Population Genetic Inference Research Articles

A Continuous Method for Gene Flow

Most modern population genetics inference methods are based on the coalescence framework. Methods that allow estimating parameters of structured populations commonly insert migration events into the genealogies. For these methods the calculation of the coalescence probability density of a genealogy requires a product over all time periods between events. Data sets that contain populations with high rates of gene flow among them require an enormous number of calculations. A new method, transition probability-structured coalescence (TPSC), replaces the discrete migration events with probability statements. Because the speed of calculation is independent of the amount of gene flow, this method allows calculating the coalescence densities efficiently. The current implementation of TPSC uses an approximation simplifying the interaction among lineages. Simulations and coverage comparisons of TPSC vs. MIGRATE show that TPSC allows estimation of high migration rates more precisely, but because of the approximation the estimation of low migration rates is biased. The implementation of TPSC into programs that calculate quantities on phylogenetic tree structures is straightforward, so the TPSC approach will facilitate more general inferences in many computer programs.

Parallel tagged next-generation sequencing on pooled samples - a new approach for population genetics in ecology and conservation.

Next-generation sequencing (NGS) on pooled samples has already been broadly applied in human medical diagnostics and plant and animal breeding. However, thus far it has been only sparingly employed in ecology and conservation, where it may serve as a useful diagnostic tool for rapid assessment of species genetic diversity and structure at the population level. Here we undertake a comprehensive evaluation of the accuracy, practicality and limitations of parallel tagged amplicon NGS on pooled population samples for estimating species population diversity and structure. We obtained 16S and Cyt b data from 20 populations of Leiopelma hochstetteri, a frog species of conservation concern in New Zealand, using two approaches – parallel tagged NGS on pooled population samples and individual Sanger sequenced samples. Data from each approach were then used to estimate two standard population genetic parameters, nucleotide diversity (π) and population differentiation (FST), that enable population genetic inference in a species conservation context. We found a positive correlation between our two approaches for population genetic estimates, showing that the pooled population NGS approach is a reliable, rapid and appropriate method for population genetic inference in an ecological and conservation context. Our experimental design also allowed us to identify both the strengths and weaknesses of the pooled population NGS approach and outline some guidelines and suggestions that might be considered when planning future projects.

RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling

Reduced representation genome-sequencing approaches based on restriction digestion are enabling large-scale marker generation and facilitating genomic studies in a wide range of model and nonmodel systems. However, sampling chromosomes based on restriction digestion may introduce a bias in allele frequency estimation due to polymorphisms in restriction sites. To explore the effects of this nonrandom sampling and its sensitivity to different evolutionary parameters, we developed a coalescent-simulation framework to mimic the biased recovery of chromosomes in restriction-based short-read sequencing experiments (RADseq). We analysed simulated DNA sequence datasets and compared known values from simulations with those that would be estimated using a RADseq approach from the same samples. We compare these 'true' and 'estimated' values of commonly used summary statistics, π, θ(w), Tajima's D and F(ST). We show that loci with missing haplotypes have estimated summary statistic values that can deviate dramatically from true values and are also enriched for particular genealogical histories. These biases are sensitive to nonequilibrium demography, such as bottlenecks and population expansion. In silico digests with 102 completely sequenced Drosophila melanogaster genomes yielded results similar to our findings from coalescent simulations. Though the potential of RADseq for marker discovery and trait mapping in nonmodel systems remains undisputed, our results urge caution when applying this technique to make population genetic inferences.

Using neutral cline decay to estimate contemporary dispersal: a generic tool and its application to a major crop pathogen

Dispersal is a key parameter of adaptation, invasion and persistence. Yet standard population genetics inference methods hardly distinguish it from drift and many species cannot be studied by direct mark-recapture methods. Here, we introduce a method using rates of change in cline shapes for neutral markers to estimate contemporary dispersal. We apply it to the devastating banana pest Mycosphaerella fijiensis, a wind-dispersed fungus for which a secondary contact zone had previously been detected using landscape genetics tools. By tracking the spatio-temporal frequency change of 15 microsatellite markers, we find that σ, the standard deviation of parent–offspring dispersal distances, is 1.2 km/generation1/2. The analysis is further shown robust to a large range of dispersal kernels. We conclude that combining landscape genetics approaches to detect breaks in allelic frequencies with analyses of changes in neutral genetic clines offers a powerful way to obtain ecologically relevant estimates of dispersal in many species.

