Signatures of selection in the Iberian honey bee (Apis mellifera iberiensis) revealed by a genome scan analysis of single nucleotide polymorphisms

The impact of geography and climate on the population structure and local adaptation in a wild bee

Deciphering processes that contribute to genetic differentiation and divergent selection of natural populations is useful for evaluating the adaptive potential and resilience of organisms faced with various anthropogenic stressors. Insect pollinator species, including wild bees, provide critical ecosystem services but are highly susceptible to biodiversity declines. Here, we use population genomics to infer the genetic structure and test for evidence of local adaptation in an economically important native pollinator, the small carpenter bee (Ceratina calcarata). Using genome-wide SNP data (n = 8302), collected from specimens across the species' entire distribution, we evaluated population differentiation and genetic diversity and identified putative signatures of selection in the context of geographic and environmental variation. Results of the analyses of principal component and Bayesian clustering were concordant with the presence of two to three genetic clusters, associated with landscape features and inferred phylogeography of the species. All populations examined in our study demonstrated a heterozygote deficit, along with significant levels of inbreeding. We identified 250 robust outlier SNPs, corresponding to 85 annotated genes with known functional relevance to thermoregulation, photoperiod, and responses to various abiotic and biotic stressors. Taken together, these data provide evidence for local adaptation in a wild bee and highlight genetic responses of native pollinators to landscape and climate features.

Using the Software DeepWings© to Classify Honey Bees across Europe through Wing Geometric Morphometrics

DeepWings© is a software that uses machine learning to automatically classify honey bee subspecies by wing geometric morphometrics. Here, we tested the five subspecies classifier (A. m. carnica, Apis mellifera caucasia, A. m. iberiensis, Apis mellifera ligustica, and A. m. mellifera) of DeepWings© on 14,816 wing images with variable quality and acquired by different beekeepers and researchers. These images represented 2601 colonies from the native ranges of the M-lineage A. m. iberiensis and A. m. mellifera, and the C-lineage A. m. carnica. In the A. m. iberiensis range, 92.6% of the colonies matched this subspecies, with a high median probability (0.919). In the Azores, where the Iberian subspecies was historically introduced, a lower proportion (85.7%) and probability (0.842) were observed. In the A. m mellifera range, only 41.1 % of the colonies matched this subspecies, which is compatible with a history of C-derived introgression. Yet, these colonies were classified with the highest probability (0.994) of the three subspecies. In the A. m. carnica range, 88.3% of the colonies matched this subspecies, with a probability of 0.984. The association between wing and molecular markers, assessed for 1214 colonies from the M-lineage range, was highly significant but not strong (r = 0.31, p < 0.0001). The agreement between the markers was influenced by C-derived introgression, with the best results obtained for colonies with high genetic integrity. This study indicates the good performance of DeepWings© on a realistic wing image dataset.

A SNP assay for assessing diversity in immune genes in the honey bee (Apis mellifera L.)

With a growing number of parasites and pathogens experiencing large-scale range expansions, monitoring diversity in immune genes of host populations has never been so important because it can inform on the adaptive potential to resist the invaders. Population surveys of immune genes are becoming common in many organisms, yet they are missing in the honey bee (Apis mellifera L.), a key managed pollinator species that has been severely affected by biological invasions. To fill the gap, here we identified single nucleotide polymorphisms (SNPs) in a wide range of honey bee immune genes and developed a medium-density assay targeting a subset of these genes. Using a discovery panel of 123 whole-genomes, representing seven A. mellifera subspecies and three evolutionary lineages, 180 immune genes were scanned for SNPs in exons, introns (< 4 bp from exons), 3' and 5´UTR, and < 1 kb upstream of the transcription start site. After application of multiple filtering criteria and validation, the final medium-density assay combines 91 quality-proved functional SNPs marking 89 innate immune genes and these can be readily typed using the high-sample-throughput iPLEX MassARRAY system. This medium-density-SNP assay was applied to 156 samples from four countries and the admixture analysis clustered the samples according to their lineage and subspecies, suggesting that honey bee ancestry can be delineated from functional variation. In addition to allowing analysis of immunogenetic variation, this newly-developed SNP assay can be used for inferring genetic structure and admixture in the honey bee.

