THE GENETIC ARCHITECTURE OF REPRODUCTIVE ISOLATION DURING SPECIATION‐WITH‐GENE‐FLOW IN LAKE WHITEFISH SPECIES PAIRS ASSESSED BY RAD SEQUENCING

During speciation‐with‐gene‐flow, effective migration varies across the genome as a function of several factors, including proximity of selected loci, recombination rate, strength of selection, and number of selected loci. Genome scans may provide better empirical understanding of the genome‐wide patterns of genetic differentiation, especially if the variance due to the previously mentioned factors is partitioned. In North American lake whitefish (Coregonus clupeaformis), glacial lineages that diverged in allopatry about 60,000 years ago and came into contact 12,000 years ago have independently evolved in several lakes into two sympatric species pairs (a normal benthic and a dwarf limnetic). Variable degrees of reproductive isolation between species pairs across lakes offer a continuum of genetic and phenotypic divergence associated with adaptation to distinct ecological niches. To disentangle the complex array of genetically based barriers that locally reduce the effective migration rate between whitefish species pairs, we compared genome‐wide patterns of divergence across five lakes distributed along this divergence continuum. Using restriction site associated DNA (RAD) sequencing, we combined genetic mapping and population genetics approaches to identify genomic regions resistant to introgression and derive empirical measures of the barrier strength as a function of recombination distance. We found that the size of the genomic islands of differentiation was influenced by the joint effects of linkage disequilibrium maintained by selection on many loci, the strength of ecological niche divergence, as well as demographic characteristics unique to each lake. Partial parallelism in divergent genomic regions likely reflected the combined effects of polygenic adaptation from standing variation and independent changes in the genetic architecture of postzygotic isolation. This study illustrates how integrating genetic mapping and population genomics of multiple sympatric species pairs provide a window on the speciation‐with‐gene‐flow mechanism.     

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

Molecular markers produced by next‐generation sequencing (NGS) technologies are revolutionizing genetic research. However, the costs of analysing large numbers of individual genomes remain prohibitive for most population genetics studies. Here, we present results based on mathematical derivations showing that, under many realistic experimental designs, NGS of DNA pools from diploid individuals allows to estimate the allele frequencies at single nucleotide polymorphisms (SNPs) with at least the same accuracy as individual‐based analyses, for considerably lower library construction and sequencing efforts. These findings remain true when taking into account the possibility of substantially unequal contributions of each individual to the final pool of sequence reads. We propose the intuitive notion of effective pool size to account for unequal pooling and derive a Bayesian hierarchical model to estimate this parameter directly from the data. We provide a user‐friendly application assessing the accuracy of allele frequency estimation from both pool‐ and individual‐based NGS population data under various sampling, sequencing depth and experimental error designs. We illustrate our findings with theoretical examples and real data sets corresponding to SNP loci obtained using restriction site–associated DNA (RAD) sequencing in pool‐ and individual‐based experiments carried out on the same population of the pine processionary moth (Thaumetopoea pityocampa). NGS of DNA pools might not be optimal for all types of studies but provides a cost‐effective approach for estimating allele frequencies for very large numbers of SNPs. It thus allows comparison of genome‐wide patterns of genetic variation for large numbers of individuals in multiple populations.     

Stochastic exits from dormancy give rise to heavy‐tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy‐tailed, with a few cells effectively ‘winning the jackpot’ to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early‐germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy‐tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.     

Exploration of Genetic Variations through Single‐cell Whole‐genome Sequencing in the Model Ciliate Tetrahymena thermophila

Ciliates are unicellular eukaryotes with separate germline and somatic genomes and diverse life cycles, which make them a unique model to improve our understanding of population genetics through the detection of genetic variations. However, traditional sequencing methods cannot be directly applied to ciliates because the majority are uncultivated. Single‐cell whole‐genome sequencing (WGS) is a powerful tool for studying genetic variation in microbes, but no studies have been performed in ciliates. We compared the use of single‐cell WGS and bulk DNA WGS to detect genetic variation, specifically single nucleotide polymorphisms (SNPs), in the model ciliate Tetrahymena thermophila. Our analyses showed that (i) single‐cell WGS has excellent performance regarding mapping rate and genome coverage but lower sequencing uniformity compared with bulk DNA WGS due to amplification bias (which was reproducible); (ii) false‐positive SNP sites detected by single‐cell WGS tend to occur in genomic regions with particularly high sequencing depth and high rate of C:G to T:A base changes; (iii) SNPs detected in three or more cells should be reliable (an detection efficiency of 83.4–97.4% was obtained for combined data from three cells). This analytical method could be adapted to measure genetic variation in other ciliates and broaden research into ciliate population genetics.     

