Single Nucleotide Polymorphism Datasets Research Articles

An Integrated Genotyping-by-Sequencing Polymorphism Map for Over 10,000 Sorghum Genotypes.

Mining crop genomic variation can facilitate the genetic research of complex traits and molecular breeding. In sorghum [ L. (Moench)], several large-scale single nucleotide polymorphism (SNP) datasets have been generated using genotyping-by-sequencing of KI reduced representation libraries. However, data reuse has been impeded by differences in reference genome coordinates among datasets. To facilitate reuse of these data, we constructed and characterized an integrated 459,304-SNP dataset for 10,323 sorghum genotypes on the version 3.1 reference genome. The SNP distribution showed high enrichment in subtelomeric chromosome arms and in genic regions (48% of SNPs) and was highly correlated ( = 0.82) to the distribution of KI restriction sites. The genetic structure reflected population differences by botanical race, as well as familial structure among recombinant inbred lines (RILs). Faster linkage disequilibrium decay was observed in the diversity panel than in the RILs, as expected, given the greater opportunity for recombination in diverse populations. To validate the quality and utility of the integrated SNP dataset, we used genome-wide association studies (GWAS) of genebank phenotype data, precisely mapping several known genes (e.g and ) and identifying novel associations for other traits. We further validated the dataset with GWAS of new and published plant height and flowering time data in a nested association mapping population, precisely mapping known genes and identifying epistatic interactions underlying both traits. These findings validate this integrated SNP dataset as a useful genomics resource for sorghum genetics and breeding.

Detailed insights into pan-European population structure and inbreeding in wild and hatchery Pacific oysters (Crassostrea gigas) revealed by genome-wide SNP data.

Cultivated bivalves are important not only because of their economic value, but also due to their impacts on natural ecosystems. The Pacific oyster (Crassostrea gigas) is the world's most heavily cultivated shellfish species and has been introduced to all continents except Antarctica for aquaculture. We therefore used a medium‐density single nucleotide polymorphism (SNP) array to investigate the genetic structure of this species in Europe, where it was introduced during the 1960s and has since become a prolific invader of coastal ecosystems across the continent. We analyzed 21,499 polymorphic SNPs in 232 individuals from 23 localities spanning a latitudinal cline from Portugal to Norway and including the source populations of Japan and Canada. We confirmed the results of previous studies by finding clear support for a southern and a northern group, with the former being indistinguishable from the source populations indicating the absence of a pronounced founder effect. We furthermore conducted a large‐scale comparison of oysters sampled from the wild and from hatcheries to reveal substantial genetic differences including significantly higher levels of inbreeding in some but not all of the sampled hatchery cohorts. These findings were confirmed by a smaller but representative SNP dataset generated using restriction site‐associated DNA sequencing. We therefore conclude that genomic approaches can generate increasingly detailed insights into the genetics of wild and hatchery produced Pacific oysters.

Restore and Renew: a genomics‐era framework for species provenance delimitation

Here we present “Restore and Renew,” a replicable framework for gathering and interpreting evolutionary, ecological, and genomic data in support of restoration practices. In an era of rapid climatic change and continuous widespread clearing, revegetation projects need to focus on producing resilient and long‐term self‐sustaining populations. Restore and Renew expands current knowledge of genetic provenance via genome‐scan data, environmental niche modeling (ENM), and site‐specific climate information. The sampling strategy is to obtain leaf tissue representing the distributions of over 100 species commonly used in restoration. We apply generalized dissimilarity modeling to genome‐wide single nucleotide polymorphism datasets from hundreds of samples. Species‐specific local provenances are obtained using a model that represents observed patterns of genetic variation across the landscape. Climate modeling is implemented to interpret genetic provenance boundaries in the context of current and future climatic conditions at the specified site. Results are presented in an easy‐to‐use webtool (www.restore‐and‐renew.org.au), where the user simply selects their site of interest and a target species to obtain the size and distribution of local genetic provenance. Although Restore and Renew is not prescriptive, it allows restoration practitioners to make informed decisions on where to source material from, to fulfill their restoration scenario of choice. Two examples,Westringia fruticosaandAcacia suaveolens, are presented to demonstrate how the analytical pipeline responds to different ecological and evolutionary patterns. The webtool has multiple applications for biodiversity management and will continue to evolve with new species and analytical/interpretative outputs.