Mitochondrial DNA Variation in Peruvian Honey Bee (Apis mellifera L.) Populations Using the tRNAleu-cox2 Intergenic Region

Simple Summary

Currently, the genetic diversity of Peruvian honey bee populations is unknown. Only two studies were carried out and suggest that many regions of Peru present Africanized honey bee colonies and a varied degree of Africanization. To molecularly characterize and know more about the genetic background of Peruvian honey bees, the highly polymorphic tRNAleu-cox2 was used. This study analyzed 512 colonies in three regions of Peru: Lima, Piura, and Junín. The results indicated that 65% colonies correspond to lineage A (African), 33.8% colonies to lineage C (Eastern European), and 1.2% colonies to lineage M (Western European). A total of 24 haplotypes were identified: 16 haplotypes belong to lineage A (sub-lineage A_I (13), sub-lineage A_III (03)), lineage C (06), and lineage M (02), and 15 of them are for the first time reported and represented by A1t, A1u, A1w, A4p, A4q, A4s, A4t, A4u, A4v, A4w, 30d, A30e, A65, M7b, and M7c. Piura and Lima presented higher proportions of African haplotypes and lower proportions of haplotypes from lineage C than Lima. Very few haplotypes of lineage M were identified, whose presence could be due to accidental purchases or traces of honey bee introductions from lineage M in the 19th century. Hence, studies about the diversity and genetic structure of Peruvian honey bee populations are necessary to promote adequate, sustainable management and establish conservation and breeding programs.

Abstract

Mitochondrial DNA variations of Peruvian honey bee populations were surveyed by using the tRNA^leu-cox2 intergenic region. Only two studies have characterized these populations, indicating the presence of Africanized honey bee colonies in different regions of Peru and varied levels of Africanization, but the current status of its genetic diversity is unknown. A total of 512 honey bee colonies were sampled from three regions to characterize them. Our results revealed the presence of European and African haplotypes: the African haplotypes identified belong to sub-lineage A_I (13) and sub-lineage A_III (03), and the European haplotypes to lineages C (06) and M (02). Of 24 haplotypes identified, 15 new sequences are reported here (11 sub-lineage A_I, 2 sub-lineage A_III, and 2 lineage M). Peruvian honey bee populations presented a higher proportion from African than European haplotypes. High proportions of African haplotype were reported for Piura and Junín, unlike Lima, which showed more European haplotypes from lineage C. Few colonies belonging to lineage M would represent accidental purchase or traces of the introduction into Peru in the 19th century.

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

BACKGROUND

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F_ST) to select the most informative SNPs for ancestry inference.

RESULTS

Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.

CONCLUSIONS

The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

The Effect of Migratory Beekeeping on the Infestation Rate of Parasites in Honey Bee (Apis mellifera) Colonies and on Their Genetic Variability

Migratory beekeeping is a widely extended practice aimed at increasing the yield of products and pollination services of honey bee colonies. However, it represents a stress factor, as it facilitates the dissemination of diseases and may compromise the genetic identity of the colonies involved. To analyze the extent of these effects, pathogens infestation rate and genetic composition were monitored in a field experiment comparing stationary and migratory colonies sharing the same environmental conditions but differing in management (stationary vs. migratory) and genetic background. We studied the pathogens infestation rate (Varroa destructor, Nosema spp., and Deformed Wing Virus (DWV)) at four different times: before migratory operation, two weeks later, at the end of the migratory period, and two weeks after the return of the migratory hives. An increased incidence of V. destructor and Nosema ceranae and a lower DWV viral load were found in migratory colonies. Temporary changes in genetic diversity were detected regardless of colony type, suggesting that stressors other than management affect the genetic diversity of the colonies. Our study demonstrates that migratory practices have variable effects on the health and genetic diversity of honey bee colonies, which should be taken into account for the development of sustainable beekeeping.

Seascape genomics reveals population isolation in the reef-building honeycomb worm, Sabellaria alveolata (L.)

BACKGROUND

Under the threat of climate change populations can disperse, acclimatise or evolve in order to avoid fitness loss. In light of this, it is important to understand neutral gene flow patterns as a measure of dispersal potential, but also adaptive genetic variation as a measure of evolutionary potential. In order to assess genetic variation and how this relates to environment in the honeycomb worm (Sabellaria alveolata (L.)), a reef-building polychaete that supports high biodiversity, we carried out RAD sequencing using individuals from along its complete latitudinal range. Patterns of neutral population genetic structure were compared to larval dispersal as predicted by ocean circulation modelling, and outlier analyses and genotype-environment association tests were used to attempt to identify loci under selection in relation to local temperature data.

