commonly used for this purpose mainly in one-locus one-population models, although it is not easy to handle diffusion theory in models with multiple loci or with multiple populations. This review introduces several successful cases, where multi-dimensional diffusion equations contributed to addressing evolutionary questions, thereby demonstrating its strong potential in population genetics.

Time-specific and pleiotropic quantitative trait loci coordinately modulate stem growth in Populus.

In perennial woody plants, the coordinated increase of stem height and diameter during juvenile growth improves competitiveness (i.e. access to light); however, the factors underlying variation in stem growth remain unknown in trees. Here, we used linkage-linkage disequilibrium (linkage-LD) mapping to decipher the genetic architecture underlying three growth traits during juvenile stem growth. We used two Populus populations: a linkage mapping population comprising a full-sib family of 1,200 progeny and an association mapping panel comprising 435 unrelated individuals from nearly the entire natural range of Populus tomentosa. We mapped 311 quantitative trait loci (QTL) for three growth traits at 12 timepoints to 42 regions in 17 linkage groups. Of these, 28 regions encompassing 233 QTL were annotated as 27 segmental homology regions (SHRs). Using SNPs identified by whole-genome re-sequencing of the 435-member association mapping panel, we identified significant SNPs (P ≤ 9.4 × 10-7 ) within 27 SHRs that affect stem growth at nine timepoints with diverse additive and dominance patterns, and these SNPs exhibited complex allelic epistasis over the juvenile growth period. Nineteen genes linked to potential causative alleles that have time-specific or pleiotropic effects, and mostly overlapped with significant signatures of selection within SHRs between climatic regions represented by the association mapping panel. Five genes with potential time-specific effects showed species-specific temporal expression profiles during the juvenile stages of stem growth in five representative Populus species. Our observations revealed the importance of considering temporal genetic basis of complex traits, which will facilitate the molecular design of tree ideotypes.

A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure.

Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

Evolution of high mobility group nucleosome-binding proteins and its implications for vertebrate chromatin specialization.

High mobility group (HMG)-N proteins are a family of small nonhistone proteins that bind to nucleosomes (N). Despite the amount of information available on their structure and function, there is an almost complete lack of information on the molecular evolutionary mechanisms leading to their exclusive differentiation. In the present work, we provide evidence suggesting that HMGN lineages constitute independent monophyletic groups derived from a common ancestor prior to the diversification of vertebrates. Based on observations of the functional diversification across vertebrate HMGN proteins and on the extensive silent nucleotide divergence, our results suggest that the long-term evolution of HMGNs occurs under strong purifying selection, resulting from the lineage-specific functional constraints of their different protein domains. Selection analyses on independent lineages suggest that their functional specialization was mediated by bursts of adaptive selection at specific evolutionary times, in a small subset of codons with functional relevance-most notably in HMGN1, and in the rapidly evolving HMGN5. This work provides useful information to our understanding of the specialization imparted on chromatin metabolism by HMGNs, especially on the evolutionary mechanisms underlying their functional differentiation in vertebrates.

Statistical challenges in a regulatory review of cardiovascular and CNS clinical trials.

There are several challenging statistical problems identified in the regulatory review of large cardiovascular (CV) clinical outcome trials and central nervous system (CNS) trials. The problems can be common or distinct due to disease characteristics and the differences in trial design elements such as endpoints, trial duration, and trial size. In schizophrenia trials, heavy missing data is a big problem. In Alzheimer trials, the endpoints for assessing symptoms and the endpoints for assessing disease progression are essentially the same; it is difficult to construct a good trial design to evaluate a test drug for its ability to slow the disease progression. In CV trials, reliance on a composite endpoint with low event rate makes the trial size so large that it is infeasible to study multiple doses necessary to find the right dose for study patients. These are just a few typical problems. In the past decade, adaptive designs were increasingly used in these disease areas and some challenges occur with respect to that use. Based on our review experiences, group sequential designs (GSDs) have borne many successful stories in CV trials and are also increasingly used for developing treatments targeting CNS diseases. There is also a growing trend of using more advanced unblinded adaptive designs for producing efficacy evidence. Many statistical challenges with these kinds of adaptive designs have been identified through our experiences with the review of regulatory applications and are shared in this article.

Some challenges with statistical inference in adaptive designs.

Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.

The adaptation of bumblebees to extremely high elevation associated with their gut microbiota.

