Estimation of population allele frequencies from next-generation sequencing data: pool-versus individual-based genotyping

Insecticide resistance reduces the profitability of insect-resistant rice cultivars

INTRODUCTION

Preventing crop yield loss caused by pests is critical for global agricultural production. Agricultural pest control has largely relied on chemical pesticides. The interaction between insecticide resistance and the adaptation of herbivorous pests to host plants may represent an emerging threat to future food security.

OBJECTIVES

This study aims to unveil genetic evidence for the reduction in the profitability of resistant cultivars derived from insecticide resistance in target pest insects.

METHODS

An experimental evolution system encompassing resistant rice and its major monophagous pest, the brown planthopper Nilaparvata lugens, was constructed. Whole genome resequencing and selective sweep analysis were utilized to identify the candidate gene loci related to the adaptation. RNA interference and induced expression assay were conducted to validate the function of the candidate loci.

RESULTS

We found that the imidacloprid-resistant population of N. lugens rapidly adapted to resistant rice IR36. Gene loci related to imidacloprid resistance may contribute to this phenomenon. Multiple alleles in the nicotinic acetylcholine receptor (nAChR)-7-like and P450 CYP4C61 were significantly correlated with changes in virulence to IR36 rice and insecticide resistance of N. lugens. One avirulent/susceptible genotype and two virulent/resistant genotypes could be inferred from the corresponding alleles. Importantly, we found that the virulent/resistant genotypes already exist in the wild in China, exhibiting increasing frequencies along with insecticide usage. We validated the relevance of these genotypes and the virulence to three more resistant rice cultivars. Knockdown of the above two genes in N. lugens significantly decreased both the resistance to imidacloprid and the virulence towards resistant rice.

CONCLUSION

Our findings provide direct genetic evidence to the eco-evolutionary consequence of insecticide resistance, and suggest an urgent need for the implementation of predictably sustainable pest management.

How Can Genomics Help or Hinder Wildlife Conservation?

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

Genomic Diversity Illuminates the Environmental Adaptation of Drosophila suzukii

Biological invasions carry substantial practical and scientific importance, and represent natural evolutionary experiments on contemporary timescales. Here, we investigated genomic diversity and environmental adaptation of the crop pest Drosophila suzukii using whole-genome sequencing data and environmental metadata for 29 population samples from its native and invasive range. Through a multifaceted analysis of this population genomic data, we increase our understanding of the D. suzukii genome, its diversity and its evolution, and we identify an appropriate genotype-environment association pipeline for our data set. Using this approach, we detect genetic signals of local adaptation associated with nine distinct environmental factors related to altitude, wind speed, precipitation, temperature, and human land use. We uncover unique functional signatures for each environmental variable, such as a prevalence of cuticular genes associated with annual precipitation. We also infer biological commonalities in the adaptation to diverse selective pressures, particularly in terms of the apparent contribution of nervous system evolution to enriched processes (ranging from neuron development to circadian behavior) and to top genes associated with all nine environmental variables. Our findings therefore depict a finer-scale adaptive landscape underlying the rapid invasion success of this agronomically important species.

Population Genomics of Pooled Samples: Unveiling Symbiont Infrapopulation Diversity and Host-Symbiont Coevolution

Microscopic symbionts represent crucial links in biological communities. However, they present technical challenges in high-throughput sequencing (HTS) studies due to their small size and minimal high-quality DNA yields, hindering our understanding of host-symbiont coevolution at microevolutionary and macroevolutionary scales. One approach to overcome those barriers is to pool multiple individuals from the same infrapopulation (i.e., individual host) and sequence them together (Pool-Seq), but individual-level information is then compromised. To simultaneously address both issues (i.e., minimal DNA yields and loss of individual-level information), we implemented a strategic Pool-Seq approach to assess variation in sequencing performance and categorize genetic diversity (single nucleotide polymorphisms (SNPs)) at both the individual-level and infrapopulation-level for microscopic feather mites. To do so, we collected feathers harboring mites (Proctophyllodidae: Amerodectes protonotaria) from four individual Prothonotary Warblers (Parulidae: Protonotaria citrea). From each of the four hosts (i.e., four mite infrapopulations), we conducted whole-genome sequencing on three extraction pools consisting of different numbers of mites (1 mite, 5 mites, and 20 mites). We found that samples containing pools of multiple mites had more sequencing reads map to the feather mite reference genome than did the samples containing only a single mite. Mite infrapopulations were primarily genetically structured by their associated individual hosts (not pool size) and the majority of SNPs were shared by all pools within an infrapopulation. Together, these results suggest that the patterns observed are driven by evolutionary processes occurring at the infrapopulation level and are not technical signals due to pool size. In total, despite the challenges presented by microscopic symbionts in HTS studies, this work highlights the value of both individual-level and infrapopulation-level sequencing toward our understanding of host-symbiont coevolution at multiple evolutionary scales.

Integrating Pool-seq uncertainties into demographic inference

Next-generation sequencing of pooled samples (Pool-seq) is a popular method to assess genome-wide diversity patterns in natural and experimental populations. However, Pool-seq is associated with specific sources of noise, such as unequal individual contributions. Consequently, using Pool-seq for the reconstruction of evolutionary history has remained underexplored. Here we describe a novel Approximate Bayesian Computation (ABC) method to infer demographic history, explicitly modelling Pool-seq sources of error. By jointly modelling Pool-seq data, demographic history and the effects of selection due to barrier loci, we obtain estimates of demographic history parameters accounting for technical errors associated with Pool-seq. Our ABC approach is computationally efficient as it relies on simulating subsets of loci (rather than the whole-genome) and on using relative summary statistics and relative model parameters. Our simulation study results indicate Pool-seq data allows distinction between general scenarios of ecotype formation (single versus parallel origin) and to infer relevant demographic parameters (e.g. effective sizes and split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod Littorina saxatilis, sampled on a narrow geographical scale at two Swedish locations where two ecotypes (Wave and Crab) are found. Our model choice and parameter estimates show that ecotypes formed before colonization of the two locations (i.e. single origin) and are maintained despite gene flow. These results indicate that demographic modelling and inference can be successful based on pool-sequencing using ABC, contributing to the development of suitable null models that allow for a better understanding of the genetic basis of divergent adaptation.

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.