Coalescent inference for infectious disease: meta-analysis of hepatitis C

Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible–infectious (SI), susceptible–infectious–susceptible (SIS) and susceptible–infectious–recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number (R0) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.

CHROMATOGATE: A TOOL FOR DETECTING BASE MIS-CALLS IN MULTIPLE SEQUENCE ALIGNMENTS BY SEMI-AUTOMATIC CHROMATOGRAM INSPECTION

Automated DNA sequencers generate chromatograms that contain raw sequencing data. They also generate data that translates the chromatograms into molecular sequences of A, C, G, T, or N (undetermined) characters. Since chromatogram translation programs frequently introduce errors, a manual inspection of the generated sequence data is required. As sequence numbers and lengths increase, visual inspection and manual correction of chromatograms and corresponding sequences on a per-peak and per-nucleotide basis becomes an error-prone, time-consuming, and tedious process. Here, we introduce ChromatoGate (CG), an open-source software that accelerates and partially automates the inspection of chromatograms and the detection of sequencing errors for bidirectional sequencing runs. To provide users full control over the error correction process, a fully automated error correction algorithm has not been implemented. Initially, the program scans a given multiple sequence alignment (MSA) for potential sequencing errors, assuming that each polymorphic site in the alignment may be attributed to a sequencing error with a certain probability. The guided MSA assembly procedure in ChromatoGate detects chromatogram peaks of all characters in an alignment that lead to polymorphic sites, given a user-defined threshold. The threshold value represents the sensitivity of the sequencing error detection mechanism. After this pre-filtering, the user only needs to inspect a small number of peaks in every chromatogram to correct sequencing errors. Finally, we show that correcting sequencing errors is important, because population genetic and phylogenetic inferences can be misled by MSAs with uncorrected mis-calls. Our experiments indicate that estimates of population mutation rates can be affected two- to three-fold by uncorrected errors.

Genetic relationships between Oeneis urda and O. mongolica (Nymphalidae: Lepidoptera)

AbstractThe species status of Oeneis urda (Eversmann) and O. mongolica (Oberthür) has been argued based on morphological characters. Reexamination of their major morphological characters has shown a slight differentiation in the two species. Sequences of three mitochondrial genes (COI, ND6, and ND1) and one nuclear region (internal transcribed spacer 2, ITS2) from two O. urda populations (Yangyang and Mt. Hanla) and one O. mongolica population (Uljin) were performed for phylogenetic and population genetic inferences. Sharing of identical sequences in the ND6 gene and ITS2, minimal sequence divergence in the COI and ND1 genes, and phylogenetically undividable sequence types in all mitochondrial genes and ITS2 suggest genetic continuity between the two species. Nevertheless, significant FST estimates (P < 0.05) were found for the COI gene in comparisons between Yangyang (O. urda) and Uljin (O. mongolica), between Yangyang (O. urda) and Mt. Hanla (O. urda), and between Uljin (O. mongolica) and Mt. Hanla (O. urda) populations. These FST estimates, along with other gene‐based analyses collectively suggest isolation of the two species at some point in the past, but incomplete separation between the two species on the mainland (Yangyang and Uljin) and biogeographically forced isolation of the O. urda population on Mt. Hanla collectively appear to complicate species status of these two species that were once further clearly separated.

Genetic Draft, Selective Interference, and Population Genetics of Rapid Adaptation

To learn about the past from a sample of genomic sequences, one needs to understand how evolutionary processes shape genetic diversity. Most population genetics inferences are based on frameworks assuming that adaptive evolution is rare. But if positive selection operates on many loci simultaneously, as has recently been suggested for many species, including animals such as flies, then a different approach is necessary. In this review, I discuss recent progress in characterizing and understanding evolution in rapidly adapting populations, in which random associations of mutations with genetic backgrounds of different fitness, i.e., genetic draft, dominate over genetic drift. As a result, neutral genetic diversity depends weakly on population size but strongly on the rate of adaptation or more generally the variance in fitness. Coalescent processes with multiple mergers, rather than Kingman's coalescent, are appropriate genealogical models for rapidly adapting populations, with important implications for population genetics inference.