Tracing the genetic impact of farmed turbot Scophthalmus maximus on wild populations

The impact of escapees from aquaculture is of general concern for the sustainability of natural resources. Turbot Scophthalmus maximus is a marine flatfish of great commercial value whose land-based aquaculture started approx. 40 yr ago; hence, a low impact of escapees is expected on wild populations. However, enhancement of wild stocks using farmed turbot has been carried out along the Northeast Atlantic coasts in the last decades. Recently, a broad panel of single nucleotide polymorphism (SNP) markers (755 SNPs; 1 SNP Mb -1 ) has been used to evaluate the genetic structure of turbot throughout its distribution range, constituting the baseline to evaluate the impact of farmed fish in the wild. Two distinct origins were identified for farmed turbot (F _ORI1 and F _ORI2 ; F ST = 0.049), which differentiated from wild populations after 5 generations of selection (average F ST = 0.059), and consistent evidence of adaptation to domestication was de - tected. A notable proportion of fish of farmed ancestry was detected in the wild (15.5%), mainly in the North Sea, where restocking activities have taken place, determining genetic introgression in wild populations. Conversely, effects of land-based aquaculture appear negligible. A simulation exercise supported panels of 40 and 80 SNPs to identify fishes of F _ORI1 and F _ORI2 ancestry in the wild, respectively. Application to empirical data showed an assignment success (wild/farmed ancestry) of approx. 95% in comparison with the full SNP dataset. The SNP tools will be useful to monitor turbot of farmed ancestry in the wild, which might represent a risk, considering the lower fitness of farmed individuals.

Resolving the Speciation Patterns and Evolutionary History of the Intercontinental Disjunct Genus Corylus (Betulaceae) Using Genome-Wide SNPs.

Understanding the underlying mechanisms of species origin, divergence, and distribution patterns of the intercontinental disjunct taxa has long fascinated botanists. Based on 4,894 genome-wide single-nucleotide polymorphism dataset, we present a molecular phylogenetic reconstruction of genus Corylus (Betulaceae), which have a disjunct distribution between Eurasia and North America (NA). The aim is to explore the speciation patterns and evolutionary relationships of Corylus species by establishing a general phylogenetic framework with extensive sampling. Both the molecular phylogeny inferred from recombination-free dataset and structure analysis support the division of Corylus into four major clades (A–D). Recombination tests and hybridization detection reveal extensive recombination and hybridization events among different clades, which have potentially influenced the speciation process of Corylus. Divergence time estimation indicates that recent common ancestor (MRCA) of Corylus occurred in late Eocene (∼36.38 Ma) and subsequent rapid diversification began during Miocene. Ancestral area reconstruction shows that Corylus originated from southwest China. The arrival of two clades (Clades B and C) to NA was well supported by the long distance dispersal crossing the Bering land bridge. The Himalayas, European-Mediterranean area, and other distribution regions are primarily the recipients of dispersal taxa. Vicariance after dispersal plays an important role in speciation.

A reference haplotype panel for genome-wide imputation of short tandem repeats

Short tandem repeats (STRs) are involved in dozens of Mendelian disorders and have been implicated in complex traits. However, genotyping arrays used in genome-wide association studies focus on single nucleotide polymorphisms (SNPs) and do not readily allow identification of STR associations. We leverage next-generation sequencing (NGS) from 479 families to create a SNP + STR reference haplotype panel. Our panel enables imputing STR genotypes into SNP array data when NGS is not available for directly genotyping STRs. Imputed genotypes achieve mean concordance of 97% with observed genotypes in an external dataset compared to 71% expected under a naive model. Performance varies widely across STRs, with near perfect concordance at bi-allelic STRs vs. 70% at highly polymorphic repeats. Imputation increases power over individual SNPs to detect STR associations with gene expression. Imputing STRs into existing SNP datasets will enable the first large-scale STR association studies across a range of complex traits.

Population genetic structure of the deep-sea mussel Bathymodiolus platifrons (Bivalvia: Mytilidae) in the Northwest Pacific.