Bumblebees are among the most abundant and important pollinators for sub-alpine and alpine flowering plant species in the Northern Hemisphere, but little is known about their adaptations to high elevations. In this article, we focused on two bumblebee species, Bombus friseanus and Bombus prshewalskyi, and their respective gut microbiota. The two species, distributed through the Hengduan Mountains of southwestern China, show species replacement at different elevations. We performed genome sequencing based on 20 worker bee samples of each species. Applying evolutionary population genetics and metagenomic approaches, we detected genes under selection and analyzed functional pathways between bumblebees and their gut microbes. We found clear genetic differentiation between the two host species and significant differences in their microbiota. Species replacement occurred in both hosts and their bacteria (Snodgrassella) with an increase in elevation. These extremely high-elevation bumblebees show evidence of positive selection related to diverse biological processes. Positively selected genes involved in host immune systems probably contributed to gut microbiota changes, while the butyrate generated by gut microbiota may influence both host energy metabolism and immune systems. This suggests a close association between the genomes of the host species and their microbiomes based on some degree of natural selection.IMPORTANCETwo closely related and dominant bumblebee species, distributed at different elevations through the Hengduan Mountains of southwestern China, showed a clear genomic signature of adaptation to elevation at the molecular level and significant differences in their respective microbiota. Species replacement occurred in both hosts and their bacteria (Snodgrassella) with an increase in elevation. Bumblebees' adaptations to higher elevations are closely associated with their gut microbiota through two biological processes: energy metabolism and immune response. Information allowing us to understand the adaptive mechanisms of species to extreme conditions is implicit if we are to conserve them as their environments change.

Adaptive selection of local and non-local attention mechanisms for speech enhancement.

In speech enhancement tasks, local and non-local attention mechanisms have been significantly improved and well studied. However, a natural speech signal contains many dynamic and fast-changing acoustic features, and focusing on one type of attention mechanism (local or non-local) cannot precisely capture the most discriminative information for estimating target speech from background interference. To address this issue, we introduce an adaptive selection network to dynamically select an appropriate route that determines whether to use the attention mechanisms and which to use for the task. We train the adaptive selection network using reinforcement learning with a developed difficulty-adjusted reward that is related to the performance, complexity, and difficulty of target speech estimation from the noisy mixtures. Consequently, we propose an Attention Selection Speech Enhancement Network (ASSENet) with the innovative dynamic block that consists of an adaptive selection network and a local and non-local attention based speech enhancement network. In particular, the ASSENet incorporates both local and non-local attention and develops the attention mechanism selection technique to explore the appropriate route of local and non-local attention mechanisms for speech enhancement tasks. The results show that our method achieves comparable and superior performance to existing approaches with attractive computational costs.

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization.

The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers' attention once more with the COVID-19 pandemic. The IP algorithm determines the number of donors and the number of receivers when two specific control parameters are initialized and protects their values until the end of termination. However, determining which values are appropriate for the control parameters by adjusting the number of donors and receivers and guessing how they interact with each other are difficult tasks. In this study, we attempted to determine the number of plasma donors and receivers with an improved mechanism that depended on dividing the whole population into two sub-populations using a statistical measure known as the percentile and then a novel variant of the IPA called the percentile IPA (pIPA) was introduced. To investigate the performance of the pIPA, 22 numerical benchmark problems were solved by assigning different values to the control parameters of the algorithm. Moreover, two complex engineering problems, one of which required the filtering of noise from the recorded signal and the other the path planning of an unmanned aerial vehicle, were solved by the pIPA. Experimental studies showed that the percentile-based donor-receiver selection mechanism significantly contributed to the solving capabilities of the pIPA and helped it outperform well-known and state-of-art meta-heuristic algorithms.

Genomic insights into the origin, adaptive evolution, and herbicide resistance of Leptochloa chinensis, a devastating tetraploid weedy grass in rice fields.

Chinese sprangletop (Leptochloa chinensis), belonging to the grass subfamily Chloridoideae, is one of the most notorious weeds in rice ecosystems. Here, we report a chromosome-scale reference genome assembly and a genomic variation map of the tetraploid L. chinensis. The L. chinensis genome is derived from two diploid progenitors that diverged ∼10.9 million years ago, and its two subgenomes display neither fractionation bias nor overall gene expression dominance. Comparative genomic analyses reveal substantial genome rearrangements in L. chinensis after its divergence from the common ancestor of Chloridoideae and, together with transcriptome profiling, demonstrate the important contribution of tetraploidization to the gene sources for the herbicide resistance of L. chinensis. Population genomic analyses of 89 accessions from China reveal that L. chinensis accessions collected from southern/southwestern provinces have substantially higher nucleotide diversity than those from the middle and lower reaches of the Yangtze River, suggesting that L. chinensis spread in China from the southern/southwestern provinces to the middle and lower reaches of the Yangtze River. During this spread, L. chinensis developed significantly increased herbicide resistance, accompanied by the selection of numerous genes involved in herbicide resistance. Taken together, our study generated valuable genomic resources for future fundamental research and agricultural management of L. chinensis, and provides significant new insights into the herbicide resistance as well as the origin and adaptive evolution of L. chinensis.