RESULTS

We genotyped 482 filtered SNPs, from 68 individuals across nine sites, 27 of which were identified as outliers using BAYESCAN and ARLEQUIN. All outlier loci were potentially under balancing selection, despite previous evidence of local adaptation in the system. Limited gene flow was observed among reef-sites (FST = 0.28 ± 0.10), in line with the low dispersal potential identified by the larval dispersal models. The North Atlantic reef emerged as a distinct population and this was linked to high local larval retention and the effect of the North Atlantic Current on dispersal.

CONCLUSIONS

As an isolated population, with limited potential for natural genetic or demographic augmentation from other reefs, the North Atlantic site warrants conservation attention in order to preserve not only this species, but above all the crucial functional ecological roles that are associated with their bioconstructions. Our study highlights the utility of using seascape genomics to identify populations of conservation concern.

Speculations on the evolution of humoral adaptive immunity

The protection of a multicellular organism from infection, at both cell and humoral levels, has been a tremendous driver of gene selection and cellular response strategies. Here we focus on a critical event in the development of humoral immunity: The transition from principally innate responses to a system of adaptive cell selection, with all the attendant mechanical problems that must be solved in order for it to work effectively. Here we review recent advances, but our major goal is to highlight that the development of adaptive immunity resulted from the adoption, reuse and repurposing of an ancient, autonomous cellular program that combines and exploits three titratable cellular fate timers. We illustrate how this common cell machinery recurs and appears throughout biology, and has been essential for the evolution of complex organisms, at many levels of scale.

G. Teixeira J, Ferreira H, J. Neves C, Francoy TM, Pinto MA. Wing Geometric Morphometrics of Workers and Drones and Single Nucleotide Polymorphisms Provide Similar Genetic Structure in the Iberian Honey Bee (Apis mellifera iberiensis)

Wing geometric morphometrics has been applied to honey bees (Apis mellifera) in identification of evolutionary lineages or subspecies and, to a lesser extent, in assessing genetic structure within subspecies. Due to bias in the production of sterile females (workers) in a colony, most studies have used workers leaving the males (drones) as a neglected group. However, considering their importance as reproductive individuals, the use of drones should be incorporated in these analyses in order to better understand diversity patterns and underlying evolutionary processes. Here, we assessed the usefulness of drone wings, as well as the power of wing geometric morphometrics, in capturing the signature of complex evolutionary processes by examining wing shape data, integrated with geographical information, from 711 colonies sampled across the entire distributional range of Apis mellifera iberiensis in Iberia. We compared the genetic patterns reconstructed from spatially-explicit shape variation extracted from wings of both sexes with that previously reported using 383 genome-wide SNPs (single nucleotide polymorphisms). Our results indicate that the spatial structure retrieved from wings of drones and workers was similar (r = 0.93) and congruent with that inferred from SNPs (r = 0.90 for drones; r = 0.87 for workers), corroborating the clinal pattern that has been described for A. m. iberiensis using other genetic markers. In addition to showing that drone wings carry valuable genetic information, this study highlights the capability of wing geometric morphometrics in capturing complex genetic patterns, offering a reliable and low-cost alternative for preliminary estimation of population structure.

Landscape genomics to the rescue of a tropical bee threatened by habitat loss and climate change

Habitat degradation and climate change are currently threatening wild pollinators, compromising their ability to provide pollination services to wild and cultivated plants. Landscape genomics offers powerful tools to assess the influence of landscape modifications on genetic diversity and functional connectivity, and to identify adaptations to local environmental conditions that could facilitate future bee survival. Here, we assessed range-wide patterns of genetic structure, genetic diversity, gene flow, and local adaptation in the stingless bee Melipona subnitida, a tropical pollinator of key biological and economic importance inhabiting one of the driest and hottest regions of South America. Our results reveal four genetic clusters across the species' full distribution range. All populations were found to be under a mutation-drift equilibrium, and genetic diversity was not influenced by the amount of reminiscent natural habitats. However, genetic relatedness was spatially autocorrelated and isolation by landscape resistance explained range-wide relatedness patterns better than isolation by geographic distance, contradicting earlier findings for stingless bees. Specifically, gene flow was enhanced by increased thermal stability, higher forest cover, lower elevations, and less corrugated terrains. Finally, we detected genomic signatures of adaptation to temperature, precipitation, and forest cover, spatially distributed in latitudinal and altitudinal patterns. Taken together, our findings shed important light on the life history of M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Conservation actions such as restricting long-distance colony transportation, preserving local adaptations, and improving the connectivity between highlands and lowlands are likely to assure future pollination services.

