The development and characterization of a 57K single nucleotide polymorphism array for rainbow trout

In this study, we describe the development and characterization of the first high‐density single nucleotide polymorphism (SNP) genotyping array for rainbow trout. The SNP array is publically available from a commercial vendor (Affymetrix). The SNP genotyping quality was high, and validation rate was close to 90%. This is comparable to other farm animals and is much higher than previous smaller scale SNP validation studies in rainbow trout. High quality and integrity of the genotypes are evident from sample reproducibility and from nearly 100% agreement in genotyping results from other methods. The array is very useful for rainbow trout aquaculture populations with more than 40 900 polymorphic markers per population. For wild populations that were confounded by a smaller sample size, the number of polymorphic markers was between 10 577 and 24 330. Comparison between genotypes from individual populations suggests good potential for identifying candidate markers for populations' traceability. Linkage analysis and mapping of the SNPs to the reference genome assembly provide strong evidence for a wide distribution throughout the genome with good representation in all 29 chromosomes. A total of 68% of the genome scaffolds and contigs were anchored through linkage analysis using the SNP array genotypes, including ~20% of the genome assembly that has not been previously anchored to chromosomes.     

Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection

Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in one or two generations at the rate of 20% to 25% per generation. When the reference population is updated by 1% or 5% every generation, the decay in accuracy was 8% to 11% after seven generations using true and imputed genotypes. These results indicate that imputed genotypes provide a viable alternative, even after several generations, as long the reference and training populations are appropriately updated to reflect the genetic change in the population.     

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

Understanding the genetic mechanisms of adaptive population divergence is one of the most fundamental endeavours in evolutionary biology and is becoming increasingly important as it will allow predictions about how organisms will respond to global environmental crisis. This is particularly important for the honey bee, a species of unquestionable ecological and economical importance that has been exposed to increasing human‐mediated selection pressures. Here, we conducted a single nucleotide polymorphism (SNP)‐based genome scan in honey bees collected across an environmental gradient in Iberia and used four F_ST‐based outlier tests to identify genomic regions exhibiting signatures of selection. Additionally, we analysed associations between genetic and environmental data for the identification of factors that might be correlated or act as selective pressures. With these approaches, 4.4% (17 of 383) of outlier loci were cross‐validated by four F_ST‐based methods, and 8.9% (34 of 383) were cross‐validated by at least three methods. Of the 34 outliers, 15 were found to be strongly associated with one or more environmental variables. Further support for selection, provided by functional genomic information, was particularly compelling for SNP outliers mapped to different genes putatively involved in the same function such as vision, xenobiotic detoxification and innate immune response. This study enabled a more rigorous consideration of selection as the underlying cause of diversity patterns in Iberian honey bees, representing an important first step towards the identification of polymorphisms implicated in local adaptation and possibly in response to recent human‐mediated environmental changes.     

Evaluation of 16 loci to examine the cross‐species utility of single nucleotide polymorphism arrays

Large collections of single nucleotide polymorphisms (SNPs) have recently been identified from a number of livestock genomes. This raises the possibility that SNP arrays might be useful for analysis in related species for which few genetic markers are currently available. To address the likely success of such an approach, the aim of this study was to examine the threshold number and position of flanking mutations which act to prevent genotype calls being produced. Sequence diversity was measured across 16 loci containing SNPs known either to work successfully between species or fail between species. In pairwise comparisons between domestic and wild sheep, sequence divergence surrounding working SNP assays was significantly lower than that surrounding non‐functional assays. In addition, the location of flanking mismatches tended to be closer to the target SNP in loci that failed to generate genotype calls across species. The magnitude of sequence divergence observed for both working and non‐functional assays was compared with the divergence separating domestic sheep from European Mouflon, African Barbary, goat and cattle. The results suggest that the utility of SNP arrays for analysis of shared polymorphism will be restricted to closely related pairs of species. Analysis across more divergent species will, however, be successful for other objectives, such as the identification of the ancestral state of SNPs.     

