Applications In Population Genetics Research Articles

Microsatellite evolutionary rate and pattern in Schistocerca gregaria inferred from direct observation of germline mutations.

Unravelling variation among taxonomic orders regarding the rate of evolution in microsatellites is crucial for evolutionary biology and population genetics research. The mean mutation rate of microsatellites tends to be lower in arthropods than in vertebrates, but data are scarce and mostly concern accumulation of mutations in model species. Based on parent-offspring segregations and a hierarchical Bayesian model, the mean rate of mutation in the orthopteran insect Schistocerca gregaria was estimated at 2.1e(-4) per generation per untranscribed dinucleotide locus. This is close to vertebrate estimates and one order of magnitude higher than estimates from species of other arthropod orders, such as Drosophila melanogaster and Daphnia pulex. We also found evidence of a directional bias towards expansions even for long alleles and exceptionally large ranges of allele sizes. Finally, at transcribed microsatellites, the mean rate of mutation was half the rate found at untranscribed loci and the mutational model deviated from that usually considered, with most mutations involving multistep changes that avoid disrupting the reading frame. Our direct estimates of mutation rate were discussed in the light of peculiar biological and genomic features of S. gregaria, including specificities in mismatch repair and the dependence of its activity to allele length. Shedding new light on the mutational dynamics of grasshopper microsatellites is of critical importance for a number of research fields. As an illustration, we showed how our findings improve microsatellite application in population genetics, by obtaining a more precise estimation of S. gregaria effective population size from a published data set based on the same microsatellites.

Probability and Manipulation: Evolution and Simulation in Applied Population Genetics

I define a concept of causal probability and apply it to questions about the role of probability in evolutionary processes. Causal probability is defined in terms of manipulation of patterns in empirical outcomes by manipulating properties that realize objective probabilities. The concept of causal probability allows us see how probabilities characterized by different interpretations of probability can share a similar causal character, and does so in such way as to allow new inferences about relationships between probabilities realized in different chance setups. I clarify relations between probabilities and properties defined in terms of them, and argue that certain widespread uses of computer simulations in evolutionary biology show that many probabilities relevant to evolutionary outcomes are causal probabilities. This supports the claim that higher-level properties such as biological fitness and processes such as natural selection are causal properties and processes, contrary to what some authors have argued.

SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates and effective population sizes.

In the Wright-Fisher diffusion, the transition density function describes the time evolution of the population-wide frequency of an allele. This function has several practical applications in population genetics and computing it for biologically realistic scenarios with selection and demography is an important problem. We develop an efficient method for finding a spectral representation of the transition density function for a general model where the effective population size, selection coefficients and mutation parameters vary over time in a piecewise constant manner. The method, called SpectralTDF, is available at https://sourceforge.net/projects/spectraltdf/ yss@berkeley.edu Supplementary data are available at Bioinformatics online.

The Paternal Landscape along the Bight of Benin - Testing Regional Representativeness of West-African Population Samples Using Y-Chromosomal Markers.

Patterns of genetic variation in human populations across the African continent are still not well studied in comparison with Eurasia and America, despite the high genetic and cultural diversity among African populations. In population and forensic genetic studies a single sample is often used to represent a complete African region. In such a scenario, inappropriate sampling strategies and/or the use of local, isolated populations may bias interpretations and pose questions of representativeness at a macrogeographic-scale. The non-recombining region of the Y-chromosome (NRY) has great potential to reveal the regional representation of a sample due to its powerful phylogeographic information content. An area poorly characterized for Y-chromosomal data is the West-African region along the Bight of Benin, despite its important history in the trans-Atlantic slave trade and its large number of ethnic groups, languages and lifestyles. In this study, Y-chromosomal haplotypes from four Beninese populations were determined and a global meta-analysis with available Y-SNP and Y-STR data from populations along the Bight of Benin and surrounding areas was performed. A thorough methodology was developed allowing comparison of population samples using Y-chromosomal lineage data based on different Y-SNP panels and phylogenies. Geographic proximity turned out to be the best predictor of genetic affinity between populations along the Bight of Benin. Nevertheless, based on Y-chromosomal data from the literature two population samples differed strongly from others from the same or neighbouring areas and are not regionally representative within large-scale studies. Furthermore, the analysis of the HapMap sample YRI of a Yoruban population from South-western Nigeria based on Y-SNPs and Y-STR data showed for the first time its regional representativeness, a result which is important for standard population and forensic genetic applications using the YRI sample. Therefore, the uniquely and powerful geographical information carried by the Y-chromosome makes it an important locus to test the representativeness of a certain sample even in the genomic era, especially in poorly investigated areas like Africa.

