Mahesh Panchal

THE AUTOMATION AND EVALUATION OF NESTED CLADE PHYLOGEOGRAPHIC ANALYSIS

Abstract 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.

In defence of model-based inference in phylogeography

Recent papers have promoted the view that model‐based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model‐based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model‐based inference in population genetics.

On the validity of nested clade phylogeographical analysis

As will be apparent to any reader of Molecular Ecology, a large number of genetic analyses of geographically structured populations are routinely carried out. Researchers make promises to their sponsors that they can uncover aspects of the demographic history of these populations. Inevitably, there is demand for statistical methods that will provide credible answers, and consequently, many methods have been supplied. Nested clade phylogeographical analysis (NCPA) is a technique that continues to be quite popular (Petit 2008). Naturally, there is curiosity to see whether the method works. Essentially, there is a disagreement between Templeton (2004, 2008), who suggests the method works well, and three independent groups (Knowles & Maddison 2002; Petit & Grivet 2002; Panchal & Beaumont 2007), who believe that they have demonstrated that it does not. As far as we are aware, there are currently no publications other than those of Templeton and co-workers to support the accuracy or efficacy of NCPA. In this article, we address some comments by Templeton (2008) on the study by Panchal & Beaumont (2007). We begin by highlighting a recommendation in Templeton (2004) that NCPA should be tested using data simulated from a single panmictic population of constant size, which we have carried out. We argue that our automated implementation is as close as is feasible to the published versions, and that the falsepositive rate that we observe is not a consequence of our implementation. With respect to validation, we suggest that it is not possible to draw comparisons between the outcome of our simulation-based tests and the inferences drawn from empirical data sets. We also note that many recent modifications of NCPA have had little validation. The difficulty of correcting for multiple statistical tests in NCPA is then discussed. Finally, we address the more general issues raised by Templeton (2008) concerning the method of scientific enquiry, and we recommend that a more model-based approach should be taken to infer demographic history. The tests discussed by Templeton (2008) were primarily applied to data assumed to have histories with range expansion and fragmentation. As Templeton (2004) noted, there is also a persuasive case for testing NCPA under scenarios of panmixia, using simulated data sets:

Genome‐wide patterns of standing genetic variation in a marine population of three‐spined sticklebacks

Since the end of the Pleistocene, the three‐spined stickleback (Gasterosteus aculeatus) has repeatedly colonized and adapted to various freshwater habitats probably originating from ancestral marine populations. Standing genetic variation and the underlying genomic architecture both have been speculated to contribute to recent adaptive radiations of sticklebacks. Here, we expand on the current genomic resources of this fish by providing extensive genome‐wide variation data from six individuals from a marine (North Sea) stickleback population. Using next‐generation sequencing and a combination of paired‐end and mate‐pair libraries, we detected a wide size range of genetic variation. Among the six individuals, we found more than 7% of the genome is polymorphic, consisting of 2 599 111 SNPs, 233 464 indels and structural variation (SV) (>50 bp) such as 1054 copy‐number variable regions (deletions and duplications) and 48 inversions. Many of these polymorphisms affect gene and coding sequences. Based on SNP diversity, we determined outlier regions concordant with signatures expected under adaptive evolution. As some of these outliers overlap with pronounced regions of copy‐number variation, we propose the consideration of such SV when analysing SNP data from re‐sequencing approaches. We further discuss the value of this resource on genome‐wide variation for further investigation upon the relative contribution of standing variation on the parallel evolution of sticklebacks and the importance of the genomic architecture in adaptive radiation.

Extensive Copy-Number Variation of Young Genes across Stickleback Populations

Duplicate genes emerge as copy-number variations (CNVs) at the population level, and remain copy-number polymorphic until they are fixed or lost. The successful establishment of such structural polymorphisms in the genome plays an important role in evolution by promoting genetic diversity, complexity and innovation. To characterize the early evolutionary stages of duplicate genes and their potential adaptive benefits, we combine comparative genomics with population genomics analyses to evaluate the distribution and impact of CNVs across natural populations of an eco-genomic model, the three-spined stickleback. With whole genome sequences of 66 individuals from populations inhabiting three distinct habitats, we find that CNVs generally occur at low frequencies and are often only found in one of the 11 populations surveyed. A subset of CNVs, however, displays copy-number differentiation between populations, showing elevated within-population frequencies consistent with local adaptation. By comparing teleost genomes to identify lineage-specific genes and duplications in sticklebacks, we highlight rampant gene content differences among individuals in which over 30% of young duplicate genes are CNVs. These CNV genes are evolving rapidly at the molecular level and are enriched with functional categories associated with environmental interactions, depicting the dynamic early copy-number polymorphic stage of genes during population differentiation.

Evaluating Nested Clade Phylogeographic Analysis under Models of Restricted Gene Flow