Rapid seasonal evolution in innate immunity of wild Drosophila melanogaster

Understanding the rate of evolutionary change and the genetic architecture that facilitates rapid adaptation is a current challenge in evolutionary biology. Comparative studies show that genes with immune function are among the most rapidly evolving genes across a range of taxa. Here, we use immune defence in natural populations of Drosophila melanogaster to understand the rate of evolution in natural populations and the genetics underlying rapid change. We probed the immune system using the natural pathogens Enterococcus faecalis and Providencia rettgeri to measure post-infection survival and bacterial load of wild D. melanogaster populations collected across seasonal time along a latitudinal transect along eastern North America (Massachusetts, Pennsylvania and Virginia). There are pronounced and repeatable changes in the immune response over the approximately 10 generations between spring and autumn collections, with a significant but less distinct difference observed among geographical locations. Genes with known immune function are not enriched among alleles that cycle with seasonal time, but the immune function of a subset of seasonally cycling alleles in immune genes was tested using reconstructed outbred populations. We find that flies containing seasonal alleles in Thioester-containing protein 3 (Tep3) have different functional responses to infection and that epistatic interactions among seasonal Tep3 and Drosomycin-like 6 (Dro6) alleles underlie the immune phenotypes observed in natural populations. This rapid, cyclic response to seasonal environmental pressure broadens our understanding of the complex ecological and genetic interactions determining the evolution of immune defence in natural populations.

Developing reduced SNP assays from whole-genome sequence data to estimate introgression in an organism with complex genetic patterns, the Iberian honeybee (Apis mellifera iberiensis)

The most important managed pollinator, the honeybee (Apis mellifera L.), has been subject to a growing number of threats. In western Europe, one such threat is large-scale introductions of commercial strains (C-lineage ancestry), which is leading to introgressive hybridization and even the local extinction of native honeybee populations (M-lineage ancestry). Here, we developed reduced assays of highly informative SNPs from 176 whole genomes to estimate C-lineage introgression in the most diverse and evolutionarily complex subspecies in Europe, the Iberian honeybee (Apis mellifera iberiensis). We started by evaluating the effects of sample size and sampling a geographically restricted area on the number of highly informative SNPs. We demonstrated that a bias in the number of fixed SNPs (FST = 1) is introduced when the sample size is small (N ≤ 10) and when sampling only captures a small fraction of a population's genetic diversity. These results underscore the importance of having a representative sample when developing reliable reduced SNP assays for organisms with complex genetic patterns. We used a training data set to design four independent SNP assays selected from pairwise FST between the Iberian and C-lineage honeybees. The designed assays, which were validated in holdout and simulated hybrid data sets, proved to be highly accurate and can be readily used for monitoring populations not only in the native range of A. m. iberiensis in Iberia but also in the introduced range in the Balearic islands, Macaronesia and South America, in a time- and cost-effective manner. While our approach used the Iberian honeybee as model system, it has a high value in a wide range of scenarios for the monitoring and conservation of potentially hybridized domestic and wildlife populations.

Population genomics and morphometric assignment of western honey bees (Apis mellifera L.) in the Republic of South Africa

BACKGROUNDS

Apis mellifera scutellata and A.m. capensis (the Cape honey bee) are western honey bee subspecies indigenous to the Republic of South Africa (RSA). Both bees are important for biological and economic reasons. First, A.m. scutellata is the invasive "African honey bee" of the Americas and exhibits a number of traits that beekeepers consider undesirable. They swarm excessively, are prone to absconding (vacating the nest entirely), usurp other honey bee colonies, and exhibit heightened defensiveness. Second, Cape honey bees are socially parasitic bees; the workers can reproduce thelytokously. Both bees are indistinguishable visually. Therefore, we employed Genotyping-by-Sequencing (GBS), wing geometry and standard morphometric approaches to assess the genetic diversity and population structure of these bees to search for diagnostic markers that can be employed to distinguish between the two subspecies.