A new method for studying population genetics of cyst nematodes based on Pool‐Seq and genomewide allele frequency analysis

Cyst nematodes are important agricultural pests responsible for billions of dollars of losses each year. Plant resistance is the most effective management tool, but it requires a close monitoring of population genetics. Current technologies for pathotyping and genotyping cyst nematodes are time‐consuming, expensive and imprecise. In this study, we capitalized on the reproduction mode of cyst nematodes to develop a simple population genetic analysis pipeline based on genotyping‐by‐sequencing and Pool‐Seq. This method yielded thousands of SNPs and allowed us to study the relationships between populations of different origins or pathotypes. Validation of the method on well‐characterized populations also demonstrated that it was a powerful and accurate tool for population genetics. The genomewide allele frequencies of 23 populations of golden nematode, from nine countries and representing the five known pathotypes, were compared. A clear separation of the pathotypes and fine genetic relationships between and among global populations were obtained using this method. In addition to being powerful, this tool has proven to be very time‐ and cost‐efficient and could be applied to other cyst nematode species.     

Development and testing of a genetic marker‐based pedigree reconstruction system ‘PR‐genie’ incorporating size‐class data

For wildlife populations, it is often difficult to determine biological parameters that indicate breeding patterns and population mixing, but knowledge of these parameters is essential for effective management. A pedigree encodes the relationship between individuals and can provide insight into the dynamics of a population over its recent history. Here, we present a method for the reconstruction of pedigrees for wild populations of animals that live long enough to breed multiple times over their lifetime and that have complex or unknown generational structures. Reconstruction was based on microsatellite genotype data along with ancillary biological information: sex and observed body size class as an indicator of relative age of individuals within the population. Using body size‐class data to infer relative age has not been considered previously in wildlife genealogy and provides a marked improvement in accuracy of pedigree reconstruction. Body size‐class data are particularly useful for wild populations because it is much easier to collect noninvasively than absolute age data. This new pedigree reconstruction system, PR‐genie, performs reconstruction using maximum likelihood with optimization driven by the cross‐entropy method. We demonstrated pedigree reconstruction performance on simulated populations (comparing reconstructed pedigrees to known true pedigrees) over a wide range of population parameters and under assortative and intergenerational mating schema. Reconstruction accuracy increased with the presence of size‐class data and as the amount and quality of genetic data increased. We provide recommendations as to the amount and quality of data necessary to provide insight into detailed familial relationships in a wildlife population using this pedigree reconstruction technique.     

Highly accurate SNP genotyping from historical and low‐quality samples

Historical and other poor‐quality samples are often necessary for population genetics, conservation, and forensics studies. Although there is a long history of using mtDNA from such samples, obtaining and genotyping nuclear loci have been considered difficult and error‐prone at best, and impossible at worst. The primary issues are the amount of nuclear DNA available for genotyping, and the degradation of the DNA into small fragments. Single nucleotide polymorphisms offer potential advantages for assaying nuclear variation in historical and poor‐quality samples, because the amplified fragments can be very small, varying little or not at all in size between alleles, and can be amplified efficiently by polymerase chain reaction (PCR). We present a method for highly multiplexed PCR of SNP loci, followed by dual‐fluorescence genotyping that is very effective for genotyping poor‐quality samples, and can potentially use very little template DNA, regardless of the number of loci to be genotyped. We genotyped 19 SNP loci from DNA extracted from modern and historical bowhead whale tissue, bone and baleen samples. The PCR failure rate was < 1.5%, and the genotyping error rate was 0.1% when DNA samples contained > 10 copies/µL of a 51‐bp nuclear sequence. Among samples with ≤ 10 copies/µL DNA, samples could still be genotyped confidently with appropriate levels of replication from independent multiplex PCRs.     

Effective size of density‐dependent populations in fluctuating environments

Reliable estimates of effective population size are of central importance in population genetics and evolutionary biology. For populations that fluctuate in size, harmonic mean population size is commonly used as a proxy for (multi‐) generational effective size. This assumes no effects of density dependence on the ratio between effective and actual population size, which limits its potential application. Here, we introduce density dependence on vital rates in a demographic model of variance effective size. We derive an expression for the ratio in a density‐regulated population in a fluctuating environment. We show by simulations that yearly genetic drift is accurately predicted by our model, and not proportional to as assumed by the harmonic mean model, where N is the total population size of mature individuals. We find a negative relationship between and N. For a given N, the ratio depends on variance in reproductive success and the degree of resource limitation acting on the population growth rate. Finally, our model indicate that environmental stochasticity may affect not only through fluctuations in N, but also for a given N at a given time. Our results show that estimates of effective population size must include effects of density dependence and environmental stochasticity.     