Assessing the genetic landscape of a contact zone: the case of European hare in northeastern Greece

The European hare populations of the Balkan Peninsula comprise two divergent phylogenetic lineages with discrete geographical distribution slightly overlapping in the area of northeastern Greece and Bulgaria. Here we elucidate their contact zone, by defining the spatial distributional pattern of the two highly divergent groups, detecting individuals of hybrid origin, and identifying genetic barriers present in the area of their co-existence. Specimens from northeastern Greece were assayed for lineage assignment and population genetic inference based on a 511bp fragment of mitochondrial DNA control region and allelic data from 10 microsatellite loci. Bayesian analyses on original and simulated genotypes were performed allowing for the contact zone delineation. Our results indicate high genetic diversity in both nuclear and mitochondrial DNA, strong population structure and non random spatial distribution of the differentiated gene pools. The information provided by the two types of molecular markers yielded consistent results. This study comprises a fine scale analysis of the contact zone between the two evolutionary lineages of European brown hares in northeastern Greece. Specific questions on the spatial patterns where addressed for the first time. Furthermore, hypotheses regarding the presence of hybrids were also tested. As a result, interpretive power to the diversity patterns observed today in the Balkans was added and previously overlooked aspects of the species biology were highlighted.

Intercoalescence Time Distribution of Incomplete Gene Genealogies in Temporally Varying Populations, and Applications in Population Genetic Inference

Tracing back to a specific time T in the past, the genealogy of a sample of haplotypes may not have reached their common ancestor and may leave m lineages extant. For such an incomplete genealogy truncated at a specific time T in the past, the distribution and expectation of the intercoalescence times conditional on T are derived in an exact form in this paper for populations of deterministically time-varying sizes, specifically, for populations growing exponentially. The derived intercoalescence time distribution can be integrated to the coalescent-based joint allele frequency spectrum (JAFS) theory, and is useful for population genetic inference from large-scale genomic data, without relying on computationally intensive approaches, such as importance sampling and Markov Chain Monte Carlo (MCMC) methods. The inference of several important parameters relying on this derived conditional distribution is demonstrated: quantifying population growth rate and onset time, and estimating the number of ancestral lineages at a specific ancient time. Simulation studies confirm validity of the derivation and statistical efficiency of the methods using the derived intercoalescence time distribution. Two examples of real data are given to show the inference of the population growth rate of a European sample from the NIEHS Environmental Genome Project, and the number of ancient lineages of 31 mitochondrial genomes from Tibetan populations.

Kernel approximate Bayesian computation in population genetic inferences

Approximate Bayesian computation (ABC) is a likelihood-free approach for Bayesian inferences based on a rejection algorithm method that applies a tolerance of dissimilarity between summary statistics from observed and simulated data. Although several improvements to the algorithm have been proposed, none of these improvements avoid the following two sources of approximation: 1) lack of sufficient statistics: sampling is not from the true posterior density given data but from an approximate posterior density given summary statistics; and 2) non-zero tolerance: sampling from the posterior density given summary statistics is achieved only in the limit of zero tolerance. The first source of approximation can be improved by adding a summary statistic, but an increase in the number of summary statistics could introduce additional variance caused by the low acceptance rate. Consequently, many researchers have attempted to develop techniques to choose informative summary statistics. The present study evaluated the utility of a kernel-based ABC method [Fukumizu, K., L. Song and A. Gretton (2010): "Kernel Bayes' rule: Bayesian inference with positive definite kernels," arXiv, 1009.5736 and Fukumizu, K., L. Song and A. Gretton (2011): "Kernel Bayes' rule. Advances in Neural Information Processing Systems 24." In: J. Shawe-Taylor and R. S. Zemel and P. Bartlett and F. Pereira and K. Q. Weinberger, (Eds.), pp. 1549-1557., NIPS 24: 1549-1557] for complex problems that demand many summary statistics. Specifically, kernel ABC was applied to population genetic inference. We demonstrate that, in contrast to conventional ABCs, kernel ABC can incorporate a large number of summary statistics while maintaining high performance of the inference.

The impact of equilibrium assumptions on tests of selection

With the increasing availability and quality of whole genome population data, various methodologies of population genetic inference are being utilized in order to identify and quantify recent population-level selective events. Though there has been a great proliferation of such methodology, the type-I and type-II error rates of many proposed statistics have not been well-described. Moreover, the performance of these statistics is often not evaluated for different biologically relevant scenarios (e.g., population size change, population structure), nor for the effect of differing data sizes (i.e., genomic vs. sub-genomic). The absence of the above information makes it difficult to evaluate newly available statistics relative to one another, and thus, difficult to choose the proper toolset for a given empirical analysis. Thus, we here describe and compare the performance of four widely used tests of selection: SweepFinder, SweeD, OmegaPlus, and iHS. In order to consider the above questions, we utilize simulated data spanning a variety of selection coefficients and beneficial mutation rates. We demonstrate that the LD-based OmegaPlus performs best in terms of power to reject the neutral model under both equilibrium and non-equilibrium conditions—an important result regarding the relative effectiveness of linkage disequilibrium relative to site frequency spectrum based statics. The results presented here ought to serve as a useful guide for future empirical studies, and provides a guide for statistical choice depending on the history of the population under consideration. Moreover, the parameter space investigated and the Type-I and Type-II error rates calculated, represent a natural benchmark by which future statistics may be assessed.