RESULTS

Apis mellifera scutellata possessed the highest mean number of polymorphic SNPs (among 2449 informative SNPs) with minor allele frequencies > 0.05 (Np = 88%). The RSA honey bees generated a high level of expected heterozygosity (Hexp = 0.24). The mean genetic differentiation (FST; 6.5%) among the RSA honey bees revealed that approximately 93% of the genetic variation was accounted for within individuals of these subspecies. Two genetically distinct clusters (K = 2) corresponding to both subspecies were detected by Model-based Bayesian clustering and supported by Principal Coordinates Analysis (PCoA) inferences. Selected highly divergent loci (n = 83) further reinforced a distinctive clustering of two subspecies across geographical origins, accounting for approximately 83% of the total variation in the PCoA plot. The significant correlation of allele frequencies at divergent loci with environmental variables suggested that these populations are adapted to local conditions. Only 17 of 48 wing geometry and standard morphometric parameters were useful for clustering A.m. capensis, A.m. scutellata, and hybrid individuals.

CONCLUSIONS

We produced a minimal set of 83 SNP loci and 17 wing geometry and standard morphometric parameters useful for identifying the two RSA honey bee subspecies by genotype and phenotype. We found that genes involved in neurology/behavior and development/growth are the most prominent heritable traits evolved in the functional evolution of honey bee populations in RSA. These findings provide a starting point for understanding the functional basis of morphological differentiations and ecological adaptations of the two honey bee subspecies in RSA.

Whole genome SNP-associated signatures of local adaptation in honeybees of the Iberian Peninsula

The availability of powerful high-throughput genomic tools, combined with genome scans, has helped identifying genes and genetic changes responsible for environmental adaptation in many organisms, including the honeybee. Here, we resequenced 87 whole genomes of the honeybee native to Iberia and used conceptually different selection methods (Samβada, LFMM, PCAdapt, iHs) together with in sillico protein modelling to search for selection footprints along environmental gradients. We found 670 outlier SNPs, most of which associated with precipitation, longitude and latitude. Over 88.7% SNPs laid outside exons and there was a significant enrichment in regions adjacent to exons and UTRs. Enrichment was also detected in exonic regions. Furthermore, in silico protein modelling suggests that several non-synonymous SNPs are likely direct targets of selection, as they lead to amino acid replacements in functionally important sites of proteins. We identified genomic signatures of local adaptation in 140 genes, many of which are putatively implicated in fitness-related functions such as reproduction, immunity, olfaction, lipid biosynthesis and circadian clock. Our genome scan suggests that local adaptation in the Iberian honeybee involves variations in regions that might alter patterns of gene expression and in protein-coding genes, which are promising candidates to underpin adaptive change in the honeybee.

The search for loci under selection: trends, biases and progress

Detecting genetic variants under selection using F_ST outlier analysis (OA) and environmental association analyses (EAAs) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analysed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographical coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics.

A Molecular Method for the Identification of Honey Bee Subspecies Used by Beekeepers in Russia

Apis mellifera L. includes several recognized subspecies that differ in their biological properties and agricultural characteristics. Distinguishing between honey bee subspecies is complicated. We analyzed the Folmer region of the COX1 gene in honey bee subspecies cultivated at bee farms in Russia and identified subspecies-specific SNPs. DNA analysis revealed two clearly distinct haplogroups in A. melliferamellifera. The first one was characterized by multiple cytosine-thymine (thymine–cytosine) transitions, one adenine-guanine substitution, and one thymine–adenine substitution. The nucleotide sequence of the second haplogroup coincided with sequences from other subspecies, except the unique C/A SNP at position 421 of the 658-bp Folmer region. A. melliferacarnica and A. melliferacarpatica could be distinguished from A. melliferamellifera and A. melliferacaucasica by the presence of the A/G SNP at position 99 of the 658-bp Folmer region. The G/A SNP at position 448 was typical for A. melliferacarnica. A. melliferacaucasicaCOX1 sequence lacked all the above-mentioned sites. We developed a procedure for rapid identification of honey bee subspecies by PCR with restriction fragment length polymorphism (RFLP) using mutagenic primers. The developed molecular method for honey bee subspecies identification is fast and inexpensive.