Effective size of density‐dependent two‐sex populations: the effect of mating systems

Density dependence in vital rates is a key feature affecting temporal fluctuations of natural populations. This has important implications for the rate of random genetic drift. Mating systems also greatly affect effective population sizes, but knowledge of how mating system and density regulation interact to affect random genetic drift is poor. Using theoretical models and simulations, we compare N_e in short‐lived, density‐dependent animal populations with different mating systems. We study the impact of a fluctuating, density‐dependent sex ratio and consider both a stable and a fluctuating environment. We find a negative relationship between annual N_e/N and adult population size N due to density dependence, suggesting that loss of genetic variation is reduced at small densities. The magnitude of this decrease was affected by mating system and life history. A male‐biased, density‐dependent sex ratio reduces the rate of genetic drift compared to an equal, density‐independent sex ratio, but a stochastic change towards male bias reduces the N_e/N ratio. Environmental stochasticity amplifies temporal fluctuations in population size and is thus vital to consider in estimation of effective population sizes over longer time periods. Our results on the reduced loss of genetic variation at small densities, particularly in polygamous populations, indicate that density regulation may facilitate adaptive evolution at small population sizes.     

Recent progress and challenges in population genetics of polyploid organisms: an overview of current state‐of‐the‐art molecular and statistical tools

Despite the importance of polyploidy and the increasing availability of new genomic data, there remain important gaps in our knowledge of polyploid population genetics. These gaps arise from the complex nature of polyploid data (e.g. multiple alleles and loci, mixed inheritance patterns, association between ploidy and mating system variation). Furthermore, many of the standard tools for population genetics that have been developed for diploids are often not feasible for polyploids. This review aims to provide an overview of the state‐of‐the‐art in polyploid population genetics and to identify the main areas where further development of molecular techniques and statistical theory is required. We review commonly used molecular tools (amplified fragment length polymorphism, microsatellites, Sanger sequencing, next‐generation sequencing and derived technologies) and their challenges associated with their use in polyploid populations: that is, allele dosage determination, null alleles, difficulty of distinguishing orthologues from paralogues and copy number variation. In addition, we review the approaches that have been used for population genetic analysis in polyploids and their specific problems. These problems are in most cases directly associated with dosage uncertainty and the problem of inferring allele frequencies and assumptions regarding inheritance. This leads us to conclude that for advancing the field of polyploid population genetics, most priority should be given to development of new molecular approaches that allow efficient dosage determination, and to further development of analytical approaches to circumvent dosage uncertainty and to accommodate ‘flexible’ modes of inheritance. In addition, there is a need for more simulation‐based studies that test what kinds of biases could result from both existing and novel approaches.     

SIMULATION‐BASED LIKELIHOOD APPROACH FOR EVOLUTIONARY MODELS OF PHENOTYPIC TRAITS ON PHYLOGENY

Phylogenetic comparative methods (PCMs) have been used to test evolutionary hypotheses at phenotypic levels. The evolutionary modes commonly included in PCMs are Brownian motion (genetic drift) and the Ornstein–Uhlenbeck process (stabilizing selection), whose likelihood functions are mathematically tractable. More complicated models of evolutionary modes, such as branch‐specific directional selection, have not been used because calculations of likelihood and parameter estimates in the maximum‐likelihood framework are not straightforward. To solve this problem, we introduced a population genetics framework into a PCM, and here, we present a flexible and comprehensive framework for estimating evolutionary parameters through simulation‐based likelihood computations. The method does not require analytic likelihood computations, and evolutionary models can be used as long as simulation is possible. Our approach has many advantages: it incorporates different evolutionary modes for phenotypes into phylogeny, it takes intraspecific variation into account, it evaluates full likelihood instead of using summary statistics, and it can be used to estimate ancestral traits. We present a successful application of the method to the evolution of brain size in primates. Our method can be easily implemented in more computationally effective frameworks such as approximate Bayesian computation (ABC), which will enhance the use of computationally intensive methods in the study of phenotypic evolution.     

Continued misuse of multiple testing correction methods in population genetics‐A wake‐up call?