Development of a 44K SNP assay focussing on the analysis of a varroa-specific defence behaviour in honey bees (Apis mellifera carnica)

Honey bees are exposed to a number of damaging pathogens and parasites. The most destructive among them, affecting mainly the brood, is Varroa destructor. A promising approach to prevent its spread is to breed for Varroa-tolerant honey bees. A trait that has been shown to provide significant resistance against the Varroa mite is hygienic behaviour, a behavioural response of honey bee workers to brood diseases in general. This study reports the development of a 44K SNP assay, specifically designed for the analysis of hygienic behaviour of individual worker bees (Apis mellifera carnica) directed against V. destructor. Initially, 70,000 SNPs chosen from a large set of SNPs published by the Honey Bee Genome Project were validated for their suitability in the analysis of the Varroa resistance trait 'uncapping of Varroa-infested brood'. This was achieved by genotyping of pooled DNA samples of trait bearers and two trait-negative controls using next-generation sequencing. Approximately 36,000 of these validated SNPs and another 8000 SNPs not validated in this study were selected for the construction of a SNP assay. This assay will be employed in following experiments to analyse individualized DNA samples in order to identify quantitative trait loci (QTL) involved in the control of the investigated trait and to evaluate and possibly confirm QTL found in other studies. However, this assay is not just suitable to study Varroa tolerance, it is as well applicable to analyse any other trait in honey bees. In addition, because of its high density, this assay provides access into genomic selection with respect to several traits considered in honey bee breeding. It will become publicly available via AROS Applied Biotechnology AS, Aarhus, Denmark, before the end of the year 2011.     

A simple method for genomic selection of moderately sized dairy cattle populations

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a 'cow model,' estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.     

Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program

Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.     

snpqc – an R pipeline for quality control of Illumina SNP genotyping array data

In genome‐wide association studies, quality control (QC) of genotypes is important to avoid spurious results. It is also important to maintain long‐term data integrity, particularly in settings with ongoing genotyping (e.g. estimation of genomic breeding values). Here we discuss snpqc , a fully automated pipeline to perform QC analyses of Illumina SNP array data. It applies a wide range of common quality metrics with user‐defined filtering thresholds to generate a comprehensive QC report and a filtered dataset, including a genomic relationship matrix, ready for further downstream analyses which make it amenable for integration in high‐throughput environments. snpqc also builds a database to store genotypic, phenotypic and quality metrics to ensure data integrity and the option of integrating more samples from subsequent runs. The program is generic across species and array designs, providing a convenient interface between the genotyping laboratory and downstream genome‐wide association study or genomic prediction.     

Identification of copy number variations in three Chinese horse breeds using 70K single nucleotide polymorphism BeadChip array

Copy number variation (CNV), an essential form of genetic variation, has been increasingly recognized as one promising genetic marker in the analysis of animal genomes. Here, we used the Equine 70K single nucleotide polymorphism genotyping array for the genome‐wide detection of CNVs in 96 horses from three diverse Chinese breeds: Debao pony (DB), Mongolian horse (MG) and Yili horse (YL). A total of 287 CNVs were determined and merged into 122 CNV regions (CNVRs) ranging from 199 bp to 2344 kb in size and distributed in a heterogeneous manner on chromosomes. These CNVRs were integrated with seven existing reports to generate a composite genome‐wide dataset of 1558 equine CNVRs, revealing 69 (56.6%) novel CNVRs. The majority (69.7%) of the 122 CNVRs overlapped with 438 genes, whereas 30.3% were located in intergenic regions. Most of these genes were associated with common CNVRs, which were shared by divergent horse breeds. As many as 60, 42 and 91 genes overlapping with the breed‐specific ss were identified in DB, MG and YL respectively. Among these genes, FGF11, SPEM1, PPARG, CIDEB, HIVEP1 and GALR may have potential relevance to breed‐specific traits. These findings provide valuable information for understanding the equine genome and facilitating association studies of economically important traits with equine CNVRs in the future.     

Characterization of polyploid wheat genomic diversity using a high‐density 90 000 single nucleotide polymorphism array

High‐density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene‐associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome‐wide distributed SNPs that are represented in populations of diverse geographical origin. We used density‐based spatial clustering algorithms to enable high‐throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model‐free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low‐intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.     