Recipient of the 2012 Molecular Ecology Prize: Craig Moritz

Recipient of the 2012 <i>Molecular Ecology</i> Prize: Craig Moritz

Classical Genetics Meets Next-Generation Sequencing: Uncovering a Genome-Wide Recombination Map in Drosophila melanogaster

Classical Genetics Meets Next-Generation Sequencing: Uncovering a Genome-Wide Recombination Map in Drosophila melanogaster

Contrasting Evolutionary Dynamics and Information Content of the Avian Mitochondrial Control Region and ND2 Gene

Mitochondrial DNA is an important tool for inference of population history in animals. A variety of mitochondrial loci have been sampled for this purpose, but many studies focus on the non-coding D-loop or control region (CR), which in at least some species appears hypermutable. Unfortunately, analyses of this region are sometimes complicated by segmental duplications, as well as by difficulties in sequencing through repeat expansions, driving many researchers to favor single-copy protein-coding or ribosomal RNA genes. Without systematic comparison, it is unclear if, how much, and what sort of information might be lost by focusing on coding regions, or conversely whether such regions might offer significant advantages over the CR. In this study, we compare the information content, both in terms of genealogy and tests of neutral equilibrium, of the mitochondrial CR and protein-coding ND2 gene of the red-winged blackbird (Agelaius phoeniceus) and its close relative the tricolored blackbird (A. tricolor). Both gene regions violate the standard infinite sites assumption central to moment-based population genetic inference, as well as exhibiting considerable among-site rate heterogeneity, obscuring significant departures from neutral equilibrium. Given the ubiquity of rate heterogeneity in mtDNA, use of more sophisticated tests that account for this should be obligatory. The two regions yield quite similar genealogical reconstructions, as well as indicating similar departures from neutral equilibrium assumptions for A. phoeniceus. However, individual Sanger-read-length fragments (∼600 bases) of the CR have significantly higher information content than comparable fragments of ND2, suggesting that limited sampling of the mitochondrial genome should focus on the CR.

Population Genetic Inference from Personal Genome Data: Impact of Ancestry and Admixture on Human Genomic Variation

Full sequencing of individual human genomes has greatly expanded our understanding of human genetic variation and population history. Here, we present a systematic analysis of 50 human genomes from 11 diverse global populations sequenced at high coverage. Our sample includes 12 individuals who have admixed ancestry and who have varying degrees of recent (within the last 500 years) African, Native American, and European ancestry. We found over 21 million single-nucleotide variants that contribute to a 1.75-fold range in nucleotide heterozygosity across diverse human genomes. This heterozygosity ranged from a high of one heterozygous site per kilobase in west African genomes to a low of 0.57 heterozygous sites per kilobase in segments inferred to have diploid Native American ancestry from the genomes of Mexican and Puerto Rican individuals. We show evidence of all three continental ancestries in the genomes of Mexican, Puerto Rican, and African American populations, and the genome-wide statistics are highly consistent across individuals from a population once ancestry proportions have been accounted for. Using a generalized linear model, we identified subtle variations across populations in the proportion of neutral versus deleterious variation and found that genome-wide statistics vary in admixed populations even once ancestry proportions have been factored in. We further infer that multiple periods of gene flow shaped the diversity of admixed populations in the Americas-70% of the European ancestry in today's African Americans dates back to European gene flow happening only 7-8 generations ago.