Population geneticists often use multiple independent hypothesis tests of Hardy–Weinberg Equilibrium (HWE), Linkage Disequilibrium (LD), and population differentiation, to make broad inferences about their systems of choice. However, correcting for Family‐Wise Error Rates (FWER) that are inflated due to multiple comparisons, is sparingly reported in our current literature. In this issue of Molecular Ecology Resources, perform a meta‐analysis of 215 population genetics studies published between 2011 and 2013 to show (i) scarce use of FWER corrections across all three classes of tests, and (ii) when used, inconsistent application of correction methods with a clear bias towards less‐conservative corrections for tests of population differentiation, than for tests of HWE, and LD. Here we replicate this meta‐analysis using 205 population genetics studies published between 2013 and 2018, to show the same continued disuse, and inconsistencies. We hope that both studies serve as a wake‐up call to population geneticists, reviewers, and editors to be rigorous about consistently correcting for FWER inflation.     

Genotype‐by‐sequencing of the plant‐pathogenic fungi Pyrenophora teres and Sphaerulina musiva utilizing Ion Torrent sequence technology

Genetic and genomics tools to characterize host–pathogen interactions are disproportionately directed to the host because of the focus on resistance. However, understanding the genetics of pathogen virulence is equally important and has been limited by the high cost of de novo genotyping of species with limited marker data. Non‐resource‐prohibitive methods that overcome the limitation of genotyping are now available through genotype‐by‐sequencing (GBS). The use of a two‐enzyme restriction‐associated DNA (RAD)‐GBS method adapted for Ion Torrent sequencing technology provided robust and reproducible high‐density genotyping of several fungal species. A total of 5783 and 2373 unique loci, ‘sequence tags’, containing 16 441 and 9992 single nucleotide polymorphisms (SNPs) were identified and characterized from natural populations of Pyrenophora teres f. maculata and Sphaerulina musiva, respectively. The data generated from the P. teres f. maculata natural population were used in association mapping analysis to map the mating‐type gene to high resolution. To further validate the methodology, a biparental population of P. teres f. teres, previously used to develop a genetic map utilizing simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers, was re‐analysed using the SNP markers generated from this protocol. A robust genetic map containing 1393 SNPs on 997 sequence tags spread across 15 linkage groups with anchored reference markers was generated from the P. teres f. teres biparental population. The robust high‐density markers generated using this protocol will allow positional cloning in biparental fungal populations, association mapping of natural fungal populations and population genetics studies.     

EFFECTIVE POPULATION SIZE AND THE FASTER‐X EFFECT: EMPIRICAL RESULTS AND THEIR INTERPRETATION

The X or Z chromosome has several characteristics that distinguish it from the autosomes, namely hemizygosity in the heterogametic sex, and a potentially different effective population size, both of which may influence the rate and nature of evolution. In particular, there may be an accelerated rate of adaptive change for X‐linked compared to autosomal coding sequences, often referred to as the Faster‐X effect. Empirical studies have indicated that the strength of Faster‐X evolution varies among different species, and theoretical treatments have shown that demography and mating system can substantially affect the degree of Faster‐X evolution. Here we integrate genomic data on Faster‐X evolution from a variety of animals with the demographic factors, mating system, and sex chromosome regulatory characteristics that may influence it. Our results suggest that differences in effective population size and mechanisms of dosage compensation may influence the perceived extent of Faster‐X evolution, and help to explain several clade‐specific patterns that we observe.     

A resource of genome‐wide single‐nucleotide polymorphisms generated by RAD tag sequencing in the critically endangered European eel

Reduced representation genome sequencing such as restriction‐site‐associated DNA (RAD) sequencing is finding increased use to identify and genotype large numbers of single‐nucleotide polymorphisms (SNPs) in model and nonmodel species. We generated a unique resource of novel SNP markers for the European eel using the RAD sequencing approach that was simultaneously identified and scored in a genome‐wide scan of 30 individuals. Whereas genomic resources are increasingly becoming available for this species, including the recent release of a draft genome, no genome‐wide set of SNP markers was available until now. The generated SNPs were widely distributed across the eel genome, aligning to 4779 different contigs and 19 703 different scaffolds. Significant variation was identified, with an average nucleotide diversity of 0.00529 across individuals. Results varied widely across the genome, ranging from 0.00048 to 0.00737 per locus. Based on the average nucleotide diversity across all loci, long‐term effective population size was estimated to range between 132 000 and 1 320 000, which is much higher than previous estimates based on microsatellite loci. The generated SNP resource consisting of 82 425 loci and 376 918 associated SNPs provides a valuable tool for future population genetics and genomics studies and allows for targeting specific genes and particularly interesting regions of the eel genome.     

