Inferring population parameters from single-feature polymorphism data

This article is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single-feature polymorphisms called in the sample. For the recombination parameter, a two-feature sampling distribution is derived in a way analogous to that for the two-locus sampling distribution with SNP data. The approximate-likelihood approach using the two-feature sampling distribution is examined and found to work well. A coalescent simulation study is used to investigate the accuracy and robustness of our method. Our approach allows the utilization of single-feature polymorphism data for inference in population genetics.     

Inferring population history from microsatellite and enzyme data in serially introduced cane toads, Bufo marinus.

Much progress has been made on inferring population history from molecular data. However, complex demographic scenarios have been considered rarely or have proved intractable. The serial introduction of the South-Central American cane toad Bufo marinus in various Caribbean and Pacific islands involves four major phases: a possible genetic admixture during the first introduction, a bottleneck associated with founding, a transitory population boom, and finally, a demographic stabilization. A large amount of historical and demographic information is available for those introductions and can be combined profitably with molecular data. We used a Bayesian approach to combine this information with microsatellite (10 loci) and enzyme (22 loci) data and used a rejection algorithm to simultaneously estimate the demographic parameters describing the four major phases of the introduction history. The general historical trends supported by microsatellites and enzymes were similar. However, there was a stronger support for a larger bottleneck at introductions for microsatellites than enzymes and for a more balanced genetic admixture for enzymes than for microsatellites. Very little information was obtained from either marker about the transitory population boom observed after each introduction. Possible explanations for differences in resolution of demographic events and discrepancies between results obtained with microsatellites and enzymes were explored. Limits of our model and method for the analysis of nonequilibrium populations were discussed.     

Human population expansion and microsatellite variation

Polymorphisms at di-, tri-, and tetranucleotide microsatellite loci have been analyzed in 14 worldwide populations. A statistical index of population expansion, denoted S(k), is introduced to detect historical changes in population size using the variation at the microsatellites. The index takes the value 0 at equilibrium with constant population size and is positive or negative according to whether the population is expanding or contracting, respectively. The use of S(k) requires estimation of properties of the mutation distribution for which we use both family data of Dib et al. for dinucleotide loci and our population data on tri- and tetranucleotide loci. Statistical estimates of the expansion index, as well as their confidence intervals from bootstrap resampling, are provided. In addition, a dynamical analysis of S(k) is presented under various assumptions on population growth or decline. The studied populations are classified as having high, intermediate, or low values of S(k) and genetic variation, and we use these to interpret the data in terms of possible population dynamics. Observed values of S(k) for samples of di-, tri-, and tetranucleotide data are compatible with population expansion earlier than 60,000 years ago in Africa, Asia, and Europe if the initial population size before the expansion was on the order of 500. Larger initial population sizes force the lower bound for the time since expansion to be much earlier. We find it unlikely that bottlenecks occurred in Central African, East Asian, or European populations, and the estimated expansion times are rather similar for all of these populations. This analysis presented here suggests that modern human populations departed from Africa long before they began to expand in size. Subsequently, the major groups (the African, East Asian, and European groups) started to grow at approximately same time. Populations of South America and Oceania show almost no growth. The Mbuti population from Zaire appears to have experienced a bottleneck during its expansion.     

Inferring the population structure and demography of Drosophila ananassae from multilocus data

Inferring the origin, population structure, and demographic history of a species is a major objective of population genetics. Although many organisms have been analyzed, the genetic structures of subdivided populations are not well understood. Here we analyze Drosophila ananassae, a highly substructured, cosmopolitan, and human-commensal species distributed in the tropical, subtropical, and mildly temperate regions of the world. We adopt a multilocus approach (with 10 neutral loci) using 16 population samples covering almost the entire species range (Asia, Australia, and America). Analyzed with our recently developed Bayesian method, 5 populations in Southeast Asia are found to be central, while the other 11 are peripheral. These 5 central populations were sampled from localities that belonged to a single landmass ("Sundaland") during the late Pleistocene ( approximately 18,000 years ago), when sea level was approximately 120 m below the present level. The inferred migration routes of D. ananassae out of Sundaland seem to parallel those of humans in this region. Strong evidence for a population size expansion is seen particularly in the ancestral populations.     

