Simulating natural selection in landscape genetics

Linking landscape effects to key evolutionary processes through individual organism movement and natural selection is essential to provide a foundation for evolutionary landscape genetics. Of particular importance is determining how spatially-explicit, individual-based models differ from classic population genetics and evolutionary ecology models based on ideal panmictic populations in an allopatric setting in their predictions of population structure and frequency of fixation of adaptive alleles. We explore initial applications of a spatially-explicit, individual-based evolutionary landscape genetics program that incorporates all factors--mutation, gene flow, genetic drift and selection--that affect the frequency of an allele in a population. We incorporate natural selection by imposing differential survival rates defined by local relative fitness values on a landscape. Selection coefficients thus can vary not only for genotypes, but also in space as functions of local environmental variability. This simulator enables coupling of gene flow (governed by resistance surfaces), with natural selection (governed by selection surfaces). We validate the individual-based simulations under Wright-Fisher assumptions. We show that under isolation-by-distance processes, there are deviations in the rate of change and equilibrium values of allele frequency. The program provides a valuable tool (cdpop v1.0; http://cel.dbs.umt.edu/software/CDPOP/) for the study of evolutionary landscape genetics that allows explicit evaluation of the interactions between gene flow and selection in complex landscapes.     

An overview of population genetic data simulation

Simulation studies in population genetics play an important role in helping to better understand the impact of various evolutionary and demographic scenarios on sequence variation and sequence patterns, and they also permit investigators to better assess and design analytical methods in the study of disease-associated genetic factors. To facilitate these studies, it is imperative to develop simulators with the capability to accurately generate complex genomic data under various genetic models. Currently, a number of efficient simulation software packages for large-scale genomic data are available, and new simulation programs with more sophisticated capabilities and features continue to emerge. In this article, we review the three basic simulation frameworks--coalescent, forward, and resampling--and some of the existing simulators that fall under these frameworks, comparing them with respect to their evolutionary and demographic scenarios, their computational complexity, and their specific applications. Additionally, we address some limitations in current simulation algorithms and discuss future challenges in the development of more powerful simulation tools.     

Fundamentals of fungal molecular population genetic analyses

The last two decades have seen tremendous growth in the development and application of molecular methods in the analyses of fungal species and populations. In this paper, I provide an overview of the molecular techniques and the basic analytical tools used to address various fundamental population and evolutionary genetic questions in fungi. With increasing availability and decreasing cost, DNA sequencing is becoming a mainstream data acquisition method in fungal evolutionary genetic studies. However, other methods, especially those based on the polymerase chain reaction, remain powerful in addressing specific questions for certain groups of taxa. These developments are bringing fungal population and evolutionary genetics into mainstream ecology and evolutionary biology.     

WinPop 2.5: software for representing population genetics phenomena

The curriculum for genetics courses is shifting from a classical to a more molecular genetics focus, increasing the importance of subjects such as population genetics. Population genetics is a computational and statistical field that requires a good understanding of the nature of stochastic events. It is a difficult field for biology students with a limited mathematical background and there is a need for visualisation tools to facilitate understanding by the use of practical examples. WinPop provides students and researchers with a visual tool to allow the simulation and representation of population genetics phenomena. WinPop is a user-friendly software meant for use in population genetics courses and basic research. WinPop 2.5 contains six different modules that represent and simulate population genetics models. Genotype and allele frequencies are calculated under the different models: panmixia, genetic drift, assortative matings, selection, gene flow and mutation. The program's interface presents information in Cartesian graphics and isosceles triangular coordinate systems, allowing the user to save graphical and textual data output from the simulations. WinPop is developed in Visual Basic 6.0 and uses Windows 95 and higher. WinPop 2.5 can be downloaded from http://www.genedrift.org/winpop.php.     

Efficient genetic markers for population biology

Population genetics has come of age. Three important components have come together: efficient techniques to examine informative segments of DNA, statistics to analyse DNA data and the availability of easy-to-use computer packages. Single-locus genetic markers and those that produce gene genealogies yield information that is truly comparable among studies. These markers answer biological questions most efficiently and also contribute to much broader investigations of evolutionary, population and conservation biology. For these reasons, single-locus and genealogical markers should be the focus of the intensive genetic data collection that has begun owing to the power of genetics in population biology.     