Studying population genetics of deep‐sea animals helps us understand their history of habitat colonization and population divergence. Here, we report a population genetic study of the deep‐sea mussel Bathymodiolus platifrons (Bivalvia: Mytilidae) widely distributed in chemosynthesis‐based ecosystems in the Northwest Pacific. Three mitochondrial genes (i.e., atp6, cox1, and nad4) and 6,398 genomewide single nucleotide polymorphisms (SNPs) were obtained from 110 individuals from four hydrothermal vents and two methane seeps. When using the three mitochondrial genes, nearly no genetic differentiation was detected for B. platifrons in the Northwest Pacific. Nevertheless, when using SNP datasets, all individuals in the South China Sea (SCS) and three individuals in Sagami Bay (SB) together formed one genetic cluster that was distinct from the remaining individuals. Such genetic divergence indicated a genetic barrier to gene flow between the SCS and the open Northwest Pacific, resulting in the co‐occurrence of two cryptic semi‐isolated lineages. When using 125 outlier SNPs identified focusing on individuals in the Okinawa Trough (OT) and SB, a minor genetic subdivision was detected between individuals in the southern OT (S‐OT) and those in the middle OT (M‐OT) and SB. This result indicated that, although under the influence of the Kuroshio Current and the North Pacific Intermediate Water, subtle geographic barriers may exist between the S‐OT and the M‐OT. Introgression analyses based on these outlier SNPs revealed that Hatoma Knoll in the S‐OT represents a possible contact zone for individuals in the OT‐SB region. Furthermore, migration dynamic analyses uncovered stronger gene flow from Dai‐yon Yonaguni Knoll in the S‐OT to the other local populations, compared to the reverse directions. Taken together, the present study offered novel perspectives on the genetic connectivity of B. platifrons mussels, revealing the potential interaction of ocean currents and geographic barriers with adaption and reproductive isolation in shaping their migration patterns and genetic differentiation in the Northwest Pacific.

Genome sequencing and conservation genomics in the Scandinavian wolverine population.

Genetic approaches have proved valuable to the study and conservation of endangered populations, especially for monitoring programs, and there is potential for further developments in this direction by extending analyses to the genomic level. We assembled the genome of the wolverine (Gulo gulo), a mustelid that in Scandinavia has recently recovered from a significant population decline, and obtained a 2.42Gb draft sequence representing >85% of the genome and including >21,000protein-coding genes. We then performed whole-genome resequencing of 10Scandinavian wolverines for population genomic and demographic analyses. Genetic diversity was among the lowest detected in a red-listed population (mean genome-wide nucleotide diversity of 0.05%). Results of the demographic analyses indicated a long-term decline of the effective population size (Ne ) from 10,000well before the last glaciation to <500 after this period. Current Ne appeared even lower. The genome-wide FIS level was 0.089 (possibly signaling inbreeding), but this effect was not observed when analyzing a set of highly variable SNP markers, illustrating that such markers can give a biased picture of the overall character of genetic diversity. We found significant population structure, which has implications for population connectivity and conservation. We used an integrated microfluidic circuit chip technology to develop an SNP-array consisting of 96 highly informative markers that, together with a multiplex pre-amplification step, was successfully applied to low-quality DNA from scat samples. Our findings will inform management, conservation, and genetic monitoring of wolverines and serve as a genomic roadmap that can be applied to other endangered species. The approach used here can be generally utilized in other systems, but we acknowledge the trade-off between investing in genomic resources and direct conservation actions.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers.

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Identification of a large SNP dataset in Larimichthys crocea using specific-locus amplified fragment sequencing.

The large yellow croaker, Larimichthys crocea, is a commercially important drum fish (Family: Sciaenidae) native to the East and South China Sea. Habitat deterioration and overfishing have led to significant population decline and the collapse of its fishery over the past decades. Today, the market supply of L.crocea depends solely on stocks produced in hatcheries and farms. Common issues that occur in the culture of L.crocea include germplasm degradation, precocious puberty, elevated disease susceptibility and growth retardation. In this study, we employed SLAF-seq (specific-locus amplified fragment sequencing) technology to identify single nucleotide polymorphism (SNP) loci across the L.crocea genome. Sixty samples were selected for SLAF analysis out of 1000 progeny in the same cohort of a cultured stock. Our analysis obtained a total of 151253 SLAFs, of which 65.88% (99652) were identified to be polymorphic, scoring a total of 710567 putative SNPs. Further filtration resulted in a final panel of 1782 SNP loci. The data derived from this work could be beneficial for understanding the genetics of complex phenotypic traits as well as for developing marker-selection-assisted breeding programs in L.crocea.