Identification and Evaluation of 21 Novel Microsatellite Markers from the Autumnal Moth (Epirrita autumnata) (Lepidoptera: Geometridae).

The autumnal moth (Epirrita autumnata) is a cyclically outbreaking forest Lepidoptera with circumpolar distribution and substantial impact on Northern ecosystems. We have isolated 21 microsatellites from the species to facilitate population genetic studies of population cycles, outbreaks, and crashes. First, PCR primers and PCR conditions were developed to amplify 19 trinucleotide loci and two tetranucleotide loci in six multiplex PCR approaches and then analyzed for species specificity, sensitivity and precision. Twelve of the loci showed simple tandem repeat array structures while nine loci showed imperfect repeat structures, and repeat numbers varied in our material between six and 15. The application in population genetics for all the 21 microsatellites were further validated in 48 autumnal moths sampled from Northern Norway, and allelic variation was detected in 19 loci. The detected numbers of alleles per locus ranged from two to 13, and the observed and expected heterozygosities varied from 0.04 to 0.69 and 0.04 to 0.79, respectively. Evidence for linkage disequilibrium was found for six loci as well as indication of one null allele. We find that these novel microsatellites and their multiplex-PCR assays are suitable for further research on fine- and large-scale population-genetic studies of Epirrita autumnata.

Statistical Physics Methods Provide the Exact Solution to a Long-Standing Problem of Genetics.

Analytic and computational methods developed within statistical physics have found applications in numerous disciplines. In this Letter, we use such methods to solve a long-standing problem in statistical genetics. The problem, posed by Haldane and Waddington [Genetics 16, 357 (1931)], concerns so-called recombinant inbred lines (RILs) produced by repeated inbreeding. Haldane and Waddington derived the probabilities of RILs when considering two and three genes but the case of four or more genes has remained elusive. Our solution uses two probabilistic frameworks relatively unknown outside of physics: Glauber's formula and self-consistent equations of the Schwinger-Dyson type. Surprisingly, this combination of statistical formalisms unveils the exact probabilities of RILs for any number of genes. Extensions of the framework may have applications in population genetics and beyond.

Genome-wide patterns and properties of de novo mutations in humans.

Mutations create variation in the population, fuel evolution, and cause genetic diseases. Current knowledge about de novo mutations is incomplete and mostly indirect 1–10. Here, we analyze 11,020 de novo mutations from whole-genomes of 250 families. We show that de novo mutations in offspring of older fathers are not only more numerous 11–13 but also occur more frequently in early-replicating, genic regions. Functional regions exhibit higher mutation rates due to CpG dinucleotides and reveal signatures of transcription-coupled repair, while mutation clusters with a unique signature point to a novel mutational mechanism. Mutation and recombination rates independently associate with nucleotide diversity, and regional variation in human-chimpanzee divergence is only partly explained by mutation rate heterogeneity. Finally, we provide a genome-wide mutation rate map for medical and population genetics applications. Our results reveal novel insights and refine long-standing hypotheses about human mutagenesis.

Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness.

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.