Characterize a typically Dscam with alternative splicing in mud crab Scylla paramamosain

As a member of the immunoglobulin superfamily, Down syndrome cell adhesion molecule (Dscam) could function in the innate immunity of invertebrates. Recently, it is shown that arthropod Dscams play similar functions as antibodies in the adaptive immune system. Dscam could produce thousands of isoforms by alternative splicing and specifically bind to various pathogens. In the present study, we cloned the first Dscam from mud crab Scylla paramamosain (SpDscam), with full-length cDNA 7363 bp containing an open reading frame (ORF) of 6069bp and encoding 2022 amino acids, which had typical domain architecture as other arthropods, i.e., 10 immunoglobulin domains (Ig), 6 fibronectin type 3 domains (FN III), transmembrane and cytoplasmic tail. Quantitative real-time PCR revealed that SpDscam was highly expressed in brain, skin, muscle, intestine and hepatopancreas, but weakly expressed in hemolymph, heart and gill. SpDscam had three alternative splicing regions, located at the N-terminal of Ig2 and Ig3 as well as on the whole Ig7. In these regions, 32, 41 and 14 exons were detected, together with the two exon types of transmembrane domain, indicating SpDscam could potentially encode at least 36,736 unique isoforms. SpDscam induced by Vibrio parahaemolyticus challenge had strong binding ability to V. parahaemolyticus. Further, SpDscam induced by V. parahaemolyticus possessed a clearance of V. parahaemolyticus in S. paramamosain. Collectively, the results indicated SpDscam was a hypervariable pattern-recognition receptor (PRR) by alternative splicing in innate immunity system of mud crab S. paramamosain.

SNPs selected by information content outperform randomly selected microsatellite loci for delineating genetic identification and introgression in the endangered dark European honeybee (Apis mellifera mellifera)

The honeybee (Apis mellifera) has been threatened by multiple factors including pests and pathogens, pesticides and loss of locally adapted gene complexes due to replacement and introgression. In western Europe, the genetic integrity of the native A. m. mellifera (M-lineage) is endangered due to trading and intensive queen breeding with commercial subspecies of eastern European ancestry (C-lineage). Effective conservation actions require reliable molecular tools to identify pure-bred A. m. mellifera colonies. Microsatellites have been preferred for identification of A. m. mellifera stocks across conservation centres. However, owing to high throughput, easy transferability between laboratories and low genotyping error, SNPs promise to become popular. Here, we compared the resolving power of a widely utilized microsatellite set to detect structure and introgression with that of different sets that combine a variable number of SNPs selected for their information content and genomic proximity to the microsatellite loci. Contrary to every SNP data set, microsatellites did not discriminate between the two lineages in the PCA space. Mean introgression proportions were identical across the two marker types, although at the individual level, microsatellites' performance was relatively poor at the upper range of Q-values, a result reflected by their lower precision. Our results suggest that SNPs are more accurate and powerful than microsatellites for identification of A. m. mellifera colonies, especially when they are selected by information content.

Survey of Global Genetic Diversity Within the Drosophila Immune System

Numerous studies across a wide range of taxa have demonstrated that immune genes are routinely among the most rapidly evolving genes in the genome. This observation, however, does not address what proportion of immune genes undergo strong selection during adaptation to novel environments. Here, we determine the extent of very recent divergence in genes with immune function across five populations of Drosophila melanogaster and find that immune genes do not show an overall trend of recent rapid adaptation. Our population-based approach uses a set of carefully matched control genes to account for the effects of demography and local recombination rate, allowing us to identify whether specific immune functions are putative targets of strong selection. We find evidence that viral-defense genes are rapidly evolving in Drosophila at multiple timescales. Local adaptation to bacteria and fungi is less extreme and primarily occurs through changes in recognition and effector genes rather than large-scale changes to the regulation of the immune response. Surprisingly, genes in the Toll pathway, which show a high rate of adaptive substitution between the D. melanogaster and D. simulans lineages, show little population differentiation. Quantifying the flies for resistance to a generalist Gram-positive bacterial pathogen, we found that this genetic pattern of low population differentiation was recapitulated at the phenotypic level. In sum, our results highlight the complexity of immune evolution and suggest that Drosophila immune genes do not follow a uniform trajectory of strong directional selection as flies encounter new environments.