When One's Not Enough: Colony Pool-Seq Outperforms Individual-Based Methods for Assessing Introgression in Apis mellifera mellifera

The human management of honey bees (Apis mellifera) has resulted in the widespread introduction of subspecies outside of their native ranges. One well known example of this is Apis mellifera mellifera, native to Northern Europe, which has now been significantly introgressed by the introduction of C lineage honey bees. Introgression has consequences for species in terms of future adaptive potential and long-term viability. However, estimating introgression in colony-living haplodiploid species is challenging. Previous studies have estimated introgression using individual workers, individual drones, multiple drones, and pooled workers. Here, we compare introgression estimates via three genetic approaches: SNP array, individual RAD-seq, and pooled colony RAD-seq. We also compare two statistical approaches: a maximum likelihood cluster program (ADMIXTURE) and an incomplete lineage sorting model (ABBA BABA). Overall, individual approaches resulted in lower introgression estimates than pooled colonies when using ADMIXTURE. However, the pooled colony ABBA BABA approach resulted in generally lower introgression estimates than all three ADMIXTURE estimates. These results highlight that sometimes one individual is not enough to assess colony-level introgression, and future studies that do use colony pools should not be solely dependent on clustering programs for introgression estimates.

Genetic diversity and thermotolerance in Holstein cows: Pathway analysis and marker development using whole-genome sequencing

Heat stress causes extensive losses in the dairy sector, due to negative effects on milk production and reproduction. Cows have evolved a series of protective mechanisms, (physiological, biochemical, behavioural) to cope with the thermostressing environments, which have allowed the preservation of productive and reproductive potential of specific animals during summer; these animals are considered thermotolerant and could be used to design programs of selective breeding. These programs, targeting the generations of a population of heat-resistant animals, would increase the frequency of the desired phenotypes, tackling the financial losses on one hand and reducing the carbon footprints of the dairy sector on the other. The development of genomics techniques has enabled genome wide variant calling, to detect SNPs associated with the desired phenotypes. In this study, we used a comparative genomics approach to detect genetic variation associated with thermotolerance and to design molecular markers for characterizing the animals as tolerant/sensitive. A total of 40 cows from each group were split in four sequencing pools and a whole-genome sequencing approach was used. Results and conclusion: Genome-wide genetic variation between groups was characterized and enrichment analysis revealed specific pathways which participate in the adaptive mechanisms of thermotolerance, implicated into systemic and cellular responses, including the immune system functionality, Heat Stress and Unfolded Protein Response. The markers made a promising set of results, as specific SNPs in five genes encoding for Heat Shock Proteins were significantly associated with thermotolerance.

A need for standardized reporting of introgression: Insights from studies across eukaryotes

With the rise of affordable next-generation sequencing technology, introgression-or the exchange of genetic materials between taxa-has become widely perceived to be a ubiquitous phenomenon in nature. Although this claim is supported by several keystone studies, no thorough assessment of the frequency of introgression across eukaryotes in nature has been performed to date. In this manuscript, we aim to address this knowledge gap by examining patterns of introgression across eukaryotes. We collated a single statistic, Patterson's D, which can be used as a test for introgression across 123 studies to further assess how taxonomic group, divergence time, and sequencing technology influence reports of introgression. Overall, introgression has mostly been measured in plants and vertebrates, with less attention given to the rest of the Eukaryotes. We find that the most frequently used metrics to detect introgression are difficult to compare across studies and even more so across biological systems due to differences in study effort, reporting standards, and methodology. Nonetheless, our analyses reveal several intriguing patterns, including the observation that differences in sequencing technologies may bias values of Patterson's D and that introgression may differ throughout the course of the speciation process. Together, these results suggest the need for a unified approach to quantifying introgression in natural communities and highlight important areas of future research that can be better assessed once this unified approach is met.

Use of DNA pools of a reference population for genomic selection of a binary trait in Atlantic salmon

Genomic selection has a great potential in aquaculture breeding since many important traits are not directly measured on the candidates themselves. However, its implementation has been hindered by staggering genotyping costs because of many individual genotypes. In this study, we explored the potential of DNA pooling for creating a reference population as a tool for genomic selection of a binary trait. Two datasets from the SalmoBreed population challenged with salmonid alphavirus, which causes pancreas disease, were used. Dataset-1, that includes 855 individuals (478 survivors and 377 dead), was used to develop four DNA pool samples (i.e., 2 pools each for dead and survival). Dataset-2 includes 914 individuals (435 survivors and 479 dead) belonging to 65 full-sibling families and was used to develop in-silico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. A similar trend was also observed for the correlation between GEBVs, which increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40× and 20×, respectively, compared to an accuracy of 0.73 for the SNP chip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction using individual genotypes was 0.712. A limited effect of sequencing depth on the correlation of GEBVs and prediction accuracy was observed. Results showed that a large number of pools are required to achieve as good prediction as individual genotypes; however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection.

Identification of loci controlling timing of stem elongation in red clover using genotyping by sequencing of pooled phenotypic extremes

MAIN CONCLUSION

Through selective genotyping of pooled phenotypic extremes, we identified a number of loci and candidate genes putatively controlling timing of stem elongation in red clover. We have identified candidate genes controlling the timing of stem elongation prior to flowering in red clover (Trifolium pratense L.). This trait is of ecological and agronomic significance, as it affects fitness, competitivity, climate adaptation, forage and seed yield, and forage quality. We genotyped replicate pools of phenotypically extreme individuals (early and late-elongating) within cultivar Lea using genotyping-by-sequencing in pools (pool-GBS). After calling and filtering SNPs and GBS locus haplotype polymorphisms, we estimated allele frequencies and searched for markers with significantly different allele frequencies in the two phenotypic groups using BayeScan, an F_ST-based test utilizing replicate pools, and a test based on error variance of replicate pools. Of the three methods, BayeScan was the least stringent, and the error variance-based test the most stringent. Fifteen significant markers were identified in common by all three tests. The candidate genes flanking the markers include genes with potential roles in the vernalization, autonomous, and photoperiod regulation of floral transition, hormonal regulation of stem elongation, and cell growth. These results provide a first insight into the potential genes and mechanisms controlling transition to stem elongation in a perennial legume, which lays a foundation for further functional studies of the genetic determinants regulating this important trait.

The ectodysplasin-A receptor is a candidate gene for lateral plate number variation in stickleback fish

Variation in lateral plating in stickleback fish represents a classical example of rapid and parallel adaptation in morphology. The underlying genetic architecture involves polymorphism at the ectodysplasin-A gene (EDA). However, lateral plate number is influenced by additional loci that remain poorly characterized. Here, we search for such loci by performing genome-wide differentiation mapping based on pooled whole-genome sequence data from a European stickleback population variable in the extent of lateral plating, while tightly controlling for the phenotypic effect of EDA. This suggests a new candidate locus, the EDA receptor gene (EDAR), for which additional support is obtained by individual-level targeted Sanger sequencing and by comparing allele frequencies among natural populations. Overall, our study illustrates the power of pooled whole-genome sequencing for searching phenotypically relevant loci and opens opportunities for exploring the population genetics and ecological significance of a new candidate locus for stickleback armor evolution.