A new species for sea cucumber ranching and aquaculture: Breeding and rearing of Holothuria arguinensis

Abstract Multi-species sea cucumber fisheries, including the species Holothuria arguinensis , have developed recently in the North-eastern Atlantic and Mediterranean Sea. In this context, the current study evaluated the potential use of H. arguinensis in aquaculture and to enhance natural populations, describing the embryonic and larval development for a better understanding and application in marine conservation and population genetics. Wild broodstock collected in Ria Formosa (37°0′33.92″N, 7°59′44.99″W; Faro, South Portugal) was induced to spawn using established methodology of thermal stimulation from June to August. The larval development under controlled conditions exhibited the classic five stages described for most aspidochirote holothurians, reaching the juvenile stage after 18 days post-spawning. Low mortality was registered during the pelagic larval stage, however high mortality (85–95%) was registered at doliolaria and juvenile stages. Results indicate that H. arguinensis is suited to hatchery rearing for aquaculture and enhancement of natural populations. Nevertheless, as this is the first European sea cucumber species reproduced in a hatchery, further research is required to improve the survival of post-settlement stages and optimize the rearing of juveniles.

Optimality and stability of symmetric evolutionary games with applications in genetic selection.

Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

Novel polymorphic microsatellite markers for Bellamya and their application in population genetics of three species.

Bellamya is a widely distributed freshwater snail genus in China; however, its genetic diversity is completely unknown. Sixty-five novel microsatellite loci were isolated and characterized from a microsatellite-enriched library of Bellamya aeruginosa genomic DNA. Most of the 65 loci were successfully amplified. We found high polymorphic information content values for these loci, ranging from 0.235 to 0.892. There were 3 to 12 alleles per locus, and the HE and HO varied from 0.425 to 0.953 and 0.026 to 1.000, respectively. Fifteen loci deviated significantly from Hardy-Weinberg equilibrium after Bonferroni's correction. All 65 SSR markers were tested in an additional five Bellamya species, and 96.9% of the 325 locus/taxon combinations tested resulted in cross-species amplification. Seven polymorphic microsatellite markers were randomly selected for comparison among nine populations of three species. All populations had moderate to high genetic diversity. In genetic distance-based cluster analysis, the populations of B. aeruginosa and B. dispiralis formed species-based clusters, whereas populations of B. angularia did not. The three examined Bellamya species could be differentiated using SSR markers. These microsatellite loci should be useful for genetic diversity analysis, analysis of phylogenetic relationship, and species delimitation of Bellamya.

Isolation and characterization of new microsatellite markers for application in population genetic studies of Caiman latirostris and related species

Wild populations ofCaiman latirostrisare subject to sustainable use programs in Argentina, becoming a species with important impact in the regional economy, based in their skin and meat. Genetic studies are fundamental to acquire information on important parameters for conservation and management, which may be obtained from analysis of molecular markers. Some microsatellites have been previously isolated in this species, but due to some difficulties in using them, we obtained new ones using Next Generation Sequencing approach. This study reports eight new microsatellites forC. latirostrisand tests their utility in a related species,Caiman yacare, with successful application in population genetics and mating systems studies. In addition, we shared data about a novel and fast bioinformatics tool to find microsatellites and to design their corresponding primers.

Microsatellite markers from the 'South American fruit fly' Anastrepha fraterculus: a valuable tool for population genetic analysis and SIT applications.

BackgroundAnastrepha fraterculus Wiedemann is a horticultural pest which causes significant economic losses in the fruit-producing areas of the American continent and limits the access of products to international markets. The use of environmentally friendly control strategies against this pest is constrained due to the limited knowledge of its population structure.ResultsWe developed microsatellite markers for A. fraterculus from four genomic libraries, which were enriched in CA, CAA, GA and CAT microsatellite motifs. Fifty microsatellite regions were evaluated and 14 loci were selected for population genetics studies. Genotypes of 122 individuals sampled from four A. fraterculus populations were analyzed. The level of polymorphism ranged from three to 13 alleles per locus and the mean expected heterozygosity ranged from 0.60 to 0.64. Comparison between allelic and genotypic frequencies showed significant differences among all pairs of populations.ConclusionsThis novel set of microsatellite markers provides valuable information for the description of genetic variability and population structure of wild populations and laboratory strains of A. fraterculus. This information will be used to identify and characterize candidate strains suitable to implement effective pest control strategies and might represent a first step towards having a more comprehensive knowledge about the genetics of this pest.