PGEToolbox: A Matlab toolbox for population genetics and evolution

Assessing genetic diversity within populations is vital for understanding the nature of evolutionary processes at the molecular level. PGEToolbox is a Matlab-based open-sourced software package for data analysis in population genetics. The main features of this software are as follows: 1) capability for handling both DNA sequence polymorphisms and single nucleotide polymorphisms (SNPs), which include genotype and haplotype data; 2) exhaustive population genetic analyses and neutrality tests based on the coalescent theory; 3) extendibility and scalability for complex and large genome-wide datasets; 4) simple yet effective graphic user interfaces and sophisticated visualization of data and results. For academic uses, PGEToolbox is available free of charge at http://bioinformatics.org/pgetoolbox.     

Geonomics: Forward-Time,Spatially Explicit,and Arbitrarily Complex Landscape Genomic Simulations

Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics.     

Marker-Based Estimation of Heritability in Immortal Populations

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.     

A hierarchical Bayesian model for next-generation population genomics

The demography of populations and natural selection shape genetic variation across the genome and understanding the genomic consequences of these evolutionary processes is a fundamental aim of population genetics. We have developed a hierarchical Bayesian model to quantify genome-wide population structure and identify candidate genetic regions affected by selection. This model improves on existing methods by accounting for stochastic sampling of sequences inherent in next-generation sequencing (with pooled or indexed individual samples) and by incorporating genetic distances among haplotypes in measures of genetic differentiation. Using simulations we demonstrate that this model has a low false-positive rate for classifying neutral genetic regions as selected genes (i.e., Φ(ST) outliers), but can detect recent selective sweeps, particularly when genetic regions in multiple populations are affected by selection. Nonetheless, selection affecting just a single population was difficult to detect and resulted in a high false-negative rate under certain conditions. We applied the Bayesian model to two large sets of human population genetic data. We found evidence of widespread positive and balancing selection among worldwide human populations, including many genetic regions previously thought to be under selection. Additionally, we identified novel candidate genes for selection, several of which have been linked to human diseases. This model will facilitate the population genetic analysis of a wide range of organisms on the basis of next-generation sequence data.     

An overview of the utility of population simulation software in molecular ecology

Stochastic simulation software that simultaneously model genetic, population and environmental processes can inform many topics in molecular ecology. These include forecasting species and community response to environmental change, inferring dispersal ecology, revealing cryptic mating, quantifying past population dynamics, assessing in situ management options and monitoring neutral and adaptive biodiversity change. Advances in population demographic–genetic simulation software, especially with respect to individual life history, landscapes and genetic processes, are transforming and expanding the ways that molecular data can be used. The aim of this review is to explain the roles that such software can play in molecular ecology studies (whether as a principal component or a supporting function) so that researchers can decide whether, when and precisely how simulations can be incorporated into their work. First, I use seven case studies to demonstrate how simulations are employed, their specific advantage/necessity and what alternative or complementary (nonsimulation) approaches are available. I also explain how simulations can be integrated with existing spatial, environmental, historical and genetic data sets. I next describe simulation features that may be of interest to molecular ecologists, such as spatial and behavioural considerations and species' interactions, to provide guidance on how particular simulation capabilities can serve particular needs. Lastly, I discuss the prospect of simulation software in emerging challenges (climate change, biodiversity monitoring, population exploitation) and opportunities (genomics, ancient DNA), in order to emphasize that the scope of simulation‐based work is expanding. I also suggest practical considerations, priorities and elements of best practice. This should accelerate the uptake of simulation approaches and firmly embed them as a versatile tool in the molecular ecologist's toolbox.     