[Industrial adaptability selection for a novel ω-transaminase].

ω-transaminase (ω-TA) is the most promising biocatalyst for chiral amine synthesis. However, most wild-type ω-TAs cannot be applied in industry directly due to their low stability and unfavorable reaction equilibrium. In order to discover a novel ω-TA for industrial application, we designed a procedure of adaptive selection, including the screening of substrates, protein sequences and clones, enzyme activity, and product conversion and characterization, as well as trouble-shooting of each step. Through this procedure, we screened a novel ω-TA, ATA-W12 of Caulobacter sp. from a soil metagenome. The strain could convert 20 mmol/L 1-Boc-3-pyrrolidinone and 20 mmol/L 1-Boc-3-piperidone with 85.84% and 67.42% conversion rate, respectively, in a 1-mL scale with isopropylamine (IPA) as amine donor. ATA-W12 maintained 100% activity at 40 °C for 168 h, and its optimal reaction condition is at pH 8.5 and 40 °C. These excellent properties benefit the application of IPA as an ideal amino donor in industry. We scaled up the production of (S)-(+)-1-boc-3-aminopiperidine up to 50 mL (100 g/L) scale with this novel biocatalyst for its further industrial application.

Adaptive selection in the evolution of programmed cell death-1 and its ligands in vertebrates.

Programmed cell death-1 (PD-1) and its ligands, particularly PD-L1 and PD-L2, are the most important proteins responsible for signaling T-cell inhibition and arbitrating immune homeostasis and tolerance mechanisms. However, the adaptive evolution of these genes is poorly understood. In this study, we aligned protein-coding genes from vertebrate species to evaluate positive selection constraints and evolution in the PD1, PD-L1 and PD-L2 genes conserved across up to 166 vertebrate species, with an average of 55 species per gene. We determined that although the positive selection was obvious, an average of 5.3% of codons underwent positive selection in the three genes across vertebrate lineages, and increased positive selection pressure was detected in both the Ig-like domains and transmembrane domains of the proteins. Moreover, the PD1, PD-L1 and PD-L2 genes were highly expressed in almost all tissues of the selected species indicating a distinct expression pattern in different tissues among most species. Our study reveals that adaptive selection plays a key role in the evolution of PD1 and its ligands in the majority of vertebrate species, which is in agreement with the contribution of these residues to the mechanisms of pathogen identification and coevolution in the complexity and novelties of vertebrate immune systems.

Sequencing of VDJ genes in Lepus americanus confirms a correlation between VHn expression and the leporid species continent of origin.

Leporid VH genes used in the generation of their primary antibody repertoire exhibit highly divergent lineages. For the European rabbit (Oryctolagus cuniculus) four VHa lineages have been described, the a1, a2, a3 and a4. Hares (Lepus spp.) and cottontail (Sylvilagus floridanus) express one VHa lineage each, the a2L and the a5, respectively, along with a more ancient lineage, the Lepus spp. sL and S. floridanus sS. Both the European rabbit and the Lepus europaeus use a third lineage, VHn, in a low proportion of their VDJ rearrangements. The VHn genes are a conserved ancestral polymorphism that is being maintained in the leporid genome.Their usage in a low proportion of VDJ rearrangements by both European rabbit and L. europaeus but not S. floridanus has been argued to be a remnant of an ancient European leporid immunologic response to pathogens. To address this hypothesis, in this study we sequenced VDJ rearranged genes for another North American leporid, L. americanus. Our results show that L. americanus expressed these genes less frequently and in a highly modified fashion compared to the European Lepus species. Our results suggest that the American leporid species use a different VH repertoire than the European species which may be related with an immune adaptation to different environmental conditions, such as different pathogenic agents.

An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images.

PURPOSE: Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. METHODS: To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. RESULTS: Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. CONCLUSION: Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.

Adaptive and neutral markers both show continent-wide population structure of mountain pine beetle (Dendroctonus ponderosae).

Assessments of population genetic structure and demographic history have traditionally been based on neutral markers while explicitly excluding adaptive markers. In this study, we compared the utility of putatively adaptive and neutral single-nucleotide polymorphisms (SNPs) for inferring mountain pine beetle population structure across its geographic range. Both adaptive and neutral SNPs, and their combination, allowed range-wide structure to be distinguished and delimited a population that has recently undergone range expansion across northern British Columbia and Alberta. Using an equal number of both adaptive and neutral SNPs revealed that adaptive SNPs resulted in a stronger correlation between sampled populations and inferred clustering. Our results suggest that adaptive SNPs should not be excluded prior to analysis from neutral SNPs as a combination of both marker sets resulted in better resolution of genetic differentiation between populations than either marker set alone. These results demonstrate the utility of adaptive loci for resolving population genetic structure in a nonmodel organism.