Revisiting the Iberian honey bee (Apis mellifera iberiensis) contact zone: maternal and genome-wide nuclear variations provide support for secondary contact from historical refugia

Dissecting diversity patterns of organisms endemic to Iberia has been truly challenging for a variety of taxa, and the Iberian honey bee is no exception. Surveys of genetic variation in the Iberian honey bee are among the most extensive for any honey bee subspecies. From these, differential and complex patterns of diversity have emerged, which have yet to be fully resolved. Here, we used a genome-wide data set of 309 neutrally tested single nucleotide polymorphisms (SNPs), scattered across the 16 honey bee chromosomes, which were genotyped in 711 haploid males. These SNPs were analysed along with an intergenic locus of the mtDNA, to reveal historical patterns of population structure across the entire range of the Iberian honey bee. Overall, patterns of population structure inferred from nuclear loci by multiple clustering approaches and geographic cline analysis were consistent with two major clusters forming a well-defined cline that bisects Iberia along a northeastern-southwestern axis, a pattern that remarkably parallels that of the mtDNA. While a mechanism of primary intergradation or isolation by distance could explain the observed clinal variation, our results are more consistent with an alternative model of secondary contact between divergent populations previously isolated in glacial refugia, as proposed for a growing list of other Iberian taxa. Despite current intense honey bee management, human-mediated processes have seemingly played a minor role in shaping Iberian honey bee genetic structure. This study highlights the complexity of the Iberian honey bee patterns and reinforces the importance of Iberia as a reservoir of Apis mellifera diversity.

Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication

Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example F(ST), cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach.

Detecting recent selective sweeps while controlling for mutation rate and background selection

A composite likelihood ratio test implemented in the program sweepfinder is a commonly used method for scanning a genome for recent selective sweeps. sweepfinder uses information on the spatial pattern (along the chromosome) of the site frequency spectrum around the selected locus. To avoid confounding effects of background selection and variation in the mutation process along the genome, the method is typically applied only to sites that are variable within species. However, the power to detect and localize selective sweeps can be greatly improved if invariable sites are also included in the analysis. In the spirit of a Hudson–Kreitman–Aguadé test, we suggest adding fixed differences relative to an out‐group to account for variation in mutation rate, thereby facilitating more robust and powerful analyses. We also develop a method for including background selection, modelled as a local reduction in the effective population size. Using simulations, we show that these advances lead to a gain in power while maintaining robustness to mutation rate variation. Furthermore, the new method also provides more precise localization of the causative mutation than methods using the spatial pattern of segregating sites alone.

Effects of genotype, environment, and their interactions on honey bee health in Europe

There are several reports of honey bee populations in Europe which survive without treatment for Varroa. However, when evaluated outside their native area, higher survival and resistance traits were not observed in colonies of a survivor population. Varroa infestation is strongly influenced by environmental factors, probably affecting threshold levels on a European scale. In a Europe-wide experiment colonies of local origin survived significantly longer than colonies of non-local origin, clearly indicating the presence of genotype-environment interactions. Transmission by Varroa selects for virulent strains of DWV, but it is currently unknown how these may interact with different genotypes of bees. The distribution of Nosema ceranae is significantly affected by environment, but there is at least one Nosema-resistant population.

A SNP test to identify Africanized honeybees via proportion of 'African' ancestry

The honeybee, Apis mellifera, is the world's most important pollinator and is ubiquitous in most agricultural ecosystems. Four major evolutionary lineages and at least 24 subspecies are recognized. Commercial populations are mainly derived from subspecies originating in Europe (75-95%). The Africanized honeybee is a New World hybrid of A. m. scutellata from Africa and European subspecies, with the African component making up 50-90% of the genome. Africanized honeybees are considered undesirable for bee-keeping in most countries, due to their extreme defensiveness and poor honey production. The international trade in honeybees is restricted, due in part to bans on the importation of queens (and semen) from countries where Africanized honeybees are extant. Some desirable strains from the United States of America that have been bred for traits such as resistance to the mite Varroa destructor are unfortunately excluded from export to countries such as Australia due to the presence of Africanized honeybees in the USA. This study shows that a panel of 95 single nucleotide polymorphisms, chosen to differentiate between the African, Eastern European and Western European lineages, can detect Africanized honeybees with a high degree of confidence via ancestry assignment. Our panel therefore offers a valuable tool to mitigate the risks of spreading Africanized honeybees across the globe and may enable the resumption of queen and bee semen imports from the Americas.