High-throughput estimation of allele frequencies using combined pooled-population sequencing and haplotype-based data processing

BACKGROUND

In addition to heterogeneity and artificial selection, natural selection is one of the forces used to combat climate change and improve agrobiodiversity in evolutionary plant breeding. Accurate identification of the specific genomic effects of natural selection will likely accelerate transfer between populations. Thus, insights into changes in allele frequency, adequate population size, gene flow and drift are essential. However, observing such effects often involves a trade-off between costs and resolution when a large sample of genotypes for many loci is analysed. Pool genotyping approaches achieve high resolution and precision in estimating allele frequency when sequence coverage is high. Nevertheless, high-coverage pool sequencing of large genomes is expensive.

RESULTS

Three pool samples (n = 300, 300, 288) from a barley backcross population were generated to assess the population's allele frequency. The tested population (BC2F21) has undergone 18 generations of natural adaption to conventional farming practice. The accuracies of estimated pool-based allele frequencies and genome coverage yields were compared using three next-generation sequencing genotyping methods. To achieve accurate allele frequency estimates with low sequence coverage, we employed a haplotyping approach. Low coverage allele frequencies of closely located single polymorphisms were aggregated into a single haplotype allele frequency, yielding 2-to-271-times higher depth and increased precision. When we combined different haplotyping tactics, we found that gene and chip marker-based haplotype analyses performed equivalently or better compared with simple contig haplotype windows. Comparing multiple pool samples and referencing against an individual sequencing approach revealed that whole-genome pool re-sequencing (WGS) achieved the highest correlation with individual genotyping (≥ 0.97). In contrast, transcriptome-based genotyping (MACE) and genotyping by sequencing (GBS) pool replicates were significantly associated with higher error rates and lower correlations, but are still valuable to detect large allele frequency variations.

CONCLUSIONS

The proposed strategy identified the allele frequency of populations with high accuracy at low cost. This is particularly relevant to evolutionary plant breeding of crops with very large genomes, such as barley. Whole-genome low coverage re-sequencing at 0.03 × coverage per genotype accurately estimated the allele frequency when a loci-based haplotyping approach was applied. The implementation of annotated haplotypes capitalises on the biological background and statistical robustness.

Population Structure and Diversity in European Honey Bees (Apis mellifera L.)—An Empirical Comparison of Pool and Individual Whole-Genome Sequencing

Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.

Metabarcoding reveals massive species diversity of Diptera in a subtropical ecosystem

Diptera is often considered to be the richest insect group due to its great species diversity and broad ecological versatility. However, data on dipteran diversity from subtropical ecosystems have hitherto been scarce, due to the lack of studies conducted at an appropriate large scale. We investigated the diversity and composition of Diptera communities on Tianmu Mountain, Zhejiang, China, using DNA metabarcoding technology, and evaluated their dynamic responses to the effects of slope aspect, season, and altitudinal zone. A total of 5,092 operational taxonomic units (OTUs) were discovered and tentatively assigned to 72 dipteran families, including 2 family records new for China and 30 family records new for the locality. Cecidomyiidae, Sciaridae, and Phoridae were the predominant families, representing 53.6% of total OTUs, while 52 families include >95% unidentified and presumed undescribed species. We found that the community structure of Diptera was significantly affected by aspect, seasonality (month) and elevation, with richer diversity harbored in north-facing than south-facing slopes, and seasonality a more profound driver of community structure and diversity than elevation. Overall, massive species diversity of Diptera communities was discovered in this subtropical ecosystem of east China. The huge diversity of potentially undescribed species only revealed by metabarcoding now requires more detailed taxonomic study, as a step toward an evolutionary integration that accumulates information on species' geographic ranges, ecological traits, functional roles, and species interactions, and thus places the local communities in the context of the growing knowledge base of global biodiversity and its response to environmental change.

Whole Exome-Sequencing of Pooled Genomic DNA Samples to Detect Quantitative Trait Loci in Esotropia and Exotropia of Strabismus in Japanese

BACKGROUND

Esotropia and exotropia are two major phenotypes of comitant strabismus. It remains controversial whether esotropia and exotropia would share common genetic backgrounds. In this study, we used a quantitative trait locus (QTL)-sequencing pipeline for diploid plants to screen for susceptibility loci of strabismus in whole exome sequencing of pooled genomic DNAs of individuals.

METHODS

Pooled genomic DNA (2.5 ng each) of 20 individuals in three groups, Japanese patients with esotropia and exotropia, and normal members in the families, was sequenced twice after exome capture, and the first and second sets of data in each group were combined to increase the read depth. The SNP index, as the ratio of variant genotype reads to all reads, and Δ(SNP index) values, as the difference of SNP index between two groups, were calculated by sliding window analysis with a 4 Mb window size and 10 kb slide size. The rows of 200 "N"s were inserted as a putative 200-b spacer between every adjoining locus to depict Δ(SNP index) plots on each chromosome. SNP positions with depth < 20 as well as SNP positions with SNP index of <0.3 were excluded.

RESULTS

After the exclusion of SNPs, 12,242 SNPs in esotropia/normal group and 12,108 SNPs in exotropia/normal group remained. The patterns of the Δ(SNP index) plots on each chromosome appeared different between esotropia/normal group and exotropia/normal group. When the consecutive groups of SNPs on each chromosome were set at three patterns: SNPs in each cytogenetic band, 50 consecutive sliding SNPs, and SNPs in 4 Mb window size with 10 kb slide size, p values (Wilcoxon signed rank test) and Q values (false discovery rate) in a few loci as Manhattan plots showed significant differences in comparison between the Δ(SNP index) in the esotropia/normal group and exotropia/normal group.

CONCLUSIONS

The pooled DNA sequencing and QTL mapping approach for plants could provide overview of genetic background on each chromosome and would suggest different genetic backgrounds for two major phenotypes of comitant strabismus, esotropia and exotropia.