Efficiency of selection, as measured by single nucleotide polymorphism variation, is dependent on inbreeding rate in Drosophila melanogaster

It is often hypothesized that slow inbreeding causes less inbreeding depression than fast inbreeding at the same absolute level of inbreeding. Possible explanations for this phenomenon include the more efficient purging of deleterious alleles and more efficient selection for heterozygote individuals during slow, when compared with fast, inbreeding. We studied the impact of inbreeding rate on the loss of heterozygosity and on morphological traits in Drosophila melanogaster. We analysed five noninbred control lines, 10 fast inbred lines and 10 slow inbred lines; the inbred lines all had an expected inbreeding coefficient of approximately 0.25. Forty single nucleotide polymorphisms in DNA coding regions were genotyped, and we measured the size and shape of wings and counted the number of sternopleural bristles on the genotyped individuals. We found a significantly higher level of genetic variation in the slow inbred lines than in the fast inbred lines. This higher genetic variation was resulting from a large contribution from a few loci and a smaller effect from several loci. We attributed the increased heterozygosity in the slow inbred lines to the favouring of heterozygous individuals over homozygous individuals by natural selection, either by associative over‐dominance or balancing selection, or a combination of both. Furthermore, we found a significant polynomial correlation between genetic variance and wing size and shape in the fast inbred lines. This was caused by a greater number of homozygous individuals among the fast inbred lines with small, narrow wings, which indicated inbreeding depression. Our results demonstrated that the same amount of inbreeding can have different effects on genetic variance depending on the inbreeding rate, with slow inbreeding leading to higher genetic variance than fast inbreeding. These results increase our understanding of the genetic basis of the common observation that slow inbred lines express less inbreeding depression than fast inbred lines. In addition, this has more general implications for the importance of selection in maintaining genetic variation.     

A single nucleotide polymorphism assay for the identification of unisexual Ambystoma salamanders

Unisexual (all female) salamanders in the genus Ambystoma are animals of variable ploidy (2N‐5N) that reproduce via a unique system of ‘leaky’ gynogenesis. As a result, these salamanders have a diverse array of nuclear genome combinations from up to five sexual species: the blue‐spotted (A. laterale), Jefferson (A. jeffersonianum), smallmouth (A. texanum), tiger (A. tigrinum) and streamside (A. barbouri) salamanders. Identifying the genome complement, or biotype, is a critical first step in addressing a broad range of ecological and evolutionary questions about these salamanders. Previous work relied upon genome‐related differences in allele size distributions for specific microsatellite loci, but overlap in these distributions among different genomes makes definitive identification and ploidy determination in unisexuals difficult or impossible. Here, we develop the first single nucleotide polymorphism assay for the identification of unisexual biotypes, based on species‐specific nucleotide polymorphisms in noncoding DNA loci. Tests with simulated and natural unisexual DNA samples show that this method can accurately identify genome complement and estimate ploidy, making this a valuable tool for assessing the genome composition of unisexual samples.     

SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.     

The genomic basis of adaptation to high‐altitude habitats in the eastern honey bee (Apis cerana)

The eastern honey bee (Apis cerana) is of central importance for agriculture in Asia. It has adapted to a wide variety of environmental conditions across its native range in southern and eastern Asia, which includes high‐altitude regions. eastern honey bees inhabiting mountains differ morphologically from neighbouring lowland populations and may also exhibit differences in physiology and behaviour. We compared the genomes of 60 eastern honey bees collected from high and low altitudes in Yunnan and Gansu provinces, China, to infer their evolutionary history and to identify candidate genes that may underlie adaptation to high altitude. Using a combination of F_ST‐based statistics, long‐range haplotype tests and population branch statistics, we identified several regions of the genome that appear to have been under positive selection. These candidate regions were strongly enriched for coding sequences and had high haplotype homozygosity and increased divergence specifically in highland bee populations, suggesting they have been subjected to recent selection in high‐altitude habitats. Candidate loci in these genomic regions included genes related to reproduction and feeding behaviour in honey bees. Functional investigation of these candidate loci is necessary to fully understand the mechanisms of adaptation to high‐altitude habitats in the eastern honey bee.     