Comprehensive evaluation of genetic population structure for anadromous river herring with single nucleotide polymorphism data

Anthropogenic activities are placing increasing pressure on many species, particularly those that rely on more than one ecosystem. River herring (alewife, Alosa pseudoharengus and blueback herring, A. aestivalis collectively) are anadromous fishes that reproduce in rivers and streams of eastern North America and migrate to the western Atlantic Ocean. Here, we use data from single nucleotide polymorphisms (SNPs) to provide a comprehensive analysis of population structure for both species of river herring throughout their native ranges. We sampled river herring spawning runs in rivers from Newfoundland to Florida, examining a total of 108 locations, and genotyping over 8000 fish. We identified geographic population groupings (regional genetic groups) in each species, as well as significant genetic differentiation between most populations and rivers. Strong correlations between geographic and genetic distances (i.e., isolation by distance) were found range-wide for both species, although the patterns were less consistent at smaller spatial scales. River herring are caught as bycatch in fisheries and estimating stock proportions in mixed fishery samples is important for management. We assessed the utility of the SNP datasets as reference baselines for genetic stock identification. Results indicated high accuracy of individual assignment (76–95%) to designated regional genetic groups, and some individual populations, as well as highly accurate estimates of mixing proportions for both species. This study is the first to evaluate genetic structure across the entire geographic range of these species and provides an important foundation for conservation and management planning. The SNP reference datasets will facilitate continued multi-lateral monitoring of bycatch, as well as ecological investigation to provide information about ocean dispersal patterns of these species.

Genotyping by Sequencing Reasserts the Close Relationship between Tef and Its Putative Wild Eragrostis Progenitors

The genus Eragrostis consists of 350 species, including tef (Eragrostis tef (Zucc.) Trotter), the only cultivated species in this genus. Very little is known about the genetic potential of these species for tef improvement and genomics research. Here, we investigated a germplasm panel consisting of 40 Eragrostis species and 42 tef lines with single nucleotide polymorphism (SNP) data generated using the genotyping by sequencing (GBS) protocol. Thousands of SNPs were identified genome-wide from the germplasm panel. High-quality SNPs were used to assess sequence similarity and/or divergence, genetic diversity, population structure, and phylogenetic relationships. Mapping individual reads to the tef reference genome revealed that of the 40 wild Eragrostis species included in this study, E. pilosa, E. aethiopica, E. obtusa, E. ferruginea, E. lugens, and E. lehmanniana had 92% of their sequences represented in the tef reference genome. In the maximum likelihood phylogenetic analysis, these wild species clearly showed grouping in the clade consisting of the entire tef germplasm. Population structure analysis showed two major clusters consistent with the germplasm class information and the inferred phylogenetic relationships. The wild Eragrostis species were more diverse than the tef cultivars and could therefore potentially be used to enrich the tef gene pool. The SNP dataset and the results documented here are taxonomically the most inclusive to date and could be a useful informational tool for the design of genomics-informed tef breeding and research.

Development of cost-effective single nucleotide polymorphism marker assays for genetic diversity analysis in Brassica rapa

Competitive allele-specific PCR (KASPar) assay is a user-friendly system that provides flexibility in the numbers of single nucleotide polymorphisms (SNPs) and genotypes. Based on Illumina-GA-IIx genomic data from 10 genotypes with a broad genetic background, 3183 SNPs were selected for KASPar assays development, and 568 were finally converted and selected for Brassica rapa germplasm characterization (17.8%) on the basis of reproducibility, missing data rate, and uniform genetic distribution. High levels of polymorphism of these markers across 231 B. rapa genotypes were verified, illustrating by high polymorphic information content (averaged 0.34), minor allele frequency (0.37), genetic diversity (0.45), and the low observed heterozygosity (0.10). Based on the SNP dataset, structure and principal coordinates analysis, and neighbor-joining phylogenetic methods were used to examine the population structure and gave highly consistent results. The 231 accessions were divided into the four primary subspecies, representing 99 accessions from B. rapa ssp. pekinensis, 85 from B. rapa ssp. chinensis, 30 from B. rapa ssp. rapifera, and 17 from B. rapa ssp. oleifera and were further subdivided into 12 lower-order clusters according to different morphotypes. The genetic variability and pairwise fixation index analysis revealed that the ssp. pekinensis accessions possess the most extensive genetic variation among the four subspecies. The KASPar system is highly useful for validating SNPs and will be valuable for genetics research and breeding applications in B. rapa.