Drosophila Evolution over Space and Time (DEST): A New Population Genomics Resource

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome data sets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate data sets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This data set, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental metadata. A web-based genome browser and web portal provide easy access to the SNP data set. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan data set. Our resource will enable population geneticists to analyze spatiotemporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

Does a complex life cycle affect adaptation to environmental change? Genome-informed insights for characterizing selection across complex life cycle

Complex life cycles, in which discrete life stages of the same organism differ in form or function and often occupy different ecological niches, are common in nature. Because stages share the same genome, selective effects on one stage may have cascading consequences through the entire life cycle. Theoretical and empirical studies have not yet generated clear predictions about how life cycle complexity will influence patterns of adaptation in response to rapidly changing environments or tested theoretical predictions for fitness trade-offs (or lack thereof) across life stages. We discuss complex life cycle evolution and outline three hypotheses—ontogenetic decoupling, antagonistic ontogenetic pleiotropy and synergistic ontogenetic pleiotropy—for how selection may operate on organisms with complex life cycles. We suggest a within-generation experimental design that promises significant insight into composite selection across life cycle stages. As part of this design, we conducted simulations to determine the power needed to detect selection across a life cycle using a population genetic framework. This analysis demonstrated that recently published studies reporting within-generation selection were underpowered to detect small allele frequency changes (approx. 0.1). The power analysis indicates challenging but attainable sampling requirements for many systems, though plants and marine invertebrates with high fecundity are excellent systems for exploring how organisms with complex life cycles may adapt to climate change.

ƒ-statistics estimation and admixture graph construction with Pool-Seq or allele count data using the R package poolfstat

By capturing various patterns of the structuring of genetic variation across populations, f -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariance of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F4-ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analyzed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

SNP Development in Penaeus vannamei via Next-Generation Sequencing and DNA Pool Sequencing

Next-generation sequencing and pool sequencing have been widely used in SNP (single-nucleotide polymorphism) detection and population genetics research; however, there are few reports on SNPs related to the growth of Penaeus vannamei. The purpose of this study was to call SNPs from rapid-growing (RG) and slow-growing (SG) individuals’ transcriptomes and use DNA pool sequencing to assess the reliability of SNPs. Two parameters were applied to detect SNPs. One parameter was the p-values generated using Fisher’s exact test, which were used to calculate the significance of allele frequency differences between RG and SG. The other one was the AFI (minor allele frequency imbalance), which was defined to highlight the fold changes in MAF (minor allele frequency) values between RG and SG. There were 216,015 hypothetical SNPs, which were obtained based on the transcriptome data. Finally, 104 high-quality SNPs and 96,819 low-quality SNPs were predicted. Then, 18 high-quality SNPs and 17 low-quality SNPs were selected to assess the reliability of the detection process. Here, 72.22% (13/18) accuracy was achieved for high-quality SNPs, while only 52.94% (9/17) accuracy was achieved for low-quality SNPs. These SNPs enrich the data for population genetics studies of P. vannamei and may play a role in the development of SNP markers for future breeding studies.

Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila

Resolving the role of natural selection is a basic objective of evolutionary biology. It is generally difficult to detect the influence of selection because ubiquitous non-selective stochastic change in allele frequencies (genetic drift) degrades evidence of selection. As a result, selection scans typically only identify genomic regions that have undergone episodes of intense selection. Yet it seems likely such episodes are the exception; the norm is more likely to involve subtle, concurrent selective changes at a large number of loci. We develop a new theoretical approach that uncovers a previously undocumented genome-wide signature of selection in the collective divergence of allele frequencies over time. Applying our approach to temporally resolved allele frequency measurements from laboratory and wild Drosophila populations, we quantify the selective contribution to allele frequency divergence and find that selection has substantial effects on much of the genome. We further quantify the magnitude of the total selection coefficient (a measure of the combined effects of direct and linked selection) at a typical polymorphic locus, and find this to be large (of order 1%) even though most mutations are not directly under selection. We find that selective allele frequency divergence is substantially elevated at intermediate allele frequencies, which we argue is most parsimoniously explained by positive-not negative-selection. Thus, in these populations most mutations are far from evolving neutrally in the short term (tens of generations), including mutations with neutral fitness effects, and the result cannot be explained simply as an ongoing purging of deleterious mutations.

Clinal genomic analysis reveals strong reproductive isolation across a steep habitat transition in stickleback fish

How ecological divergence causes strong reproductive isolation between populations in close geographic contact remains poorly understood at the genomic level. We here study this question in a stickleback fish population pair adapted to contiguous, ecologically different lake and stream habitats. Clinal whole-genome sequence data reveal numerous genome regions (nearly) fixed for alternative alleles over a distance of just a few hundred meters. This strong polygenic adaptive divergence must constitute a genome-wide barrier to gene flow because a steep cline in allele frequencies is observed across the entire genome, and because the cline center closely matches the habitat transition. Simulations confirm that such strong divergence can be maintained by polygenic selection despite high dispersal and small per-locus selection coefficients. Finally, comparing samples from near the habitat transition before and after an unusual ecological perturbation demonstrates the fragility of the balance between gene flow and selection. Overall, our study highlights the efficacy of divergent selection in maintaining reproductive isolation without physical isolation, and the analytical power of studying speciation at a fine eco-geographic and genomic scale.

How ecological divergence causes reproductive isolation between populations in close contact remains poorly understood at the genomic level. This study presents a clinal investigation based on whole-genome sequencing to characterize reproductive isolation between threespine stickleback adapted to contiguous but ecologically different lake and stream habitats.

Characterization of a Sex-Determining Region and Its Genomic Context via Statistical Estimates of Haplotype Frequencies in Daughters and Sons Sequenced in Pools

Sex chromosomes are generally derived from a pair of autosomes that have acquired a locus controlling sex. Sex chromosomes may evolve reduced recombination around this locus and undergo a long process of molecular divergence. At that point, the original loci controlling sex may be difficult to pinpoint. This difficulty has affected many model species from mammals to birds to flies, which present highly diverged sex chromosomes. Identifying sex-controlling loci is easier in species with molecularly similar sex chromosomes. Here we aimed at pinpointing the sex-determining region (SDR) of Armadillidium vulgare, a terrestrial isopod with female heterogamety (ZW females and ZZ males) and whose sex chromosomes appear to show low genetic divergence. To locate the SDR, we assessed single-nucleotide polymorphism (SNP) allele frequencies in F1 daughters and sons sequenced in pools (pool-seq) in several families. We developed a Bayesian method that uses the SNP genotypes of individually sequenced parents and pool-seq data from F1 siblings to estimate the genetic distance between a given genomic region (contig) and the SDR. This allowed us to assign more than 43 Mb of contigs to sex chromosomes, and to demonstrate extensive recombination and very low divergence between these chromosomes. By taking advantage of multiple F1 families, we delineated a very short genomic region (∼65 kb) that presented no evidence of recombination with the SDR. In this short genomic region, the comparison of sequencing depths between sexes highlighted female-specific genes that have undergone recent duplication, and which may be involved in sex determination in A. vulgare.