Reduced SNP panels for genetic identification and introgression analysis in the dark honey bee (Apis mellifera mellifera)

Beekeeping activities, especially queen trading, have shaped the distribution of honey bee (Apis mellifera) subspecies in Europe, and have resulted in extensive introductions of two eastern European C-lineage subspecies (A. m. ligustica and A. m. carnica) into the native range of the M-lineage A. m. mellifera subspecies in Western Europe. As a consequence, replacement and gene flow between native and commercial populations have occurred at varying levels across western European populations. Genetic identification and introgression analysis using molecular markers is an important tool for management and conservation of honey bee subspecies. Previous studies have monitored introgression by using microsatellite, PCR-RFLP markers and most recently, high density assays using single nucleotide polymorphism (SNP) markers. While the latter are almost prohibitively expensive, the information gained to date can be exploited to create a reduced panel containing the most ancestry-informative markers (AIMs) for those purposes with very little loss of information. The objective of this study was to design reduced panels of AIMs to verify the origin of A. m. mellifera individuals and to provide accurate estimates of the level of C-lineage introgression into their genome. The discriminant power of the SNPs using a variety of metrics and approaches including the Weir & Cockerham’s F_ST, an F_ST-based outlier test, Delta, informativeness (I_n), and PCA was evaluated. This study shows that reduced AIMs panels assign individuals to the correct origin and calculates the admixture level with a high degree of accuracy. These panels provide an essential tool in Europe for genetic stock identification and estimation of admixture levels which can assist management strategies and monitor honey bee conservation programs.

Dscam and pancrustacean immune memory - a review of the evidence

Evidence is accumulating for a memory-like phenomenon in the immune defence of invertebrates. Down syndrome cell adhesion molecule (Dscam) has been proposed as a key candidate for a somatically diversified receptor system in the crustaceans and insects (Pancrustacea) that could enable challenge-specific protection. However, what is the evidence for an involvement of Dscam in pancrustacean immune memory, and in particular specificity? Here we review the current state of the art, and discuss hypotheses of how Dscam could be involved in immunity. We conclude that while there is increasing evidence for the involvement of Dscam in pancrustacean immunity, crucial experiments to address whether it plays a role in specificity upon secondary encounter with a pathogen still remain to be done.

On the prospect of identifying adaptive loci in recently bottlenecked populations

Identifying adaptively important loci in recently bottlenecked populations – be it natural selection acting on a population following the colonization of novel habitats in the wild, or artificial selection during the domestication of a breed – remains a major challenge. Here we report the results of a simulation study examining the performance of available population-genetic tools for identifying genomic regions under selection. To illustrate our findings, we examined the interplay between selection and demography in two species of Peromyscus mice, for which we have independent evidence of selection acting on phenotype as well as functional evidence identifying the underlying genotype. With this unusual information, we tested whether population-genetic-based approaches could have been utilized to identify the adaptive locus. Contrary to published claims, we conclude that the use of the background site frequency spectrum as a null model is largely ineffective in bottlenecked populations. Results are quantified both for site frequency spectrum and linkage disequilibrium-based predictions, and are found to hold true across a large parameter space that encompasses many species and populations currently under study. These results suggest that the genomic footprint left by selection on both new and standing variation in strongly bottlenecked populations will be difficult, if not impossible, to find using current approaches.

Population genomics of the honey bee reveals strong signatures of positive selection on worker traits

Most theories used to explain the evolution of eusociality rest upon two key assumptions: mutations affecting the phenotype of sterile workers evolve by positive selection if the resulting traits benefit fertile kin, and that worker traits provide the primary mechanism allowing social insects to adapt to their environment. Despite the common view that positive selection drives phenotypic evolution of workers, we know very little about the prevalence of positive selection acting on the genomes of eusocial insects. We mapped the footprints of positive selection in Apis mellifera through analysis of 40 individual genomes, allowing us to identify thousands of genes and regulatory sequences with signatures of adaptive evolution over multiple timescales. We found Apoidea- and Apis-specific genes to be enriched for signatures of positive selection, indicating that novel genes play a disproportionately large role in adaptive evolution of eusocial insects. Worker-biased proteins have higher signatures of adaptive evolution relative to queen-biased proteins, supporting the view that worker traits are key to adaptation. We also found genes regulating worker division of labor to be enriched for signs of positive selection. Finally, genes associated with worker behavior based on analysis of brain gene expression were highly enriched for adaptive protein and cis-regulatory evolution. Our study highlights the significant contribution of worker phenotypes to adaptive evolution in social insects, and provides a wealth of knowledge on the loci that influence fitness in honey bees.