Late Pleistocene species distribution modelling of North Atlantic intertidal invertebrates

AbstractAimIn this study we test molecular‐based biogeographical hypotheses of Pleistocene persistence and recolonization in a North Atlantic rocky intertidal invertebrate assemblage using species distribution modelling.LocationNorth Atlantic coastlines.MethodsWe develop a novel application of species distribution models for intertidal taxa by using both oceanographic and terrestrial environmental variables recently made available at biologically relevant pixel resolutions. We use these model‐based distribution estimates to test alternative hypotheses of late Pleistocene history across six amphi‐Atlantic taxa whose hypothesized Pleistocene distributions are constructed from previous population genetic inferences.ResultsSpecies distribution models correctly estimate all six current amphi‐Atlantic distributions on both sides of the Atlantic. Models hindcasted to the Last Glacial Maximum estimate trans‐Atlantic distributional persistence in three of six taxa, and conversely indicate no suitable habitat on the North American coastline in the other three taxa. These results are consistent with the histories inferred from population genetic data for five of six of the taxa, the exception being Nucella lapillus, whose hindcasted trans‐Atlantic persistence at the Last Glacial Maximum contrasts with the mitochondrial DNA‐based estimates of post‐glacial expansion from Europe to North America.Main conclusionsMarine and terrestrial distribution models are broadly concordant with known contemporary distributions and fail to reject five of six historical distributional hypotheses based on population genetic inferences. This demonstrated utility suggests that this technique could be expanded to examine how Pleistocene and contemporary climate change might play a role in intertidal species distributions across the entire community. Future application and development of these marine and terrestrial distribution models will strengthen inferences regarding the processes generating intertidal community assembly and population divergence, and may assist in conservation planning and the design of marine reserves.

Population Demographic History Can Cause the Appearance of Recombination Hotspots

Although the prevailing view among geneticists suggests that recombination hotspots exist ubiquitously across the human genome, there is only limited experimental evidence from a few genomic regions to support the generality of this claim. A small number of true recombination hotspots are well supported experimentally, but the vast majority of hotspots have been identified on the basis of population genetic inferences from the patterns of linkage disequilibrium (LD) seen in the human population. These inferences are made assuming a particular model of human history, and one of the assumptions of that model is that the effective population size of humans has remained constant throughout our history. Our results show that relaxation of the constant population size assumption can create LD and variation patterns that are qualitatively and quantitatively similar to human populations without any need to invoke localized hotspots of recombination. In other words, apparent recombination hotspots could be an artifact of variable population size over time. Several lines of evidence suggest that the vast majority of hotspots identified on the basis of LD information are unlikely to have elevated recombination rates.

Stopping-Time Resampling and Population Genetic Inference under Coalescent Models

To extract full information from samples of DNA sequence data, it is necessary to use sophisticated model-based techniques such as importance sampling under the coalescent. However, these are limited in the size of datasets they can handle efficiently. Chen and Liu (2000) introduced the idea of stopping-time resampling and showed that it can dramatically improve the efficiency of importance sampling methods under a finite-alleles coalescent model. In this paper, a new framework is developed for designing stopping-time resampling schemes under more general models. It is implemented on data both from infinite sites and stepwise models of mutation, and extended to incorporate crossover recombination. A simulation study shows that this new framework offers a substantial improvement in the accuracy of likelihood estimation over a range of parameters, while a direct application of the scheme of Chen and Liu (2000) can actually diminish the estimate. The method imposes no additional computational burden and is robust to the choice of parameters.

Assessing the accuracy and power of population genetic inference from low-pass next-generation sequencing data.

Next-generation sequencing (NGS) technologies have made it possible to address population genetic questions in almost any system, but high error rates associated with such data can introduce significant biases into downstream analyses, necessitating careful experimental design and interpretation in studies based on short-read sequencing. Exploration of population genetic analyses based on NGS has revealed some of the potential biases, but previous work has emphasized parameters relevant to human population genetics and further examination of parameters relevant to other systems is necessary, including situations where sample sizes are small and genetic variation is high. To assess experimental power to address several principal objectives of population genetic studies under these conditions, we simulated population samples under selective sweep, population growth, and population subdivision models and tested the power to accurately infer population genetic parameters from sequence polymorphism data obtained through simulated 4×, 8×, and 15× read depth sequence data. We found that estimates of population genetic differentiation and population growth parameters were systematically biased when inference was based on 4× sequencing, but biases were markedly reduced at even 8× read depth. We also found that the power to identify footprints of positive selection depends on an interaction between read depth and the strength of selection, with strong selection being recovered consistently at all read depths, but weak selection requiring deeper read depths for reliable detection. Although we have explored only a small subset of the many possible experimental designs and population genetic models, using only one SNP-calling approach, our results reveal some general patterns and provide some assessment of what biases could be expected under similar experimental structures.