The effects of genetic drift and genomic selection on differentiation and local adaptation of the introduced populations of Aedes albopictus in southern Russia

Background

Asian tiger mosquito Aedes albopictus is an arbovirus vector that has spread from its native habitation areal in Southeast Asia throughout North and South Americas, Europe, and Africa. Ae. albopictus was first detected in the Southern Federal District of the Russian Federation in the subtropical town of Sochi in 2011. In subsequent years, this species has been described in the continental areas with more severe climate and lower winter temperatures.

Methods

Genomic analysis of pooled Ae. albopictus samples collected in the mosquito populations in the coastal and continental regions of the Krasnodar Krai was conducted to look for the genetic changes associated with the spread and potential cold adaptation in Ae. albopictus.

Results

The results of the phylogenetic analysis based on mitochondrial genomes corresponded well with the hypothesis that Ae. albopictus haplotype A1a2a1 was introduced into the region from a single source. Population analysis revealed the role of dispersal and genetic drift in the local adaptation of the Asian tiger mosquito. The absence of shared haplotypes between the samples and high fixation indices suggest that gene flow between samples was heavily restricted. Mitochondrial and genomic differentiation together with different distances between dispersal routes, natural and anthropogenic barriers and local effective population size reduction could lead to difficulties in local climatic adaptations due to reduced selection effectiveness. We have found genomic regions with selective sweep patterns which can be considered as having been affected by recent selection events. The genes located in these regions participate in neural protection, lipid conservation, and cuticle formation during diapause. These processes were shown to be important for cold adaptation in the previous transcriptomic and proteomic studies. However, the population history and relatively low coverage obtained in the present article could have negatively affect sweep detection.

Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species

Despite their suitability for studying evolution, many conifer species have large and repetitive giga‐genomes (16–31 Gbp) that create hurdles to producing high coverage SNP data sets that capture diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies creates further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost‐saving benefits of pool‐seq and exome‐capture to discover SNPs in two conifer species, Douglas‐fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong, positive correlation with those estimated by sequencing the same population as individuals (r > .948), on par with such comparisons made in model organisms. Further, we highlight the utility of haploid megagametophyte tissue for identifying sites that are probably due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > .973). Our work addresses bioinformatic challenges in non‐model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralogous artefacts, and suggests the combination of pool‐seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

Gut microbiome adaptation to extreme cold winter in wild plateau pika (Ochotona curzoniae) on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau is a harsh environment characterized by low temperature, high altitude and hypoxia, although some native mammals may adapt well to the extreme climate. However, how animal gut microbial community structure and function adapt to extreme cold climates is not well understood. Plateau pika (Ochotona curzoniae) is an ideal animal model with which to study the effects of climate change on host adaptation by studing intestinal microorganisms. Here, we used 16S rRNA sequencing technology combined with physiological methods to investigate plateau pika gut microbiota in summer and winter. Due to limited diet resources, the pikas in winter have a lower ability of degradation and fermentation for plant-based food (reduced cellulase activity and total short-chain fatty acids) by decreasing gut microbial diversity and some functional microbes, such as fiber-degrading bacteria Oscillospira and Treponema. Metagenomic prediction showed that most of those gene functions associated with metabolism (e.g. energy metabolism and lipid metabolism) were less abundant in winter, implying that the plateau pika slows diet fermentation and weakens energy requirements in the cold season. Our results have significance for explaining the mechanism of wild plateau mammals adapting to a high-altitude cold environment from the perspective of gut microbiome.

Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations

Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

Sensory-based quantification of male colour patterns in Trinidadian guppies reveals no support for parallel phenotypic evolution in multivariate trait space

Parallel evolution, in which independent populations evolve along similar phenotypic trajectories, offers insights into the repeatability of adaptive evolution. Here, we revisit a classic example of parallelism, that of repeated evolution of brighter males in the Trinidadian guppy (Poecilia reticulata). In guppies, colonisation of low predation habitats is associated with emergence of 'more colourful' phenotypes since predator-induced viability selection for crypsis weakens while sexual selection by female preference for conspicuousness remains strong. Our study differs from previous investigations in three respects. First, we adopted a multivariate phenotyping approach to characterise parallelism in multitrait space. Second, we used ecologically-relevant colour traits defined by the visual systems of the two selective agents (i.e., guppy, predatory cichlid). Third, we estimated population genetic structure to test for adaptive (parallel) evolution against a model of neutral phenotypic divergence. We find strong phenotypic differentiation that is inconsistent with a neutral model but very limited support for the predicted pattern of greater conspicuousness at low predation. Effects of predation regime on each trait were in the expected direction, but weak, largely nonsignificant, and explained little among-population variation. In multitrait space, phenotypic trajectories of lineages colonising low from high predation regimes were not parallel. Our results are consistent with reduced predation risk facilitating adaptive differentiation, potentially by female choice, but suggest that this proceeds in independent directions of multitrait space across lineages. Pool-sequencing data also revealed SNPs showing greater differentiation than expected under neutrality, among which some are found in genes contributing to colour pattern variation, presenting opportunities for future genetic study.

Pooled Sequencing Analysis of Geese (Anser cygnoides) Reveals Genomic Variations Associated With Feather Color

During the domestication of the goose a change in its feather color took place, however, the molecular mechanisms responsible for this change are not completely understood. Here, we performed whole-genome resequencing on three pooled samples of geese (feral and domestic geese), with two distinct feather colors, to identify genes that might regulate feather color. We identified around 8 million SNPs within each of the three pools and validated allele frequencies for a subset of these SNPs using PCR and Sanger sequencing. Several genomic regions with signatures of differential selection were found when we compared the gray and white feather color populations using the F_ST and Hp approaches. When we combined previous functional studies with our genomic analyses we identified 26 genes (KITLG, MITF, TYRO3, KIT, AP3B1, SMARCA2, ROR2, CSNK1G3, CCDC112, VAMP7, SLC16A2, LOC106047519, RLIM, KIAA2022, ST8SIA4, LOC106044163, TRPM6, TICAM2, LOC106038556, LOC106038575, LOC106038574, LOC106038594, LOC106038573, LOC106038604, LOC106047489, and LOC106047492) that potentially regulate feather color in geese. These results substantially expand the catalog of potential feather color regulators in geese and provide a basis for further studies on domestication and avian feather coloration.