Fine‐scale genetic structure reflects sex‐specific dispersal strategies in a population of sociable weavers (Philetairus socius)

Dispersal is a critical driver of gene flow, with important consequences for population genetic structure, social interactions and other biological processes. Limited dispersal may result in kin‐structured populations in which kin selection may operate, but it may also increase the risk of kin competition and inbreeding. Here, we use a combination of long‐term field data and molecular genetics to examine dispersal patterns and their consequences for the population genetics of a highly social bird, the sociable weaver (Philetairus socius), which exhibits cooperation at various levels of sociality from nuclear family groups to its unique communal nests. Using 20 years of data, involving capture of 6508 birds and 3151 recaptures at 48 colonies, we found that both sexes exhibit philopatry and that any dispersal occurs over relatively short distances. Dispersal is female‐biased, with females dispersing earlier, further, and to less closely related destination colonies than males. Genotyping data from 30 colonies showed that this pattern of dispersal is reflected by fine‐scale genetic structure for both sexes, revealed by isolation by distance in terms of genetic relatedness and significant genetic variance among colonies. Both relationships were stronger among males than females. Crucially, significant relatedness extended beyond the level of the colony for both sexes. Such fine‐scale population genetic structure may have played an important role in the evolution of cooperative behaviour in this species, but it may also result in a significant inbreeding risk, against which female‐biased dispersal alone is unlikely to be an effective strategy.     

Population genetics of the olive‐winged bulbul (Pycnonotus plumosus) in a tropical urban‐fragmented landscape

With increasing urbanization, urban‐fragmented landscapes are becoming more and more prevalent worldwide. Such fragmentation may lead to small, isolated populations that face great threats from genetic factors that affect even avian species with high dispersal propensities. Yet few studies have investigated the population genetics of species living within urban‐fragmented landscapes in the Old World tropics, in spite of the high levels of deforestation and fragmentation within this region. We investigated the evolutionary history and population genetics of the olive‐winged bulbul (Pycnonotus plumosus) in Singapore, a highly urbanized island which retains <5% of its original forest cover in fragments. Combining our own collected and sequenced samples with those from the literature, we conducted phylogenetic and population genetic analyses. We revealed high genetic diversity, evidence for population expansion, and potential presence of pronounced gene flow across the population in Singapore. This suggests increased chances of long‐term persistence for the olive‐winged bulbul and the ecosystem services it provides within this landscape.     

Interval Estimation of the Effective Population Size from Heterozygote‐Excess in SNP Markers

The effective population size N_e is an important parameter in population genetics and conservation biology. In recent years, there has been great interest in the use of molecular markers to estimate N_e. Although the point estimates from molecular markers in general suffer from a low reliability, the use of single nucleotide polymorphism (SNP) markers over a wide range of genome is expected to remarkably improve the reliability. In this study, expressions were derived for interval estimates of N_e from one published method, the heterozygote‐excess method, when it is applied to SNP markers. The conditional variance theory is applied to the derivation of a confidence interval for N_e under random union of gametes, monogamy and polygyny. Stochastic simulation shows that the obtained confidence interval is slightly conservative, but fairly useful for practical applications. The result is illustrated with real data on SNP markers in a pig strain.     

Effects of size‐ and sex‐selective harvesting: An integral projection model approach

Harvesting is often size‐selective, and in species with sexual size dimorphism, it may also be sex‐selective. A powerful approach to investigate potential consequences of size‐ and/or sex‐selective harvesting is to simulate it in a demographic population model. We developed a population‐based integral projection model for a size‐ and sex‐structured species, the commonly exploited pike (Esox lucius). The model allows reproductive success to be proportional to body size and potentially limited by both sexes. We ran all harvest simulations with both lower size limits and slot limits, and to quantify the effects of selective harvesting, we calculated sex ratios and the long‐term population growth rate (λ). In addition, we quantified to what degree purely size‐selective harvesting was sex‐selective, and determined when λ shifted from being female to male limited under size‐ and sex‐selective harvesting. We found that purely size‐selective harvest can be sex‐selective, and that it depends on the harvest limits and the size distributions of the sexes. For the size‐ and sex‐selective harvest simulations, λ increased with harvest intensity up to a threshold as females limited reproduction. Beyond this threshold, males became the limiting sex, and λ decreased as more males were harvested. The peak in λ, and the corresponding sex ratio in harvest, varied with both the selectivity and the intensity of the harvest simulation. Our model represents a useful extension of size‐structured population models as it includes both sexes, relaxes the assumption of female dominance, and accounts for size‐dependent fecundity. The consequences of selective harvesting presented here are especially relevant for size‐ and sex‐structured exploited species, such as commercial fisheries. Thus, our model provides a useful contribution toward the development of more sustainable harvesting regimes.     

In defence of model‐based inference in phylogeography

Recent papers have promoted the view that model‐based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model‐based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model‐based inference in population genetics.