ONeSAMP: a program to estimate effective population size using approximate Bayesian computation

The estimation of effective population size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, ONeSAMP that uses approximate Bayesian computation to estimate effective population size from a sample of microsatellite genotypes. ONeSAMP requires an input file of sampled individuals' microsatellite genotypes along with information about several sampling and biological parameters. ONeSAMP provides an estimate of effective population size, along with 95% credible limits. We illustrate the use of ONeSAMP with an example data set from a re-introduced population of ibex Capra ibex.

Novel high-resolution characterization of ancient DNA reveals C > U-type base modification events as the sole cause of post mortem miscoding lesions

Ancient DNA (aDNA) research has long depended on the power of PCR to amplify trace amounts of surviving genetic material from preserved specimens. While PCR permits specific loci to be targeted and amplified, in many ways it can be intrinsically unsuited to damaged and degraded aDNA templates. PCR amplification of aDNA can produce highly-skewed distributions with significant contributions from miscoding lesion damage and non-authentic sequence artefacts. As traditional PCR-based approaches have been unable to fully resolve the molecular nature of aDNA damage over many years, we have developed a novel single primer extension (SPEX)-based approach to generate more accurate sequence information. SPEX targets selected template strands at defined loci and can generate a quantifiable redundancy of coverage; providing new insights into the molecular nature of aDNA damage and fragmentation. SPEX sequence data reveals inherent limitations in both traditional and metagenomic PCR-based approaches to aDNA, which can make current damage analyses and correct genotyping of ancient specimens problematic. In contrast to previous aDNA studies, SPEX provides strong quantitative evidence that C U-type base modifications are the sole cause of authentic endogenous damage-derived miscoding lesions. This new approach could allow ancient specimens to be genotyped with unprecedented accuracy.

Stocking may increase mitochondrial DNA diversity but fails to halt the decline of endangered Atlantic salmon populations

Over the last 50 years, Spanish Atlantic salmon (Salmo salar) populations have been in decline. In order to bolster these populations, rivers were stocked with fish of northern European origin during the period 1974-1996, probably also introducing the furunculosis-inducing pathogen, Aeromonas salmonicida. Here we assess the relative importance of processes influencing mitochondrial (mt)DNA variability in these populations from 1948 to 2002. Genetic material collected over this period from four rivers in northern Spain (Cantabria) was used to detect variability at the mtDNA ND1 gene. Before stocking, a single haplotype was found at high frequency (0.980). Following stocking, haplotype diversity (h) increased in all rivers (mean h before stocking was 0.041, and 0.245 afterwards). These increases were due principally to the dramatic increase in frequency of a previously very low frequency haplotype, reported at higher frequencies in northern European populations proximate to those used to stock Cantabrian rivers. Genetic structuring increased after stocking: among-river differentiation was low before stocking (1950s/1960s Phi(ST) = -0.00296-0.00284), increasing considerably at the height of stocking (1980s Phi(ST) = 0.18932) and decreasing post-stocking (1990s/2002 Phi(ST) = 0.04934-0.03852). Gene flow from stocked fish therefore seems to have had a substantial role in increasing mtDNA variability. Additionally, we found significant differentiation between individuals that had probably died from infectious disease and apparently healthy, angled fish, suggesting a possible role for pathogen-driven selection of mtDNA variation. Our results suggest that stocking with non-native fish may increase genetic diversity in the short term, but may not reverse population declines.

Rare and fleeting: an example of interspecific recombination in animal mitochondrial DNA

Recombination is thought to occur only rarely in animal mitochondrial DNA ( mtDNA). However, detection of mtDNA recombination requires that cells become heteroplasmic through mutation, intramolecular recombination or ' leakage' of paternal mtDNA. Interspecific hybridization increases the probability of detecting mtDNA recombinants due to higher levels of sequence divergence and potentially higher levels of paternal leakage. During a study of historical variation in Atlantic salmon ( Salmo salar) mtDNA, an individual with a recombinant haplotype containing sequence from both Atlantic salmon and brown trout ( Salmo trutta) was detected. The individual was not an F1 hybrid but it did have an unusual nuclear genotype which suggested that it was a later-generation backcross. No other similar recombinant haplotype was found from the same population or three neighbouring Atlantic salmon populations in 717 individuals collected during 1948 - 2002. Interspecific recombination may increase mtDNA variability within species and can have implications for phylogenetic studies.

A note on the accuracy of PAC-likelihood inference with microsatellite data

Stephens and Donnelly have introduced a simple yet powerful importance sampling scheme for computing the likelihood in population genetic models. Fundamental to the method is an approximation to the conditional probability of the allelic type of an additional gene, given those currently in the sample. As noted by Li and Stephens, the product of these conditional probabilities for a sequence of draws that gives the frequency of allelic types in a sample is an approximation to the likelihood, and can be used directly in inference. The aim of this note is to demonstrate the high level of accuracy of "product of approximate conditionals" (PAC) likelihood when used with microsatellite data. Results obtained on simulated microsatellite data show that this strategy leads to a negligible bias over a wide range of the scaled mutation parameter theta. Furthermore, the sampling variance of likelihood estimates as well as the computation time are lower than that obtained with importance sampling on the whole range of theta. It follows that this approach represents an efficient substitute to IS algorithms in computer intensive (e.g. MCMC) inference methods in population genetics. (c) 2006 Elsevier Inc. All rights reserved.