Ecological Load and Balancing Selection in Circumboreal Barnacles

Acorn barnacle adults experience environmental heterogeneity at various spatial scales of their circumboreal habitat, raising the question of how adaptation to high environmental variability is maintained in the face of strong juvenile dispersal and mortality. Here, we show that 4% of genes in the barnacle genome experience balancing selection across the entire range of the species. Many of these genes harbor mutations maintained across 2 My of evolution between the Pacific and Atlantic oceans. These genes are involved in ion regulation, pain reception, and heat tolerance, functions which are essential in highly variable ecosystems. The data also reveal complex population structure within and between basins, driven by the trans-Arctic interchange and the last glaciation. Divergence between Atlantic and Pacific populations is high, foreshadowing the onset of allopatric speciation, and suggesting that balancing selection is strong enough to maintain functional variation for millions of years in the face of complex demography.

From tides to nucleotides: Genomic signatures of adaptation to environmental heterogeneity in barnacles

The northern acorn barnacle (Semibalanus balanoides) is a robust system to study the genetic basis of adaptations to highly heterogeneous environments. Adult barnacles may be exposed to highly dissimilar levels of thermal stress depending on where they settle in the intertidal (i.e., closer to the upper or lower tidal boundary). For instance, barnacles near the upper tidal limit experience episodic summer temperatures above recorded heat coma levels. This differential stress at the microhabitat level is also dependent on the aspect of sun exposure. In the present study, we used pool‐seq approaches to conduct a genome wide screen for loci responding to intertidal zonation across the North Atlantic basin (Maine, Rhode Island, and Norway). Our analysis discovered 382 genomic regions containing SNPs which are consistently zonated (i.e., SNPs whose frequencies vary depending on their position in the rocky intertidal) across all surveyed habitats. Notably, most zonated SNPs are young and private to the North Atlantic. These regions show high levels of genetic differentiation across ecologically extreme microhabitats concomitant with elevated levels of genetic variation and Tajima's D, suggesting the action of non‐neutral processes. Overall, these findings support the hypothesis that spatially heterogeneous selection is a general and repeatable feature for this species, and that natural selection can maintain functional genetic variation in heterogeneous environments.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

Benchmarking the performance of Pool-seq SNP callers using simulated and real sequencing data

Population genomics is a fast-developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome-wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool-seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE-pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large-scale population studies but also in cases where the Pool-seq strategy is the only option, such as in metagenomic or polyploid studies.

Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.

A Whole-Genome Scan for Association with Invasion Success in the Fruit Fly Drosophila suzukii Using Contrasts of Allele Frequencies Corrected for Population Structure

Evidence is accumulating that evolutionary changes are not only common during biological invasions but may also contribute directly to invasion success. The genomic basis of such changes is still largely unexplored. Yet, understanding the genomic response to invasion may help to predict the conditions under which invasiveness can be enhanced or suppressed. Here, we characterized the genome response of the spotted wing drosophila Drosophila suzukii during the worldwide invasion of this pest insect species, by conducting a genome-wide association study to identify genes involved in adaptive processes during invasion. Genomic data from 22 population samples were analyzed to detect genetic variants associated with the status (invasive versus native) of the sampled populations based on a newly developed statistic, we called C2, that contrasts allele frequencies corrected for population structure. We evaluated this new statistical framework using simulated data sets and implemented it in an upgraded version of the program BayPass. We identified a relatively small set of single-nucleotide polymorphisms that show a highly significant association with the invasive status of D. suzukii populations. In particular, two genes, RhoGEF64C and cpo, contained single-nucleotide polymorphisms significantly associated with the invasive status in the two separate main invasion routes of D. suzukii. Our methodological approaches can be applied to any other invasive species, and more generally to any evolutionary model for species characterized by nonequilibrium demographic conditions for which binary covariables of interest can be defined at the population level.

Genetic and phenotypic variation exhibit both predictable and stochastic patterns across an intertidal fish metapopulation

Interactions among selection, gene flow, and drift affect the trajectory of adaptive evolution. In natural populations, the direction and magnitude of these processes can be variable across different spatial, temporal, or ontogenetic scales. Consequently, variability in evolutionary processes affects the predictability or stochasticity of microevolutionary outcomes. We studied an intertidal fish, Bathygobius cocosensis (Bleeker, 1854), to understand how space, time, and life stage structure genetic and phenotypic variation in a species with potentially extensive dispersal and a complex life cycle (larval dispersal preceding benthic recruitment). We sampled juvenile and adult life stages, at three sites, over three years. Genome-wide SNPs uncovered a pattern of chaotic genetic patchiness, that is, weak-but-significant patchy spatial genetic structure that was variable through time and between life stages. Outlier locus analyses suggested that targets of spatially divergent selection were mostly temporally variable, though a significant number of spatial outlier loci were shared between life stages. Head shape, a putatively ecologically responsive (adaptive) phenotype in B. cocosensis also exhibited high temporal variability within sites. However, consistent spatial relationships between sites indicated that environmental similarities among sites may generate predictable phenotype distributions across space. Our study highlights the complex microevolutionary dynamics of marine systems, where consideration of multiple ecological dimensions can reveal both predictable and stochastic patterns in the distributions of genetic and phenotypic variation. Such considerations probably apply to species that possess short, complex life cycles, have large dispersal potential and fecundities, and that inhabit heterogeneous environments.

Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms and field samples

Advances in environmental DNA (eDNA) methodologies have led to improvements in the ability to detect species and communities in aquatic environments, yet the majority of studies emphasize biological diversity at the species level by targeting variable sites within the mitochondrial genome. Here, we demonstrate that eDNA approaches also have the capacity to detect intraspecific diversity in the nuclear genome, allowing for assessments of population-level allele frequencies and estimates of the number of genetic contributors in an eDNA sample. Using a panel of microsatellite loci developed for the round goby (Neogobius melanostomus), we tested the similarity between eDNA-based and individual tissue-based estimates of allele frequencies from experimental mesocosms and in a field-based trial. Subsequently, we used a likelihood-based DNA mixture framework to estimate the number of unique genetic contributors in eDNA samples and in simulated mixtures of alleles. In both mesocosm and field samples, allele frequencies from eDNA were highly correlated with allele frequencies from genotyped round goby tissue samples, indicating nuclear markers can be reliably amplified from water samples. DNA mixture analyses were able to estimate the number of genetic contributors from mesocosm eDNA samples and simulated mixtures of DNA from up to 58 individuals, with the degree of positive or negative bias dependent on the filtering scheme of low-frequency alleles. With this study we document the application of eDNA and multiple amplicon-based methods to obtain intraspecific nuclear genetic information and estimate the absolute abundance of a species in eDNA samples. With proper validation, this approach has the potential to advance noninvasive survey methods to characterize populations and detect population-level genetic diversity.