Mutate now, die later. Evolutionary dynamics with delayed selection

We analyze here the evolutionary consequences of selection with delay in a population genetics context. In the classical works on evolutionary dynamics, an individual produces off-springs in direct proportion to its fitness, a process in which mutations may occur. In the present scenario of delayed selection, individuals that acquire deleterious mutations can still reproduce unharmed for several generations. During this time delay, the damage passed on to off-springs can potentially be repaired by subsequent compensatory mutations. In the absence of such a repair, the individual becomes sterile. Here we study the population-genetic effects of such a time delay by means of both numerical simulations and theoretical modeling. The results show that delayed selection lowers the extinction threshold, endangering the survival of the population. Surprisingly, however, no traces of this delay effect are encountered in the sequence diversity of the population. These conclusions suggest that delayed selection is hard to detect in genetic data and thus could be a wide-spread but rarely detected phenomenon.     

An ecologist's guide to the animal model

1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known.
2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research.
3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations.
4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models.
5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.     

Isozymes in macroalgae (seaweeds): genetic differentiation,genetic variability and applications in systematics

Use of isozymes (including allozymes) in studies of population genetics and systematics of seaweeds has increased sufficiently in the last decade to allow some generalization. Only a single locus has been observed for about half the enzymes analysed in seaweeds, compared with 29% in vascular plants. Compared with higher plants, macroalgal species generally have low amounts of electrophoretically detectable genetic variation; the lowest levels of genetic variation found in natural populations are those reported for seaweeds. Nonetheless, seaweeds show an association between levels of genetic diversity as revealed by isozymes and species-specific attributes, such as mating system and predominance of asexual versus sexual reproduction. In systematic studies, isozymes have revealed cryptic species and identified pairs of sibling taxa. The quaternary structure of enzymes appears to be conserved at the phylum level. With the current availability of improved techniques for enzyme electrophoresis and for data interpretation, we expect future studies utilizing isozyme electrophoresis to provide further insight into population and evolutionary processes in seaweeds.     

History can matter: non-Markovian behavior of ancestral lineages

Although most of the important evolutionary events in the history of biology can only be studied via interspecific comparisons, it is challenging to apply the rich body of population genetic theory to the study of interspecific genetic variation. Probabilistic modeling of the substitution process would ideally be derived from first principles of population genetics, allowing a quantitative connection to be made between the parameters describing mutation, selection, drift, and the patterns of interspecific variation. There has been progress in reconciling population genetics and interspecific evolution for the case where mutation rates are sufficiently low, but when mutation rates are higher, reconciliation has been hampered due to complications from how the loss or fixation of new mutations can be influenced by linked nonneutral polymorphisms (i.e., the Hill-Robertson effect). To investigate the generation of interspecific genetic variation when concurrent fitness-affecting polymorphisms are common and the Hill-Robertson effect is thereby potentially strong, we used the Wright-Fisher model of population genetics to simulate very many generations of mutation, natural selection, and genetic drift. This was done so that the chronological history of advantageous, deleterious, and neutral substitutions could be traced over time along the ancestral lineage. Our simulations show that the process by which a nonrecombining sequence changes over time can markedly deviate from the Markov assumption that is ubiquitous in molecular phylogenetics. In particular, we find tendencies for advantageous substitutions to be followed by deleterious ones and for deleterious substitutions to be followed by advantageous ones. Such non-Markovian patterns reflect the fact that the fate of the ancestral lineage depends not only on its current allelic state but also on gene copies not belonging to the ancestral lineage. Although our simulations describe nonrecombining sequences, we conclude by discussing how non-Markovian behavior of the ancestral lineage is plausible even when recombination rates are not low. As a result, we believe that increased attention needs to be devoted to the robustness of evolutionary inference procedures that rely upon the Markov assumption.     

The G matrix under fluctuating correlational mutation and selection

Theoretical quantitative genetics provides a framework for reconstructing past selection and predicting future patterns of phenotypic differentiation. However, the usefulness of the equations of quantitative genetics for evolutionary inference relies on the evolutionary stability of the additive genetic variance-covariance matrix (G matrix). A fruitful new approach for exploring the evolutionary dynamics of G involves the use of individual-based computer simulations. Previous studies have focused on the evolution of the eigenstructure of G. An alternative approach employed in this paper uses the multivariate response-to-selection equation to evaluate the stability of G. In this approach, I measure similarity by the correlation between response-to-selection vectors due to random selection gradients. I analyze the dynamics of G under several conditions of correlational mutation and selection. As found in a previous study, the eigenstructure of G is stabilized by correlational mutation and selection. However, over broad conditions, instability of G did not result in a decreased consistency of the response to selection. I also analyze the stability of G when the correlation coefficients of correlational mutation and selection and the effective population size change through time. To my knowledge, no prior study has used computer simulations to investigate the stability of G when correlational mutation and selection fluctuate. Under these conditions, the eigenstructure of G is unstable under some simulation conditions. Different results are obtained if G matrix stability is assessed by eigenanalysis or by the response to random selection gradients. In this case, the response to selection is most consistent when certain aspects of the eigenstructure of G are least stable and vice versa.     

