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

The first steps toward a global pandemic: Reconstructing the demographic history of parasite host switches in its native range

Host switching allows parasites to expand their niches. However, successful switching may require suites of adaptations and also may decrease performance on the old host. As a result, reductions in gene flow accompany many host switches, driving speciation. Because host switches tend to be rapid, it is difficult to study them in real-time, and their demographic parameters remain poorly understood. As a result, fundamental factors that control subsequent parasite evolution, such as the size of the switching population or the extent of immigration from the original host, remain largely unknown. To shed light on the host switching process, we explored how host switches occur in independent host shifts by two ectoparasitic honey bee mites (Varroa destructor and V. jacobsoni). Both switched to the western honey bee (Apis mellifera) after being brought into contact with their ancestral host (Apis cerana), ~70 and ~12 years ago, respectively. Varroa destructor subsequently caused worldwide collapses of honey bee populations. Using whole-genome sequencing on 63 mites collected in their native ranges from both the ancestral and novel hosts, we were able to reconstruct the known temporal dynamics of the switch. We further found multiple previously undiscovered mitochondrial lineages on the novel host, along with the genetic equivalent of tens of individuals that were involved in the initial host switch. Despite being greatly reduced, some gene flow remains between mites adapted to different hosts. Our findings suggest that while reproductive isolation may facilitate the fixation of traits beneficial for exploiting the new host, ongoing genetic exchange may allow genetic amelioration of inbreeding effects.

A new detection method for a newly revealed mechanism of pyrethroid resistance development in Varroa destructor

The Varroa destructor mite has recently displayed an ever increasing resistance to new drugs, contributing to CCD proliferation. This work was aimed at determining new viable methods for identifying the pyrethroid resistance of V. destructor and DNA methylation in resistant and sensitive mites. DNA was extracted from Varroa mites. Nucleotide changes in the DNA of pyrethroid-resistant, pyrethroid-sensitive, and control mites were identified with polymerase chain reaction single-strand conformation polymorphism (PCR-SSCP) in the case of five mitochondrial gene fragments. More bands were observed in the drug-resistant mites than in the other two groups. Sequencing confirmed these observations. Decreased global DNA methylation levels were observed in the pyrethroid-resistant mites. There exists a previously undescribed mechanism of pyrethroid resistance development in Varroa mites. The PCR-SSCP methods can be considered and further developed as useful tools for detecting V. destructor resistance.

Multiple host shifts by the emerging honeybee parasite, Varroa jacobsoni

Host shifts are a key mechanism of parasite evolution and responsible for the emergence of many economically important pathogens. Varroa destructor has been a major factor in global honeybee (Apis mellifera) declines since shifting hosts from the Asian honeybee (Apis cerana) > 50 years ago. Until recently, only two haplotypes of V. destructor (Korea and Japan) had successfully host shifted to A. mellifera. In 2008, the sister species V. jacobsoni was found for the first time parasitizing A. mellifera in Papua New Guinea (PNG). This recent host shift presents a serious threat to world apiculture but also provides the opportunity to examine host shifting in this system. We used 12 microsatellites to compare genetic variation of V. jacobsoni on A. mellifera in PNG with mites on A. cerana in both PNG and surrounding regions. We identified two distinct lineages of V. jacobsoni reproducing on A. mellifera in PNG. Our analysis indicated independent host shift events have occurred through small numbers of mites shifting from local A. cerana populations. Additional lineages were found in the neighbouring Papua and Solomon Islands that had partially host shifted to A. mellifera, that is producing immature offspring on drone brood only. These mites were likely in transition to full colonization of A. mellifera. Significant population structure between mites on the different hosts suggested host shifted V. jacobsoni populations may not still reproduce on A. cerana, although limited gene flow may exist. Our studies provide further insight into parasite host shift evolution and help characterize this new Varroa mite threat to A. mellifera worldwide.

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.