Deciphering the Genetic Diversity of Landraces With High-Throughput SNP Genotyping of DNA Bulks: Methodology and Application to the Maize 50k Array

Genebanks harbor original landraces carrying many original favorable alleles for mitigating biotic and abiotic stresses. Their genetic diversity remains, however, poorly characterized due to their large within genetic diversity. We developed a high-throughput, cheap and labor saving DNA bulk approach based on single-nucleotide polymorphism (SNP) Illumina Infinium HD array to genotype landraces. Samples were gathered for each landrace by mixing equal weights from young leaves, from which DNA was extracted. We then estimated allelic frequencies in each DNA bulk based on fluorescent intensity ratio (FIR) between two alleles at each SNP using a two step-approach. We first tested either whether the DNA bulk was monomorphic or polymorphic according to the two FIR distributions of individuals homozygous for allele A or B, respectively. If the DNA bulk was polymorphic, we estimated its allelic frequency by using a predictive equation calibrated on FIR from DNA bulks with known allelic frequencies. Our approach: (i) gives accurate allelic frequency estimations that are highly reproducible across laboratories, (ii) protects against false detection of allele fixation within landraces. We estimated allelic frequencies of 23,412 SNPs in 156 landraces representing American and European maize diversity. Modified Roger's genetic Distance between 156 landraces estimated from 23,412 SNPs and 17 simple sequence repeats using the same DNA bulks were highly correlated, suggesting that the ascertainment bias is low. Our approach is affordable, easy to implement and does not require specific bioinformatics support and laboratory equipment, and therefore should be highly relevant for large-scale characterization of genebanks for a wide range of species.

Introduction to Population Genomics Methods

High-throughput sequencing technologies have provided an unprecedented opportunity to study the different evolutionary forces that have shaped present-day patterns of genetic diversity, with important implications for many directions in plant biology research. To manage such massive quantities of sequencing data, biologists, however, need new additional skills in informatics and statistics. In this chapter, our objective is to introduce population genomics methods to beginners following a learning-by-doing strategy in order to help the reader to analyze the sequencing data by themselves. Conducted analyses cover several main areas of evolutionary biology, such as an initial description of the evolutionary history of a given species or the identification of genes targeted by natural or artificial selection. In addition to the practical advices, we performed re-analyses of two cases studies with different kind of data: a domesticated cereal (African rice) and a non-domesticated tree species (sessile oak). All the code needed to replicate this work is publicly available on github ( https://github.com/ThibaultLeroyFr/Intro2PopGenomics/ ).

Genomics versus mtDNA for resolving stock structure in the silky shark (Carcharhinus falciformis)

Conservation genetic approaches for elasmobranchs have focused on regions of the mitochondrial genome or a handful of nuclear microsatellites. High-throughput sequencing offers a powerful alternative for examining population structure using many loci distributed across the nuclear and mitochondrial genomes. These single nucleotide polymorphisms are expected to provide finer scale and more accurate population level data; however, there have been few genomic studies applied to elasmobranch species. The desire to apply next-generation sequencing approaches is often tempered by the costs, which can be offset by pooling specimens prior to sequencing (pool-seq). In this study, we assess the utility of pool-seq by applying this method to the same individual silky sharks, Carcharhinus falciformis, previously surveyed with the mtDNA control region in the Atlantic and Indian Oceans. Pool-seq methods were able to recover the entire mitochondrial genome as well as thousands of nuclear markers. This volume of sequence data enabled the detection of population structure between regions of the Atlantic Ocean populations, undetected in the previous study (inter-Atlantic mitochondrial SNPs FST values comparison ranging from 0.029 to 0.135 and nuclear SNPs from 0.015 to 0.025). Our results reinforce the conclusion that sampling the mitochondrial control region alone may fail to detect fine-scale population structure, and additional sampling across the genome may increase resolution for some species. Additionally, this study shows that the costs of analyzing 4,988 loci using pool-seq methods are equivalent to the standard Sanger-sequenced markers and become less expensive when large numbers of individuals (>300) are analyzed.

Microbial ecology and predicted metabolic pathways in various oral environments from patients with acute endodontic infections

AIM

To assess in a cross-sectional clinical study the effect of antibiotics on the diversity, structure and metabolic pathways of bacterial communities in various oral environments in patients with acute primary infections.

METHODOLOGY

Samples of saliva (SA), supragingival biofilm (SB) and from the pulp cavity (PC) were collected from teeth with acute primary infections and then grouped according to previous use of antibiotics (NoAtb = no antibiotics [n = 6]; Atb = antibiotics [n = 6]). DNA sequencing was conducted using MiSeq (Illumina, San Diego, CA, USA). The V1-V3 hyper-variable region of the 16S rRNA gene was amplified. A custom Mothur pipeline was used for 16S rRNA processing. Subsequent analyses of the sequence dataset were performed in R (using vegan, phyloseq and ggplot2 packages) or QIIME.

RESULTS

Twelve patients aged from 22 to 56 years were recruited. Participants in the Atb group had taken the beta-lactamics amoxicillin (5/6) or cephalexin (1/6) for 2-3 days. A total of 332 bacterial taxa (OTUs) were identified, belonging to 120 genera, 60 families and nine phyla. Firmicutes (41%) and Bacteroidetes (38%) were the most abundant phyla in all samples. Taxa clustered significantly by oral site (PCoA analysis; P < 0.05, ANOSIM). Use of antibiotics had little effect on this clustering. However, SA, SB and PC had different degrees of richness, diversity and evenness. The greatest diversity was observed in SB samples and the least diversity was observed in PC samples. Metabolic prediction identified 163 pathways and previous use of antibiotics had a major effect on the estimated functional clustering in SA and PC samples.

CONCLUSION

The ecological niche had a strong influence on the bacterial content of samples from various oral sites. Previous exposure to antibiotics may exert an effect on the phylogenetic composition of SA. Metabolic pathways appear to be modulated by antimicrobial agents in SA and PC samples. The dynamics of host/microbial interactions in the apical region and the functional ecology of the infected pulp cavity should be revisited.