Detecting population expansion and decline using microsatellites

This article considers a demographic model where a population varies in size either linearly or exponentially. The genealogical history of microsatellite data sampled from this population can be described using coalescent theory. A method is presented whereby the posterior probability distribution of the genealogical and demographic parameters can be estimated using Markov chain Monte Carlo simulations. The likelihood surface for the demographic parameters is complicated and its general features are described. The method is then applied to published microsatellite data from two populations. Data from the northern hairy-nosed wombat show strong evidence of decline. Data from European humans show weak evidence of expansion.     

Inferring a population structure for Staphylococcus epidermidis from multilocus sequence typing data

Despite its importance as a human pathogen, information on population structure and global epidemiology of Staphylococcus epidermidis is scarce and the relative importance of the mechanisms contributing to clonal diversification is unknown. In this study, we addressed these issues by analyzing a representative collection of S. epidermidis isolates from diverse geographic and clinical origins using multilocus sequence typing (MLST). Additionally, we characterized the mobile element (SCCmec) carrying the genetic determinant of methicillin resistance. The 217 S. epidermidis isolates from our collection were split by MLST into 74 types, suggesting a high level of genetic diversity. Analysis of MLST data using the eBURST algorithm revealed the existence of nine epidemic clonal lineages that were disseminated worldwide. One single clonal lineage (clonal complex 2) comprised 74% of the isolates, whereas the remaining isolates were clustered into 8 minor clonal lineages and 13 singletons. According to our evolutionary model, SCCmec was acquired at least 56 times by S. epidermidis. Although geographic dissemination of S. epidermidis strains and the value of the index of association between the alleles, 0.2898 (P < 0.05), support the clonality of S. epidermidis species, examination of the sequence changes at MLST loci during clonal diversification showed that recombination gives rise to new alleles approximately twice as frequently as point mutations. We suggest that S. epidermidis has a population with an epidemic structure, in which nine clones have emerged upon a recombining background and evolved quickly through frequent transfer of genetic mobile elements, including SCCmec.     

Estimating genetic correlations based on phenotypic data: a simulation-based method

Knowledge of genetic correlations is essential to understand the joint evolution of traits through correlated responses to selection, a difficult and seldom, very precise task even with easy-to-breed species. Here, a simulation-based method to estimate genetic correlations and genetic covariances that relies only on phenotypic measurements is proposed. The method does not require any degree of relatedness in the sampled individuals. Extensive numerical results suggest that the propose method may provide relatively efficient estimates regardless of sample sizes and contributions from common environmental effects.     

Inferring population and metapopulation dynamics of Liparis loeselii from single-census and inventory data

To conserve endangered species, information is needed on (meta)population responses to habitat quality and management. As possibilities for long-term studies are generally limited, it is important to obtain as much information as possible in a single field season.We obtained such single-census data for the orchid Liparis loeselii, a European Habitat Directive species. Stage structures of 15 Dutch dune and fen populations were related to vegetation structure, environmental indicators, and management. Botanical inventory records from 1930 to 2003 were used to infer population life spans.Cluster analysis did not reveal successional stage structure types. Dense populations with high recruitment mainly occurred in open, young-successional vegetation with high soil pH. High soil humidity and acidification negatively affected orchid densities. Early mowing was preferable over late mowing in dune slacks, because the latter reduced juvenile densities. The predominant population life span was three to eight years, and similar for dune slacks and fens. Longer life spans were occasionally observed at mown sites with influx of base-rich water.Our results suggest high metapopulation dynamics. Long-term metapopulation viability requires the formation of new habitat by dune slack formation in dunes and peat removal in fens. Population persistence can be prolonged to some extent by mowing, extensive grazing, or sod removal if natural habitat formation is impossible. Our study demonstrates that useful information on (meta)population ecology and viability can be obtained in a single field season.     