FASconCAT-G: extensive functions for multiple sequence alignment preparations concerning phylogenetic studies.

BackgroundPhylogenetic and population genetic studies often deal with multiple sequence alignments that require manipulation or processing steps such as sequence concatenation, sequence renaming, sequence translation or consensus sequence generation. In recent years phylogenetic data sets have expanded from single genes to genome wide markers comprising hundreds to thousands of loci. Processing of these large phylogenomic data sets is impracticable without using automated process pipelines. Currently no stand-alone or pipeline compatible program exists that offers a broad range of manipulation and processing steps for multiple sequence alignments in a single process run.ResultsHere we present FASconCAT-G, a system independent editor, which offers various processing options for multiple sequence alignments. The software provides a wide range of possibilities to edit and concatenate multiple nucleotide, amino acid, and structure sequence alignment files for phylogenetic and population genetic purposes. The main options include sequence renaming, file format conversion, sequence translation between nucleotide and amino acid states, consensus generation of specific sequence blocks, sequence concatenation, model selection of amino acid replacement with ProtTest, two types of RY coding as well as site exclusions and extraction of parsimony informative sites. Convieniently, most options can be invoked in combination and performed during a single process run. Additionally, FASconCAT-G prints useful information regarding alignment characteristics and editing processes such as base compositions of single in- and outfiles, sequence areas in a concatenated supermatrix, as well as paired stem and loop regions in secondary structure sequence strings.ConclusionsFASconCAT-G is a command-line driven Perl program that delivers computationally fast and user-friendly processing of multiple sequence alignments for phylogenetic and population genetic applications and is well suited for incorporation into analysis pipelines.Electronic supplementary materialThe online version of this article (doi:10.1186/s12983-014-0081-x) contains supplementary material, which is available to authorized users.

Tracing the sources of human salmonellosis: A multi-model comparison of phenotyping and genotyping methods

Salmonella source attribution is usually performed using frequency-matched models, such as the (modified) Dutch and Hald models, based on phenotyping data, i.e. serotyping, phage typing, and antimicrobial resistance profiling. However, for practical and economic reasons, genotyping methods such as Multi-locus Variable Number of Tandem Repeats Analysis (MLVA) are gradually replacing traditional phenotyping of salmonellas beyond the serovar level. As MLVA-based source attribution of human salmonellosis using frequency-matched models is problematic due to the high variability of the genetic targets investigated, other models need to be explored. Using a comprehensive data set from the Netherlands in 2005–2013, this study aimed at attributing sporadic and domestic cases of Salmonella Typhimurium/4,[5],12:i:- and Salmonella Enteritidis to four putative food-producing animal sources (pigs, cattle, broilers, and layers/eggs) using the modified Dutch and Hald models (based on sero/phage typing data) in comparison with a widely applied population genetics model – the asymmetric island model (AIM) – supplied with MLVA data. This allowed us to compare model outcomes and to corroborate whether MLVA-based Salmonella source attribution using the AIM is able to provide sound, comparable results. All three models provided very similar results, confirming once more that most S. Typhimurium/4,[5],12:i:- and S. Enteritidis cases are attributable to pigs and layers/eggs, respectively. We concluded that MLVA-based source attribution using the AIM is a feasible option, at least for S. Typhimurium/4,[5],12:i:- and S. Enteritidis. Enough information seems to be contained in the MLVA profiles to trace the sources of human salmonellosis even in presence of imperfect temporal overlap between human and source isolates. Besides Salmonella, the AIM might also be applicable to other pathogens that do not always comply to clonal models. This would add further value to current surveillance activities by performing source attribution using genotyping data that are being collected in a standardized fashion internationally.

Characterization of microsatellite markers in silver pomfret, Pampus argenteus (Perciformes: Stromateidae) through cross-species amplification and population genetic applications

Pampus argenteus (Euphrasen 1788) is one of the commercially important marine food fish in India, having high value and demand in both domestic and export market. Pomfrets in Indian waters showed a steady decline in catch due to capture of undersized, juvenile fishes. Baseline information on the genetic stock structure is important for conservation and sustainable harvest of this species. Fish stock structure and genetic variation can be evaluated/identified through microsatellite molecular markers. An alternative for finding polymorphic microsatellites is cross-species amplification between genetically closely related species.

FastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets

Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here, we develop efficient algorithms for approximate inference of the model underlying the STRUCTURE program using a variational Bayesian framework. Variational methods pose the problem of computing relevant posterior distributions as an optimization problem, allowing us to build on recent advances in optimization theory to develop fast inference tools. In addition, we propose useful heuristic scores to identify the number of populations represented in a data set and a new hierarchical prior to detect weak population structure in the data. We test the variational algorithms on simulated data and illustrate using genotype data from the CEPH–Human Genome Diversity Panel. The variational algorithms are almost two orders of magnitude faster than STRUCTURE and achieve accuracies comparable to those of ADMIXTURE. Furthermore, our results show that the heuristic scores for choosing model complexity provide a reasonable range of values for the number of populations represented in the data, with minimal bias toward detecting structure when it is very weak. Our algorithm, fastSTRUCTURE, is freely available online at http://pritchardlab.stanford.edu/structure.html.

PopGenome: an efficient Swiss army knife for population genomic analyses in R.

Although many computer programs can perform population genetics calculations, they are typically limited in the analyses and data input formats they offer; few applications can process the large data sets produced by whole-genome resequencing projects. Furthermore, there is no coherent framework for the easy integration of new statistics into existing pipelines, hindering the development and application of new population genetics and genomics approaches. Here, we present PopGenome, a population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. It reads DNA alignments and single-nucleotide polymorphism (SNP) data sets in most common formats, including those used by the HapMap, 1000 human genomes, and 1001 Arabidopsis genomes projects. PopGenome also reads associated annotation files in GFF format, enabling users to easily define regions or classify SNPs based on their annotation; all analyses can also be applied to sliding windows. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson’s MS and Ewing’s MSMS programs to assess statistical significance based on coalescent simulations. PopGenome’s integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics. Developers can easily incorporate new analyses methods into the PopGenome framework. PopGenome and R are freely available from CRAN (http://cran.r-project.org/) for all major operating systems under the GNU General Public License.

Application of microsatellite markers in conservation genetics and fisheries management: recent advances in population structure analysis and conservation strategies.

Microsatellites are the most popular and versatile genetic marker with myriads of applications in population genetics, conservation biology, and evolutionary biology. These are the arrays of DNA sequences, consisting of tandemly repeating mono-, di-, tri-, and tetranucleotide units, which are distributed throughout the genomes of most eukaryotic species. Microsatellites are codominant in nature, highly polymorphic, easily typed, and Mendelian inherited, all properties which make them very suitable for the study of population structure and pedigree analysis and capable of detecting differences among closely related species. PCR for microsatellites can be automated for identifying simple sequence repeat polymorphism. Small amount of blood samples or alcohol preserved tissue is adequate for analyzing them. Most of the microsatellites are noncoding, and therefore variations are independent of natural selection. These properties make microsatellites ideal genetic markers for conservation genetics and fisheries management. This review addresses the applications of microsatellite markers in conservation genetics and recent advances in population structure analysis in the context of fisheries management.

Genetic introgression of cultured rainbow trout in the Mexican native trout complex

The Mexican native trout complex is the group of salmonids that naturally has the southernmost distribution in the world. Despite its unique status and evidence of more than 13 distinct lineages, there are only two described species (Oncorhynchus mykiss nelsoni and O. chrysogaster). These fishes are threatened by environmental and anthropogenic factors, most notably the introduction of the exotic species O. mykiss (rainbow trout) for aquaculture. Here we applied population genetics analysis in 1,017 wild and cultured trout to understand the extent of genetic introgression of rainbow trout in Mexican native trout. Present results indicate a high degree of introgression and genetic admixture among introduced rainbow trout and some populations of Mexican native trout, exposing them to loss of genetic diversity. Thus, introduction of exotic trout for aquaculture purposes must be strictly regulated or avoided and we advise the use of native trout for aquaculture.