Collective effects of common SNPs and risk prediction in lung cancer

Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r2 = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.

Validation of genotype imputation in Southeast Asian populations and the effect of single nucleotide polymorphism annotation on imputation outcome

BackgroundImputation involves the inference of untyped single nucleotide polymorphisms (SNPs) in genome-wide association studies. The haplotypic reference of choice for imputation in Southeast Asian populations is unclear. Moreover, the influence of SNP annotation on imputation results has not been examined.MethodsThis study was divided into two parts. In the first part, we applied imputation to genotyped SNPs from Southeast Asian populations from the Pan-Asian SNP database. Five percent of the total SNPs were removed. The remaining SNPs were applied to imputation with IMPUTE2. The imputed outcomes were verified with the removed SNPs. We compared imputation references from Chinese and Japanese haplotypes from the HapMap phase II (HMII) and the complete set of haplotypes from the 1000 Genomes Project (1000G). The second part was imputation accuracy and yield in Thai patient dataset. Half of the autosomal SNPs was removed to create Set 1. Another dataset, Set 2, was then created where we switched which half of the SNPs were removed. Both Set 1 and Set 2 were imputed with HMII to create a complete imputed SNPs dataset. The dataset was used to validate association testing, SNPs annotation and imputation outcome.ResultsThe accuracy was highest for all populations when using the HMII reference, but at the cost of a lower yield. Thai genotypes showed the highest accuracy over other populations in both HMII and 1000G panels, although accuracy and yield varied across chromosomes. Imputation was tested in a clinical dataset to compare accuracy in gene-related regions, and coding regions were found to have a higher accuracy and yield.ConclusionsThis work provides the first evidence of imputation reference selection for Southeast Asian studies and highlights the effects of SNP locations respective to genes on imputation outcome. Researchers will need to consider the trade-off between accuracy and yield in future imputation studies.

Exploring the phylogeography of a hexaploid freshwater fish by RAD sequencing.

The KwaZulu‐Natal yellowfish (Labeobarbus natalensis) is an abundant cyprinid, endemic to KwaZulu‐Natal Province, South Africa. In this study, we developed a single‐nucleotide polymorphism (SNP) dataset from double‐digest restriction site‐associated DNA (ddRAD) sequencing of samples across the distribution. We addressed several hidden challenges, primarily focusing on proper filtering of RAD data and selecting optimal parameters for data processing in polyploid lineages. We used the resulting high‐quality SNP dataset to investigate the population genetic structure of L. natalensis. A small number of mitochondrial markers present in these data had disproportionate influence on the recovered genetic structure. The presence of singleton SNPs also confounded genetic structure. We found a well‐supported division into northern and southern lineages, with further subdivision into five populations, one of which reflects north–south admixture. Approximate Bayesian Computation scenario testing supported a scenario where an ancestral population diverged into northern and southern lineages, which then diverged to yield the current five populations. All river systems showed similar levels of genetic diversity, which appears unrelated to drainage system size. Nucleotide diversity was highest in the smallest river system, the Mbokodweni, which, together with adjacent small coastal systems, should be considered as a key catchment for conservation.

A Hybrid Feature Selection Method for Complex Diseases SNPs

Machine learning techniques have the potential to revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability; thus, they have been implicated in several human diseases. To separate the affected samples from the normal ones, various techniques have been applied on SNPs. Achieving high classification accuracy in such a high-dimensional space is crucial for successful diagnosis and treatment. In this work, we propose an accurate hybrid feature selection method for detecting the most informative SNPs and selecting an optimal SNP subset. The proposed method is based on the fusion of a filter and a wrapper method, i.e., the Conditional Mutual Information Maximization (CMIM) method and the support vector machine-recursive feature elimination, respectively. The performance of the proposed method was evaluated against four state-of-the-art feature selection methods, minimum redundancy maximum relevancy, fast correlation-based feature selection, CMIM, and ReliefF, using four classifiers, support vector machine, naive Bayes, linear discriminant analysis, and $k$ nearest neighbors on five different SNP data sets obtained from the National Center for Biotechnology Information gene expression omnibus genomics data repository. The experimental results demonstrate the efficiency of the adopted feature selection approach outperforming all of the compared feature selection algorithms and achieving up to 96% classification accuracy for the used data set. In general, from these results we conclude that SNPs of the whole genome can be efficiently employed to distinguish affected individuals with complex diseases from the healthy ones.