4P: fast computing of population genetics statistics from large DNA polymorphism panels

Massive DNA sequencing has significantly increased the amount of data available for population genetics and molecular ecology studies. However, the parallel computation of simple statistics within and between populations from large panels of polymorphic sites is not yet available, making the exploratory analyses of a set or subset of data a very laborious task. Here, we present 4P (parallel processing of polymorphism panels), a stand‐alone software program for the rapid computation of genetic variation statistics (including the joint frequency spectrum) from millions of DNA variants in multiple individuals and multiple populations. It handles a standard input file format commonly used to store DNA variation from empirical or simulation experiments. The computational performance of 4P was evaluated using large SNP (single nucleotide polymorphism) datasets from human genomes or obtained by simulations. 4P was faster or much faster than other comparable programs, and the impact of parallel computing using multicore computers or servers was evident. 4P is a useful tool for biologists who need a simple and rapid computer program to run exploratory population genetics analyses in large panels of genomic data. It is also particularly suitable to analyze multiple data sets produced in simulation studies. Unix, Windows, and MacOs versions are provided, as well as the source code for easier pipeline implementations.     

Microsatellite null alleles and estimation of population differentiation

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on F(ST) and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating F(ST) and genetic distances, and we compared this method with a new method proposed here (for F(ST) only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, F(ST) and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of F(ST) and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. F(ST) estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results.     

Simulation of Genomes: A Review

There is an increasing role of population genetics in human genetic research linking empirical observations with hypotheses about sequence variation due to historical and evolutionary causes. In addition, the data sets are increasing in size, with genome-wide data becoming a common place in many empirical studies. As far as more information is available, it becomes clear that simplest hypotheses are not consistent with data. Simulations will provide the key tool to contrast complex hypotheses on real data by generating simulated data under the hypothetical historical and evolutionary conditions that we want to contrast. Undoubtedly, developing tools for simulating large sequences that at the same time allow simulate natural selection, recombination and complex demography patterns will be of great interest in order to better understanding the trace left on the DNA by different interacting evolutionary forces. Simulation tools will be also essential to evaluate the sampling properties of any statistics used on genome-wide association studies and to compare performance of methods applied at genome-wide scales. Several recent simulation tools have been developed. Here, we review some of the currently existing simulators which allow for efficient simulation of large sequences on complex evolutionary scenarios. In addition, we will point out future directions in this field which are already a key part of the current research in evolutionary biology and it seems that it will be a primary tool in the future research of genome and post-genomic biology.     

A review of the application of molecular genetics for fisheries management and conservation of sharks and rays

Since the first investigation 25 years ago, the application of genetic tools to address ecological and evolutionary questions in elasmobranch studies has greatly expanded. Major developments in genetic theory as well as in the availability, cost effectiveness and resolution of genetic markers were instrumental for particularly rapid progress over the last 10 years. Genetic studies of elasmobranchs are of direct importance and have application to fisheries management and conservation issues such as the definition of management units and identification of species from fins. In the future, increased application of the most recent and emerging technologies will enable accelerated genetic data production and the development of new markers at reduced costs, paving the way for a paradigm shift from gene to genome-scale research, and more focus on adaptive rather than just neutral variation. Current literature is reviewed in six fields of elasmobranch molecular genetics relevant to fisheries and conservation management (species identification, phylogeography, philopatry, genetic effective population size, molecular evolutionary rate and emerging methods). Where possible, examples from the Indo-Pacific region, which has been underrepresented in previous reviews, are emphasized within a global perspective.