Statistical evaluation of alternative models of human evolution

An appropriate model of recent human evolution is not only important to understand our own history, but it is necessary to disentangle the effects of demography and selection on genome diversity. Although most genetic data support the view that our species originated recently in Africa, it is still unclear if it completely replaced former members of the Homo genus, or if some interbreeding occurred during its range expansion. Several scenarios of modern human evolution have been proposed on the basis of molecular and paleontological data, but their likelihood has never been statistically assessed. Using DNA data from 50 nuclear loci sequenced in African, Asian and Native American samples, we show here by extensive simulations that a simple African replacement model with exponential growth has a higher probability (78%) as compared with alternative multiregional evolution or assimilation scenarios. A Bayesian analysis of the data under this best supported model points to an origin of our species approximate to 141 thousand years ago (Kya), an exit out-of-Africa approximate to 51 Kya, and a recent colonization of the Americas approximate to 10.5 Kya. We also find that the African replacement model explains not only the shallow ancestry of mtDNA or Y-chromosomes but also the occurrence of deep lineages at some autosomal loci, which has been formerly interpreted as a sign of interbreeding with Homo erectus.

The automation and evaluation of nested clade phylogeographic analysis

Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.

Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo

Varroa destructor is a parasitic mite of the Eastern honeybee Apis cerana. Fifty years ago, two distinct evolutionary lineages (Korean and Japanese) invaded the Western honeybee Apis mellifera. This haplo-diploid parasite species reproduces mainly through brother sister matings, a system which largely favors the fixation of new mutations. In a worldwide sample of 225 individuals from 21 locations collected on Western honeybees and analyzed at 19 microsatellite loci, a series of de novo mutations was observed. Using historical data concerning the invasion, this original biological system has been exploited to compare three mutation models with allele size constraints for microsatellite markers: stepwise (SMM) and generalized (GSM) mutation models, and a model with mutation rate increasing exponentially with microsatellite length (ESM). Posterior probabilities of the three models have been estimated for each locus individually using reversible jump Markov Chain Monte Carlo. The relative support of each model varies widely among loci, but the GSM is the only model that always receives at least 9% support, whatever the locus. The analysis also provides robust estimates of mutation parameters for each locus and of the divergence time of the two invasive lineages (67,000 generations with a 90% credibility interval of 35,000-174,000). With an average of 10 generations per year, this divergence time fits with the last post-glacial Korea Japan land separation. (c) 2005 Elsevier Inc. All rights reserved.

Geographical variation in the clouded leopard, Neofelis nebulosa, reveals two species

The clouded leopard, Neofelis nebulosa, is an endangered semiarboreal felid with a wide distribution in tropical forests of southern and southeast Asia, including the islands of Sumatra and Borneo in the Indonesian archipelago [1]. In common with many larger animal species, it displays morphological variation within its wide geographical range and is currently regarded as comprising of up to four subspecies [2-4]. It is widely recognized that taxonomic designation has a major impact on conservation planning and action [5-8]. Given that the last taxonomic revision was made over 50 years ago [2], a more detailed examination of geographical variation is needed. We describe here the results of a morphometric analysis of the pelages of 57 clouded leopards sampled throughout the species' range. We conclude that there are two distinct morphological groups, which differ primarily in the size of their cloud markings. These results are supported by a recent genetic analysis [9]. On that basis, we give diagnoses for the distinction of two species, one in mainland Asia (N. nebulosa) and the other in Indonesia (N. diardi). The implications for conservation that arise from this new taxonomic arrangement are discussed.

Adaptation and speciation: what can F-st tell us?

A useful way of summarizing genetic variability among different populations is through estimates of the inbreeding coefficient, F-st. Several recent studies have tried to use the distribution of estimates of F-st from individual genetic loci to detect the effects of natural selection. However, the promise of this approach has yet to be fully realized owing to the pervasive dogma that this distribution is highly dependent on demographic history. Here, I review recent theoretical results that indicate that the distribution of estimates of F-st is generally expected to be robust to the vagaries of demographic history. I suggest that analyses based on it provide a useful first step for identifying candidate genes that might be under selection, and explore the ways in which this information can be used in ecological and evolutionary studies.

Bayesian estimation of recent migration rates after a spatial expansion

Approximate Bayesian computation (ABC) is a highly flexible technique that allows the estimation of parameters under demographic models that are too complex to be handled by full-likelihood methods. We assess the utility of this method to estimate the parameters of range expansion in a two-dimensional stepping-stone model, using samples from either a single deme or multiple demes. A minor modification to the ABC procedure is introduced, which leads to an improvement in the accuracy of estimation. The method is then used to estimate the expansion time and migration rates for five natural common vole populations in Switzerland typed for a sex-linked marker and a nuclear marker. Estimates based on both markers suggest that expansion occurred < 10,000 years ago, after the most recent glaciation, and that migration rates are strongly male biased.

Recent developments in genetic data analysis: what can they tell us about human demographic history?

Over the last decade, a number of new methods of population genetic analysis based on likelihood have been introduced. This review describes and explains the general statistical techniques that have recently been used, and discusses the underlying population genetic models. Experimental papers that use these methods to infer human demographic and phylogeographic history are reviewed. It appears that the use of likelihood has hitherto had little impact in the field of human population genetics, which is still primarily driven by more traditional approaches. However, with the current uncertainty about the effects of natural selection, population structure and ascertainment of single-nucleotide polymorphism markers, it is suggested that likelihood-based methods may have a greater impact in the future.

The Bayesian revolution in genetics

Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis. Nonetheless, the sheer amount of computational power that is required for Bayesian statistical analyses has previously limited their use in genetics. These computational constraints have now largely been overcome and the underlying advantages of Bayesian approaches are putting them at the forefront of genetic data analysis in an increasing number of areas.