Always look on both sides: phylogenetic information conveyed by simple sequence repeat allele sequences

Simple sequence repeat (SSR) markers are widely used tools for inferences about genetic diversity, phylogeography and spatial genetic structure. Their applications assume that variation among alleles is essentially caused by an expansion or contraction of the number of repeats and that, accessorily, mutations in the target sequences follow the stepwise mutation model (SMM). Generally speaking, PCR amplicon sizes are used as direct indicators of the number of SSR repeats composing an allele with the data analysis either ignoring the extent of allele size differences or assuming that there is a direct correlation between differences in amplicon size and evolutionary distance. However, without precisely knowing the kind and distribution of polymorphism within an allele (SSR and the associated flanking region (FR) sequences), it is hard to say what kind of evolutionary message is conveyed by such a synthetic descriptor of polymorphism as DNA amplicon size. In this study, we sequenced several SSR alleles in multiple populations of three divergent tree genera and disentangled the types of polymorphisms contained in each portion of the DNA amplicon containing an SSR. The patterns of diversity provided by amplicon size variation, SSR variation itself, insertions/deletions (indels), and single nucleotide polymorphisms (SNPs) observed in the FRs were compared. Amplicon size variation largely reflected SSR repeat number. The amount of variation was as large in FRs as in the SSR itself. The former contributed significantly to the phylogenetic information and sometimes was the main source of differentiation among individuals and populations contained by FR and SSR regions of SSR markers. The presence of mutations occurring at different rates within a marker’s sequence offers the opportunity to analyse evolutionary events occurring on various timescales, but at the same time calls for caution in the interpretation of SSR marker data when the distribution of within-locus polymorphism is not known.

A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis

Variable numbers of tandem repeats (VNTR) typing is widely used for studying the bacterial cause of tuberculosis. Knowledge of the rate of mutation of VNTR loci facilitates the study of the evolution and epidemiology of Mycobacterium tuberculosis. Previous studies have applied population genetic models to estimate the mutation rate, leading to estimates varying widely from around to per locus per year. Resolving this issue using more detailed models and statistical methods would lead to improved inference in the molecular epidemiology of tuberculosis. Here, we use a model-based approach that incorporates two alternative forms of a stepwise mutation process for VNTR evolution within an epidemiological model of disease transmission. Using this model in a Bayesian framework we estimate the mutation rate of VNTR in M. tuberculosis from four published data sets of VNTR profiles from Albania, Iran, Morocco and Venezuela. In the first variant, the mutation rate increases linearly with respect to repeat numbers (linear model); in the second, the mutation rate is constant across repeat numbers (constant model). We find that under the constant model, the mean mutation rate per locus is (95% CI: ,)and under the linear model, the mean mutation rate per locus per repeat unit is (95% CI: ,). These new estimates represent a high rate of mutation at VNTR loci compared to previous estimates. To compare the two models we use posterior predictive checks to ascertain which of the two models is better able to reproduce the observed data. From this procedure we find that the linear model performs better than the constant model. The general framework we use allows the possibility of extending the analysis to more complex models in the future.

Genetically typing the bacterium responsible for tuberculosis is useful for understanding the evolutionary and epidemiological characteristics of the disease. Typing methods based on variable number tandem repeat (VNTR) loci are increasingly being used. These loci, which are composed of repeated units, mutate by increasing or decreasing in the number of these repeats. Knowledge of the mutation rate of molecular markers facilitates the epidemiological interpretation of the observed genetic variation in a sample of bacterial isolates. Few studies have examined the rate of mutation at these markers and estimates to date have varied considerably. To address this problem we develop a stochastic model of evolution of these markers and then estimate their mutation rate using approximate Bayesian computation. We examine two alternative forms of the mutation process. The observed data are from four published data sets of tuberculosis bacterial isolates sampled in Albania, Iran, Morocco and Venezuela. We find that these markers have fairly high rates of mutation compared with estimates from previous studies.

Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo

We provide a framework for Bayesian coalescent inference from microsatellite data that enables inference of population history parameters averaged over microsatellite mutation models. To achieve this we first implemented a rich family of microsatellite mutation models and related components in the software package BEAST. BEAST is a powerful tool that performs Bayesian MCMC analysis on molecular data to make coalescent and evolutionary inferences. Our implementation permits the application of existing nonparametric methods to microsatellite data. The implemented microsatellite models are based on the replication slippage mechanism and focus on three properties of microsatellite mutation: length dependency of mutation rate, mutational bias toward expansion or contraction, and number of repeat units changed in a single mutation event. We develop a new model that facilitates microsatellite model averaging and Bayesian model selection by transdimensional MCMC. With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty. Simulated data are used to evaluate our method in terms of accuracy and precision of θ estimation and also identification of the true mutation model. Finally we apply our method to a red colobus monkey data set as an example.