Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden

Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low

Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals

Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny

Recombination in feline immunodeficiency virus from feral and companion domestic cats

<p>Abstract</p> <p>Background</p> <p>Recombination is a relatively common phenomenon in retroviruses. We investigated recombination in <it>Feline Immunodeficiency Virus </it>from naturally-infected New Zealand domestic cats (<it>Felis catus</it>) by sequencing regions of the <it>gag</it>, <it>pol </it>and <it>env </it>genes.</p> <p>Results</p> <p>The occurrence of intragenic recombination was highest in <it>env</it>, with evidence of recombination in 6.4% (n = 156) of all cats. A further recombinant was identified in each of the <it>gag </it>(n = 48) and <it>pol </it>(n = 91) genes. Comparisons of phylogenetic trees across genes identified cases of incongruence, indicating intergenic recombination. Three (7.7%, n = 39) of these incongruencies were found to be significantly different using the Shimodaira-Hasegawa test.</p> <p>Surprisingly, our phylogenies from the <it>gag </it>and <it>pol </it>genes showed that no New Zealand sequences group with reference subtype C sequences within intrasubtype pairwise distances. Indeed, we find one and two distinct unknown subtype groups in <it>gag </it>and <it>pol</it>, respectively. These observations cause us to speculate that these New Zealand FIV strains have undergone several recombination events between subtype A parent strains and undefined unknown subtype strains, similar to the evolutionary history hypothesised for HIV-1 "subtype E".</p> <p>Endpoint dilution sequencing was used to confirm the consensus sequences of the putative recombinants and unknown subtype groups, providing evidence for the authenticity of these sequences. Endpoint dilution sequencing also resulted in the identification of a dual infection event in the <it>env </it>gene. In addition, an intrahost recombination event between variants of the same subtype in the <it>pol </it>gene was established. This is the first known example of naturally-occurring recombination in a cat with infection of the parent strains.</p> <p>Conclusion</p> <p>Evidence of intragenic recombination in the <it>gag</it>, <it>pol </it>and <it>env </it>regions, and complex intergenic recombination, of FIV from naturally-infected domestic cats in New Zealand was found. Strains of unknown subtype were identified in all three gene regions. These results have implications for the use of the current FIV vaccine in New Zealand.</p

On the Use of Bootstrapped Topologies in Coalescent-Based Bayesian MCMC Inference: A Comparison of Estimation and Computational Efficiencies

Coalescent-based Bayesian Markov chain Monte Carlo (MCMC) inference generates estimates of evolutionary parameters and their posterior probability distributions. As the number of sequences increases, the length of time taken to complete an MCMC analysis increases as well. Here, we investigate an approach to distribute the MCMC analysis across a cluster of computers. To do this, we use bootstrapped topologies as fixed genealogies, perform a single MCMC analysis on each genealogy without topological rearrangements, and pool the results across all MCMC analyses. We show, through simulations, that although the standard MCMC performs better than the bootstrap-MCMC at estimating the effective population size (scaled by mutation rate), the bootstrap-MCMC returns better estimates of growth rates. Additionally, we find that our bootstrap-MCMC analyses are, on average, 37 times faster for equivalent effective sample sizes

HaploJuice : accurate haplotype assembly from a pool of sequences with known relative concentrations

Pooling techniques, where multiple sub-samples are mixed in a single sample, are widely used to take full advantage of high-throughput DNA sequencing. Recently, Ranjard et al. [1] proposed a pooling strategy without the use of barcodes. Three sub-samples were mixed in different known proportions (i.e. 62.5%, 25% and 12.5%), and a method was developed to use these proportions to reconstruct the three haplotypes effectively. HaploJuice provides an alternative haplotype reconstruction algorithm for Ranjard et al.’s pooling strategy. HaploJuice significantly increases the accuracy by first identifying the empirical proportions of the three mixed sub-samples and then assembling the haplotypes using a dynamic programming approach. HaploJuice was evaluated against five different assembly algorithms, Hmmfreq [1], ShoRAH [2], SAVAGE [3], PredictHaplo [4] and QuRe [5]. Using simulated and real data sets, HaploJuice reconstructed the true sequences with the highest coverage and the lowest error rate. HaploJuice achieves high accuracy in haplotype reconstruction, making Ranjard et al.’s pooling strategy more efficient, feasible, and applicable, with the benefit of reducing the sequencing cost

Models of microbiome evolution incorporating host and microbial selection

BACKGROUND: Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. METHODS: In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. RESULTS: We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong parental contribution, when host-mediated selection acts on microbes concomitantly. CONCLUSIONS: We present a computational framework that integrates different selective processes acting on the evolution of microbiomes. Our framework demonstrates that selection acting on microbes can have a strong effect on microbial diversities and fitnesses, whereas selection on hosts can have weaker outcomes.This research was supported by funds to QZ and AR from Duke University

Neutral Models of Microbiome Evolution

There has been an explosion of research on host-associated microbial communities (i.e.,microbiomes). Much of this research has focused on surveys of microbial diversities across a variety of host species, including humans, with a view to understanding how these microbiomes are distributed across space and time, and how they correlate with host health, disease, phenotype, physiology and ecology. Fewer studies have focused on how these microbiomes may have evolved. In this paper, we develop an agent-based framework to study the dynamics of microbiome evolution. Our framework incorporates neutral models of how hosts acquire their microbiomes, and how the environmental microbial community that is available to the hosts is assembled. Most importantly, our framework also incorporates a Wright-Fisher genealogical model of hosts, so that the dynamics of microbiome evolution is studied on an evolutionary timescale. Our results indicate that the extent of parental contribution to microbial availability from one generation to the next significantly impacts the diversity of microbiomes: the greater the parental contribution, the less diverse the microbiomes. In contrast, even when there is only a very small contribution from a constant environmental pool, microbial communities can remain highly diverse. Finally, we show that our models may be used to construct hypotheses about the types of processes that operate to assemble microbiomes over evolutionary time