Nested clade phylogeographic analysis (NCPA) is a widely used method that aims to identify past demographic events that have shaped the history of a population. In an earlier study, NCPA has been fully automated, allowing it to be tested with simulated data sets generated under a null model in which samples simulated from a panmictic population are geographically distributed. It was noted that NCPA was prone to inferring false positives, corroborating earlier findings. The present study aims to evaluate both single-locus and multilocus NCPA under the scenario of restricted gene flow among spatially distributed populations. We have developed a new program, ANeCA-ML, which implements multilocus NCPA. Data were simulated under 3 models of gene flow: a stepping stone model, an island model, and a stepping stone model with some long-distance dispersal. Results indicate that single-locus NCPA tends to give a high frequency of false positives, but, unlike the random-mating scenario presented previously, inferences are not limited to restricted gene flow with isolation by distance or contiguous range expansion. The proportion of single-locus data sets that contained false inferences was 76% for the panmictic case, 87% for the stepping stone model, 79% for the stepping stone model with long-distance dispersal, and more than 99% for the island model. The frequency of inferences is inversely related to the amount of gene flow between demes. We performed multilocus NCPA by grouping the simulated loci into data sets of 5 loci. The false-positive rate was reduced in multilocus NCPA for some inferences but remained high for others. The proportion of multilocus data sets that contained false inferences was 17% for the panmictic case, 30% for the stepping stone model, 4% for the stepping stone model with long-distance dispersal, and 54% for the island model. Multilocus NCPA reduces the false-positive rate by restricting the sensitivity of the method but does not appear to increase the accuracy of the approach. Three classical tests-the analysis of molecular variance method, Fus Fs, and the Mantel test-show that there is information in the data that gives rise to explicable results using these standard approaches. In conclusion, for the scenarios that we have examined, our simulation study suggests that the NCPA method is unreliable and its inferences may be misleading. We suggest that the NCPA method should not be used without objective simulation-based testing by independent researchers.

Transcriptome profiling of immune tissues reveals habitat-specific gene expression between lake and river sticklebacks

The observation of habitat‐specific phenotypes suggests the action of natural selection. The three‐spined stickleback (Gasterosteus aculeatus) has repeatedly colonized and adapted to diverse freshwater habitats across the northern hemisphere since the last glaciation, while giving rise to recurring phenotypes associated with specific habitats. Parapatric lake and river populations of sticklebacks harbour distinct parasite communities, a factor proposed to contribute to adaptive differentiation between these ecotypes. However, little is known about the transcriptional response to the distinct parasite pressure of those fish in a natural setting. Here, we sampled wild‐caught sticklebacks across four geographical locations from lake and river habitats differing in their parasite load. We compared gene expression profiles between lake and river populations using 77 whole‐transcriptome libraries from two immune‐relevant tissues, the head kidney and the spleen. Differential expression analyses revealed 139 genes with habitat‐specific expression patterns across the sampled population pairs. Among the 139 differentially expressed genes, eight are annotated with an immune function and 42 have been identified as differentially expressed in previous experimental studies in which fish have been immune challenged. Together, these findings reinforce the hypothesis that parasites contribute to adaptation of sticklebacks in lake and river habitats.

Identification of candidate mimicry proteins involved in parasite-driven phenotypic changes

BackgroundEndoparasites with complex life cycles are faced with several biological challenges, as they need to occupy various ecological niches throughout their development. Host phenotypes that increase the parasite’s transmission rate to the next host have been extensively described, but few mechanistic explanations have been proposed to describe their proximate causes. In this study we explore the possibility that host phenotypic changes are triggered by the production of mimicry proteins from the parasite by using an ecological model system consisting of the infection of the threespine stickleback (Gasterosteus aculeatus) by the cestode Schistocephalus solidus.MethodUsing RNA-seq data, we assembled 9,093 protein-coding genes from which ORFs were predicted to generate a reference proteome. Based on a previously published method, we built two complementary analysis pipelines to i) establish a general classification of protein similarity among various species (pipeline A) and ii) identify candidate mimicry proteins showing specific host-parasite similarities (pipeline B), a key feature underlying the possibility of molecular mimicry.ResultsNinety-four tapeworm proteins showed high local sequence homology with stickleback proteins. Four of these candidates correspond to secreted or membrane proteins that could be produced by the parasite and eventually be released in or be in contact with the host to modulate physiological pathways involved in various phenotypes (e.g. behaviors). One of these candidates belongs to the Wnt family, a large group of signaling molecules involved in cell-to-cell interactions and various developmental pathways. The three other candidates are involved in ion transport and post-translational protein modifications. We further confirmed that these four candidates are expressed in three different developmental stages of the cestode by RT-PCR, including the stages found in the host.ConclusionIn this study, we identified mimicry candidate peptides from a behavior-altering cestode showing specific sequence similarity with host proteins. Despite their potential role in modulating host pathways that could lead to parasite-induced phenotypic changes and despite our confirmation that they are expressed in the developmental stage corresponding to the altered host behavior, further investigations will be needed to confirm their mechanistic role in the molecular cross-talk taking place between S. solidus and the threespine stickleback.

Correction: Genomics of Divergence along a Continuum of Parapatric Population Differentiation

The patterns of genomic divergence during ecological speciation are shaped by a combination of evolutionary forces. Processes such as genetic drift, local reduction of gene flow around genes causing reproductive isolation, hitchhiking around selected variants, variation in recombination and mutation rates are all factors that can contribute to the heterogeneity of genomic divergence. On the basis of 60 fully sequenced three-spined stickleback genomes, we explore these different mechanisms explaining the heterogeneity of genomic divergence across five parapatric lake and river population pairs varying in their degree of genetic differentiation. We find that divergent regions of the genome are mostly specific for each population pair, while their size and abundance are not correlated with the extent of genome-wide population differentiation. In each pair-wise comparison, an analysis of allele frequency spectra reveals that 25-55% of the divergent regions are consistent with a local restriction of gene flow. Another large proportion of divergent regions (38-75%) appears to be mainly shaped by hitchhiking effects around positively selected variants. We provide empirical evidence that alternative mechanisms determining the evolution of genomic patterns of divergence are not mutually exclusive, but rather act in concert to shape the genome during population differentiation, a first necessary step towards ecological speciation.