High accuracy of pooled DNA genotyping by 2b-RAD sequencing in the Pacific white shrimp, Litopenaeus vannamei

Using pooled DNA genotyping to estimate the proportional contributions from multiple families in a pooled sample is of particular interest for selective breeding in aquaculture. We compared different pooled libraries with separate 2b-RAD sequencing of Litopenaeus vannamei individuals to assess the effect of different population structures (different numbers of individuals and families) on pooled DNA sequencing, the accuracy of parent sequencing of the DNA pools and the effect of SNP numbers on pooled DNA sequencing. We demonstrated that small pooled DNA genotyping of up to 53 individuals by 2b-RAD sequencing could provide a highly accurate assessment of population allele frequencies. The accuracy increased as the number of individuals and families increased. The allele frequencies of the parents from each pool were highly correlated with those of the pools or the corresponding individuals in the pool. We chose 500-28,000 SNPs to test the effect of SNP number on the accuracy of pooled sequencing, and no linear relationship was found between them. When the SNP number was fixed, increasing the number of individuals in the mixed pool resulted in higher accuracy of each pooled genotyping. Our data confirmed that pooled DNA genotyping by 2b-RAD sequencing could achieve higher accuracy than that of individual-based genotyping. The results will provide important information for shrimp breeding programs.

Simulation with RADinitio improves RADseq experimental design and sheds light on sources of missing data

Restriction-site associated DNA sequencing (RADseq) has become a powerful and versatile tool in modern population genomics, enabling large-scale evolutionary and genomic analyses in otherwise inaccessible biological systems. With its widespread use, different variants on the protocol have been developed to suit specific experimental needs. Researchers face the challenge of choosing the optimal molecular and sequencing protocols for their reduced representation experimental design, an often-complicated process. Strategic errors can lead to biased data generation that has reduced power to answer biological questions. Here, we present RADinitio, simulation software for the selection and optimization of RADseq experiments via the generation of sequencing data that behave similarly to empirical sources. RADinitio provides an evolutionary simulation of populations, implementation of various RADseq protocols with customizable parameters, and thorough assessment of missing data. We test the efficacy of the software using different RAD protocols across several organisms, highlighting the importance of protocol selection on the magnitude and quality of data acquired. Additionally, we test the effects of RAD library preparation and sequencing on allelic dropout, observing that library preparation and sequencing often contributes more to missing alleles than population-level variation.

Massive postglacial gene flow between European white oaks uncovered genes underlying species barriers

Oaks are dominant forest tree species widely distributed across the Northern Hemisphere, where they constitute natural resources of economic, ecological, social and historical value. Hybridisation and adaptive introgression have long been thought to be major drivers of their ecological success. Therefore, the maintenance of species barriers remains a key question, given the extent of interspecific gene flow. In this study, we made use of the tremendous genetic variation among four European white oak species (31 million single nucleotide polymorphisms (SNPs)) to infer the evolutionary history of these species, study patterns of genetic differentiation and identify reproductive barriers. We first analysed the ecological and historical relationships among these species and inferred a long-term strict isolation followed by a recent and extensive postglacial contact using approximate Bayesian computation. Assuming this demographic scenario, we then performed backward simulations to generate the expected distributions of differentiation under neutrality to scan their genomes for reproductive barriers. We finally identified important intrinsic and ecological functions driving the reproductive isolation. We discussed the importance of identifying the genetic basis for the ecological preferences between these oak species and its implications for the renewal of European forests under global warming.

Genomic release-recapture experiment in the wild reveals within-generation polygenic selection in stickleback fish

How rapidly natural selection sorts genome-wide standing genetic variation during adaptation remains largely unstudied experimentally. Here, we present a genomic release-recapture experiment using paired threespine stickleback fish populations adapted to selectively different lake and stream habitats. First, we use pooled whole-genome sequence data from the original populations to identify hundreds of candidate genome regions likely under divergent selection between these habitats. Next, we generate F2 hybrids from the same lake-stream population pair in the laboratory and release thousands of juveniles into a natural stream habitat. Comparing the individuals surviving one year of stream selection to a reference sample of F2 hybrids allows us to detect frequency shifts across the candidate regions toward the genetic variants typical of the stream population-an experimental outcome consistent with polygenic directional selection. Our study reveals that adaptation in nature can be detected as a genome-wide signal over just a single generation.

A newly developed genetic sex marker and its application to understanding chemically induced feminisation in roach (Rutilus rutilus)

Oestrogenic wastewater treatment works (WwTW) effluents discharged into UK rivers have been shown to affect sexual development, including inducing intersex, in wild roach (Rutilus rutilus). This can result in a reduced breeding capability with potential population level impacts. In the absence of a sex probe for roach it has not been possible to confirm whether intersex fish in the wild arise from genetic males or females, or whether sex reversal occurs in the wild, as this condition can be induced experimentally in controlled exposures to WwTW effluents and a steroidal oestrogen. Using restriction site-associated DNA sequencing (RAD-seq), we identified a candidate for a genetic sex marker and validated this marker as a sex probe through PCR analyses of samples from wild roach populations from nonpolluted rivers. We also applied the sex marker to samples from roach exposed experimentally to oestrogen and oestrogenic effluents to confirm suspected phenotypic sex reversal from males to females in some treatments, and also that sex-reversed males are able to breed as females. We then show, unequivocally, that intersex in wild roach populations results from feminisation of males, but find no strong evidence for complete sex reversal in wild roach at river sites contaminated with oestrogens. The discovered marker has utility for studies in roach on chemical effects, wild stock assessments, and reducing the number of fish used where only one sex is required for experimentation. Furthermore, we show that the marker can be applied nondestructively using a fin clip or skin swab, with animal welfare benefits.

A-to-I RNA Editing Uncovers Hidden Signals of Adaptive Genome Evolution in Animals

In animals, the most common type of RNA editing is the deamination of adenosines (A) into inosines (I). Because inosines basepair with cytosines (C), they are interpreted as guanosines (G) by the cellular machinery and genomically encoded G alleles at edited sites mimic the function of edited RNAs. The contribution of this hardwiring effect on genome evolution remains obscure. We looked for population genomics signatures of adaptive evolution associated with A-to-I RNA edited sites in humans and Drosophila melanogaster. We found that single nucleotide polymorphisms at edited sites occur 3 (humans) to 15 times (Drosophila) more often than at unedited sites, the nucleotide G is virtually the unique alternative allele at edited sites and G alleles segregate at higher frequency at edited sites than at unedited sites. Our study reveals that a significant fraction of coding synonymous and nonsynonymous as well as silent and intergenic A-to-I RNA editing sites are likely adaptive in the distantly related human and Drosophila lineages.