Population genomics and comparisons of selective signatures in two invasions of melon fly, Bactrocera cucurbitae (Diptera: Tephritidae)

Population genetics is a powerful tool for invasion biology and pest management, and useful for a range of questions from tracing invasion pathways to informing management decisions with inference of population demographics. Genomics greatly increases the resolution of population-scale analyses, yet outside of model species with extensive genomic resources, few studies have used population genomics in invasion biology. We use genome-wide single nucleotide polymorphisms (SNPs) to investigate population genomic structure with samples from across the range of melon fly, Bactrocera cucurbitae (Coquillett, 1849), a highly polyphagous pest of commercial produce. We then make use of a chromosome-scale genome assembly and gene set to compare signatures of selection across the melon fly’s genome, both across our sampling as a whole and in the context of two independent, established introductions. Using multiple approaches, we find support for six genetic clusters across melon fly’s distribution. Some of these agree with previously identified genetic clusters using microsatellites, but consensus of clusters in mainland and oceanic southeast Asia is confounded by variable sampling between studies. We find few adaptive signatures across the genome, and virtually no unique signatures when comparing the two independent introductions, which suggests that similar management strategies are appropriate across melon fly’s range. This is the first use of genome-wide data to characterize population structure in tephritid fruit fly pests, and our SNP dataset provides a foundation for objective and cost-effective genotyping of previously collected melon fly specimens. Future research needs to focus on truly comprehensive sampling across melon fly’s range to overcome the historic variability of range-wide estimates of population structure for this pest.

Global genotype flow in Cercospora beticola populations confirmed through genotyping-by-sequencing.

Genotyping-by-sequencing (GBS) was conducted on 333 Cercospora isolates collected from Beta vulgaris (sugar beet, table beet and swiss chard) in the USA and Europe. Cercospora beticola was confirmed as the species predominantly isolated from leaves with Cercospora leaf spot (CLS) symptoms. However, C. cf. flagellaris also was detected at a frequency of 3% in two table beet fields in New York. Resolution of the spatial structure and identification of clonal lineages in C. beticola populations using genome-wide single nucleotide polymorphisms (SNPs) obtained from GBS was compared to genotyping using microsatellites. Varying distance thresholds (bitwise distance = 0, 1.854599 × 10−4, and 1.298 × 10−3) were used for delineation of clonal lineages in C. beticola populations. Results supported previous reports of long distance dispersal of C. beticola through genotype flow. The GBS-SNP data set provided higher resolution in discriminating clonal lineages; however, genotype identification was impacted by filtering parameters and the distance threshold at which the multi-locus genotypes (MLGs) were contracted to multi-locus lineages. The type of marker or different filtering strategies did not impact estimates of population differentiation and structure. Results emphasize the importance of robust filtering strategies and designation of distance thresholds for delineating clonal lineages in population genomics analyses that depend on individual assignment and identification of clonal lineages. Detection of recurrent clonal lineages shared between the USA and Europe, even in the relaxed-filtered SNP data set and with a conservative distance threshold for contraction of MLGs, provided strong evidence for global genotype flow in C. beticola populations. The implications of intercontinental migration in C. beticola populations for CLS management are discussed.

Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian Tiger Mosquito, Aedes albopictus.

Aedes albopictus is a highly invasive disease vector with an expanding worldwide distribution. Genetic assays using low to medium resolution markers have found little evidence of spatial genetic structure even at broad geographic scales, suggesting frequent passive movement along human transportation networks. Here we analysed genetic structure of Aedes albopictus collected from 12 sample sites in Guangzhou, China, using thousands of genome-wide single nucleotide polymorphisms (SNPs). We found evidence for passive gene flow, with distance from shipping terminals being the strongest predictor of genetic distance among mosquitoes. As further evidence of passive dispersal, we found multiple pairs of full-siblings distributed between two sample sites 3.7 km apart. After accounting for geographical variability, we also found evidence for isolation by distance, previously undetectable in Ae. albopictus. These findings demonstrate how large SNP datasets and spatially-explicit hypothesis testing can be used to decipher processes at finer geographic scales than formerly possible. Our approach can be used to help predict new invasion pathways of Ae. albopictus and to refine strategies for vector control that involve the transformation or suppression of mosquito populations.