Development of an Arabis alpina genomic contig sequence data set and application to single nucleotide polymorphisms discovery

The alpine plant Arabis alpina is an emerging model in the ecological genomic field which is well suited to identifying the genes involved in local adaptation in contrasted environmental conditions, a subject which remains poorly understood at molecular level. This study presents the assembly of a pool of A. alpina genomic fragments using next‐generation sequencing technologies. These contigs cover 172 Mb of the A. alpina genome (i.e. 50% of the genome) and were shown to contain sequences giving positive hits against 96% of the 458 CEGMA core genes (Core Eukaryotic Genes Mapping Approach), a set of highly conserved eukaryotic genes. Regions presenting high nucleic sequence identity with 77% of the close relative Arabidopsis thaliana's genes were found with an unbiased distribution across the different functional categories of A. thaliana genes. This new resource was tested using a resequencing assay to identify polymorphic sites. Sixteen samples were successfully analysed and 127 041 single‐nucleotide polymorphisms identified. This contig data set will contribute to improving our understanding of the ecology of Arabis alpina, thus constituting an important resource for future ecological genomic studies.     

Linked selection,ancient polymorphism,and ecological adaptation shape the genomic landscape of divergence in Quercus dentata

Multiple evolutionary forces contribute to heterogeneous genomic landscapes; however, disentangling their relative contributions is challenging. We sampled nine populations across the distribution of Quercus dentata, a dominant forest tree in East Asia, and used whole-genome sequencing data to investigate mechanisms underlying divergence. We identified two genetic groups (north and south) that diverged ~1.84 million years ago, consistent with the uplift of the Qinling Mountains during the Pleistocene. The north group experienced a bottleneck during the middle–late Pleistocene and expanded from multiple refugia. The south group experienced a more severe bottleneck and showed high population differentiation, probably due to long-term isolation and habitat fragmentation. We detected genomic islands with elevated relative differentiation (F_ST) scattered across the genome. Among these, 65.9% showed reduced absolute divergence (d_XY) consistent with linked selection, while the remaining (34.1%) showed elevated d_XY suggestive of divergent sorting of ancient polymorphisms. The recombination rate in genomic islands was lower than background, suggesting the importance of genome structure in shaping the genomic landscape. We detected 108 single nucleotide polymorphisms significantly associated with environmental factors, 12 of which clustered in a region of ~500 kb. This region showed multiple signals of positive selection in the north group, including the enrichment of XP-extended haplotype homozygosity scores, an elevated population branch statistic, and an excess of high-frequency derived alleles. In addition, we found that linkage disequilibrium was low and derived haplotypes declined rapidly in this region, indicating selection on standing variation. Our results clarify the evolutionary processes driving genomic divergence in Q. dentata.     

Relationship between single nucleotide polymorphism of interleukin-18 and susceptibility to pulmonary tuberculosis in the Chinese Han population

Interleukin-18 (IL-18) is a multi-functional cytokine capable of inducing either Th1 or Th2 polarization depending on the immunologic milieu. IL-18 may influence the host response to Mycobacterium tuberculosis (M.tb) infection. To investigate the relationship between single nucleotide polymorphisms of the IL-18 and susceptibility to pulmonary tuberculosis in the Chinese Han population, the IL-18 gene was sequenced to detect polymorphisms and to examine the genotype frequencies in 300 patients and 702 healthy controls. DNA sequencing revealed three IL-18 variants: rs1946518, rs5744247, and rs549908. It also revealed that allele A of rs1946518 confers a 1.47-fold increased risk of developing tuberculosis (TB) (P = 0.0001, OR [95%CI] = 1.47 [1.21-1.78]), and that the C allele of rs5744247 confers a 0.77-fold decreased risk of disease (P = 0.01, R [95%CI] = 0.77 [0.632-0.937]). The genotypes rs1946518, rs5744247 and rs549908 were found to be significantly associated with TB. Estimation of the frequencies of haplotypes revealed a potential risk haplotype AGA (P = 0.01, OR [95%CI] = 1.41 [1.15-1.72]) and a protective haplotype CCA (P = 0.01, OR [95%CI] = 0.70 [0.57-0.85]) for TB. The present findings suggest that polymorphisms in the IL-18 gene may affect susceptibility to TB and increase the risk of developing the disease in the Chinese Han population.     