TETRASAT: a program for the population analysis of allotetraploid microsatellite data

Microsatellite markers are quite popular due to their degree of polymorphism and efficiency; however, the utility of such markers for analysing allotetraploid species is often hampered by an inability to determine allele copy number for partial heterozygotes. tetrasat is a program that uses an iterative substitution process to account for all probable combinations of allele copy numbers in populations with partial heterozygote samples. The program subsequently calculates allele frequencies, and mean Hardy–Weinberg expected heterozygosity (H_E), Shannon–Weiner Diversity Index (H′) and Nei's measure of population differentiation (G_ST) are reported for each locus and population. Of equal importance is the calculation of statistical variability generated by the missing data and allele substitution process, which allows for assessment of the strength of conclusions drawn from the statistics.     

Inferring sex-biased dispersal from population genetic tools: a review

Sex-biased dispersal, where individuals of one sex stay or return to their natal site (or group) to breed while individuals of the other sex are prone to disperse, is a wide-spread pattern in vertebrate organisms. In general, mammals exhibit male-biased dispersal whereas birds exhibit female-bias. Dispersal estimates are often difficult to obtain from direct field observations. Here we describe different methods for inferring sex-specific dispersal using population genetic tools and discuss the problems they can raise. We distinguish two types of methods: those based on bi-parental markers (eg comparison of male/female relatedness, F(st) and assignment probabilities) and those relying on the comparison between markers with different modes of inheritance (eg mtDNA markers and microsatellites). Finally, we discuss statistical problems that are encountered with these different methods (eg pseudoreplication, problems due to the comparison of distinct markers). While the genetic methods to detect sex-biased dispersal are now relatively well developed, their interpretation can prove problematic due to the confounding effects of factors such as the mating system of the species. Moreover, the relative power of these methods is not well known and requires further investigation.     

Inferring the dynamics of a spatial epidemic from time-series data

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host-pathogen systems is discussed.     

Inferring the mode of speciation from genomic data: a study of the great apes

The strictly allopatric model of speciation makes definable predictions on the pattern of divergence, one of which is the uniformity in the divergence time across genomic regions. Using 345 coding and 143 intergenic sequences from the African great apes, we were able to reject the null hypothesis that the divergence time in the coding sequences (CDSs) and intergenic sequences (IGSs) is the same between human and chimpanzee. The conclusion is further supported by the analysis of whole-genome sequences between these species. The difference suggests a prolonged period of genetic exchange during the formation of these two species. Because the analysis should be generally applicable, collecting DNA sequence data from many genomic regions between closely related species should help to settle the debate over the prevalence of the allopatric mode of speciation.     

Inferences on pathogenic fungus population structures from microsatellite data: new insights from spatial genetics approaches

Landscape genetics, which combines population genetics, landscape ecology and spatial statistics, has emerged recently as a new discipline that can be used to assess how landscape features or environmental variables can influence gene flow and spatial genetic variation. We applied this approach to the invasive plant pathogenic fungus Mycosphaerella fijiensis, which causes black leaf streak disease of banana. Around 880 isolates were sampled within a 50 × 50 km area located in a fragmented banana production zone in Cameroon that includes several potential physical barriers to gene flow. Two clustering algorithms and a new F(ST) -based procedure were applied to define the number of genetic entities and their spatial domain without a priori assumptions. Two populations were clearly delineated, and the genetic discontinuity appeared sharp but asymmetric. Interestingly, no landscape features matched this genetic discontinuity, and no isolation by distance (IBD) was found within populations. Our results suggest that the genetic structure observed in this production area reflects the recent history of M. fijiensis expansion in Cameroon rather than resulting from contemporary gene flow. Finally, we discuss the influence of the suspected high effective population size for such an organism on (i) the absence of an IBD signal, (ii) the characterization of contemporary gene-flow events through assignation methods of analysis and (iii) the evolution of the genetic discontinuity detected in this study.     

Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data

We present an integrated method that uses extended time-lapse automated imaging to quantify the dynamics of cell proliferation. Cell counts are fit with a quiescence-growth model that estimates rates of cell division, entry into quiescence and death. The model is constrained with rates extracted experimentally from the behavior of tracked single cells over time. We visualize the output of the analysis in fractional proliferation graphs, which deconvolve dynamic proliferative responses to perturbations into the relative contributions of dividing, quiescent (nondividing) and dead cells. The method reveals that the response of 'oncogene-addicted' human cancer cells to tyrosine kinase inhibitors is a composite of altered rates of division, death and entry into quiescence, a finding that challenges the notion that such cells simply die in response to oncogene-targeted therapy.     

A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates

Longitudinal data often contain missing observations and error-prone covariates. Extensive attention has been directed to analysis methods to adjust for the bias induced by missing observations. There is relatively little work on investigating the effects of covariate measurement error on estimation of the response parameters, especially on simultaneously accounting for the biases induced by both missing values and mismeasured covariates. It is not clear what the impact of ignoring measurement error is when analyzing longitudinal data with both missing observations and error-prone covariates. In this article, we study the effects of covariate measurement error on estimation of the response parameters for longitudinal studies. We develop an inference method that adjusts for the biases induced by measurement error as well as by missingness. The proposed method does not require the full specification of the distribution of the response vector but only requires modeling its mean and variance structures. Furthermore, the proposed method employs the so-called functional modeling strategy to handle the covariate process, with the distribution of covariates left unspecified. These features, plus the simplicity of implementation, make the proposed method very attractive. In this paper, we establish the asymptotic properties for the resulting estimators. With the proposed method, we conduct sensitivity analyses on a cohort data set arising from the Framingham Heart Study. Simulation studies are carried out to evaluate the impact of ignoring covariate measurement error and to assess the performance of the proposed method.     

Inferring Pongo conservation units: a perspective based on microsatellite and mitochondrial DNA analyses

In order to define evolutionarily significant and management units (ESUs and MUs) among subpopulations of Sumatran (Pongo pygmaeus abelii) and Bornean (P. p. pygmaeus) orangutans we determined their genetic relationships. We analyzed partial sequences of four mitochondrial genes and nine autosomal microsatellite loci of 70 orangutans to test two hypotheses regarding the population structure within Borneo and the genetic distinction between Bornean and Sumatran orangutans. Our data show Bornean orangutans consist of two genetic clusters—the western and eastern clades. Each taxon exhibits relatively distinct mtDNA and nuclear genetic distributions that are likely attributable to genetic drift. These groups, however, do not warrant designations as separate conservation MUs because they demonstrate no demographic independence and only moderate genetic differentiation. Our findings also indicate relatively high levels of overall genetic diversity within Borneo, suggesting that observed habitat fragmentation and erosion during the last three decades had limited influence on genetic variability. Because the mtDNA of Bornean and Sumatran orangutans are not strictly reciprocally monophyletic, we recommend treating these populations as separate MUs and discontinuing inter-island translocation of animals unless absolutely necessary.     

Test for a population expansion after a drastic reduction in population size using DNA sequence data

Populations may, during their evolutionary history, go through drastic changes in population size due to bottlenecks or founder events upon colonization of new areas. This involves a subsample of haplotypes, causing the allele frequencies to be different from the original population. In addition, the period of recovery after a bottleneck can be of considerable length. If reproduction is unequal among individuals but random with regard to haplotype, large deviations from the patterns expected in a stable population may result. By means of computer simulation, I have analysed the patterns arising when populations undergo bottlenecks and then slowly recover, and used two new statistical tests for the detection of the bottleneck. A test based on the variance of the relative frequency of haplotypes had generally high power even at low sample size (n=25). This statistic was most powerful after very strong bottlenecks and lost power with increasing propagule size. A test based on the variance of the pairwise differences shows slightly less power. As expected, power was reduced when migration into the founder population was allowed from the source population. This suggests that the test is particularly suited for detecting relatively recent and strong bottlenecks, and thus may be a valuable tool for identifying population events on a fine temporal scale, such as colonisations after the last glaciation.