Phylogeography and adaptation genetics of stickleback from the Haida Gwaii archipelago revealed using genome‐wide single nucleotide polymorphism genotyping

Threespine stickleback populations are model systems for studying adaptive evolution and the underlying genetics. In lakes on the Haida Gwaii archipelago (off western Canada), stickleback have undergone a remarkable local radiation and show phenotypic diversity matching that seen throughout the species distribution. To provide a historical context for this radiation, we surveyed genetic variation at >1000 single nucleotide polymorphism (SNP) loci in stickleback from over 100 populations. SNPs included markers evenly distributed throughout genome and candidate SNPs tagging adaptive genomic regions. Based on evenly distributed SNPs, the phylogeographic pattern differs substantially from the disjunct pattern previously observed between two highly divergent mtDNA lineages. The SNP tree instead shows extensive within watershed population clustering and different watersheds separated by short branches deep in the tree. These data are consistent with separate colonizations of most watersheds, despite underlying genetic connections between some independent drainages. This supports previous suppositions that morphological diversity observed between watersheds has been shaped independently, with populations exhibiting complete loss of lateral plates and giant size each occurring in several distinct clades. Throughout the archipelago, we see repeated selection of SNPs tagging candidate freshwater adaptive variants at several genomic regions differentiated between marine–freshwater populations on a global scale (e.g. EDA, Na/K ATPase). In estuarine sites, both marine and freshwater allelic variants were commonly detected. We also found typically marine alleles present in a few freshwater lakes, especially those with completely plated morphology. These results provide a general model for postglacial colonization of freshwater habitat by sticklebacks and illustrate the tremendous potential of genome‐wide SNP data sets hold for resolving patterns and processes underlying recent adaptive divergences.     

Aliphatic alcohols and aldehydes of the honey bee cocoon induce arrestment behavior in Varroa jacobsoni (Acari: Mesostigmata), an ectoparasite of Apis mellifera

The ectoparasitic mite Varroa jacobsoni reproduces in the capped brood of the honey bees Apis cerana and Apis mellifera. Observations on the reproductive behavior of the mite have shown a well-structured spatial allocation of its activity using the bee or cell wall for different behaviors. The resulting advantages for the parasite of this subdivision of the concealed brood environment suggests an important role for chemostimuli in these substrates. Extracts of the European honey bee cocoons induce a strong arrestment response in the mite, as indicated by prolonged periods of walking on the extracts applied on a semipermeable membrane and by systematically returning to the stimulus after encountering the treatment borders. Two thin-layer chromatography fractions of the cocoon extract eliciting arrestment were found to contain saturated C₁₇ to C₂₂ primary aliphatic alcohols and C₁₉ to C₂₂ aldehydes. We analyzed extracts of the cocoon and different larvae, pupae, and adults of both worker and drone A. mellifera to determine the relative amounts of these chemostimuli in the different substrates employed by Varroa. Both aldehydes and alcohols were more abundant in the cocoon than in the cuticle of adult or developing bees. Mixtures of the aliphatic alcohols and aldehydes at the proportions found in the cocoons acted synergistically on the arrestment response, but this activity disappeared when mixed in equal amounts. When these oxygenated chemostimuli were mixed with C₁₉ to C₂₅ alkanes at the proportions found in the cocoon extract, we observed a significantly lower threshold for the chemostimulant mixture. These results indicate how Varroa may use mixtures of rarer products to differentiate between substrates and host stages during its developmental cycle within honey bee brood cells. Arch. Insect Biochem. Physiol. 37:129–145, 1998. © 1998 Wiley-Liss, Inc.