Biology subjects

Stadler, T.

Publications and source records attributed to Stadler, T..

62 records · Page 4Linked to original sources

bioRxiv2017

Fast Bayesian Inference of Phylogenetic Models Using Parallel Likelihood Calculation and Adaptive Metropolis Sampling

O_LIPhylogenetic comparative models (PCMs) have been used to study macroevolutionary patterns, to characterize adaptive phenotypic landscapes, to quantify rates of evolution, to measure the heritability of traits, and to test various evolutionary hypotheses. A major obstacle to applying these models has been the complexity of evaluating their likelihood function. Recent works have shown that for many PCMs, the likelihood can be obtained in time proportional to the size of the tree based on post-order tree traversal, also known as pruning. Despite this progress, inferring complex multi-trait PCMs on large trees remains a time-intensive task. Here, we study parallelizing the pruning algorithm as a generic technique for speeding-up PCM-inference.\nC_LIO_LIWe implement several parallel traversal algorithms in the form of a generic C++ library for Serial and Parallel LIneage Traversal of Trees (SPLITT). Based on SPLITT, we provide examples of parallel likelihood evaluation for several popular PCMs, ranging from a single-trait Brownian motion model to complex multi-trait Ornstein-Uhlenbeck and mixed Gaussian phylogenetic models.\nC_LIO_LIUsing the phylogenetic Ornstein-Uhlenbeck mixed model (POUMM) as a showcase, we run benchmarks on up to 24 CPU cores, reporting up to an order of magnitude parallel speed-up on simulated balanced and unbalanced trees of up to 100,000 tips with up to 16 traits. Noticing that the parallel speed-up depends on multiple factors, the SPLITT library is capable to automatically select the fastest traversal strategy for a given hardware, tree-topology, and data. Combining SPLITT likelihood calculation with adaptive Metropolis sampling on real data, we show that the time for Bayesian POUMM inference on a tree of 10,000 tips can be reduced from several days to minutes.\nC_LIO_LIWe conclude that parallel pruning effectively accelerates the likelihood calculation and, thus, the statistical inference of Gaussian phylogenetic models. For time-intensive Bayesian inferences, we recommend combining this technique with adaptive Metropolis sampling. Beyond Gaussian models, the parallel tree traversal can be applied to numerous other models, including discrete trait and birth-death population dynamics models. Currently, SPLITT supports multi-core shared memory architectures, but can be extended to distributed memory architectures as well as graphical processing units.\nC_LI

evolutionary biology↗

bioRxiv2017

Tuberculosis outbreak investigation using phylodynamic analysis

The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis.\n\nIn this study, we investigate the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. The first outbreak occurred in the Swiss city of Bern (1993-2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.\n\nThere is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. We estimate an evolutionary rate of 0.0039 per single nucleotide polymorphism (SNP) per year for Bern and 0.0024 per SNP per year for WTK. Nevertheless, due to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, theres much uncertainty in the epidemiological estimates concerning the WTK outbreak, which has a small sampling proportion (9%). Our results suggest that both outbreaks peaked around 1990, although the Bernese outbreak was only detected in 1993, and the WTK outbreak around 2004. Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4-5 years).\n\nOur work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.

epidemiology↗

bioRxiv2017

MASCOT: Parameter and state inference under the marginal structured coalescent approximation

MotivationThe structured coalescent is widely applied to study demography within and migration between sub-populations from genetic sequence data. Current methods are either exact but too computationally inefficient to analyse large datasets with many states, or make strong approximations leading to severe biases in inference. We recently introduced an approximation based on weaker assumptions to the structured coalescent enabling the analysis of larger datasets with many different states. We showed that our approximation provides unbiased migration rate and population size estimates across a wide parameter range.\n\nResultsWe here extend this approach by providing a new algorithm to calculate the probability of the state of internal nodes that includes the information from the full phylogenetic tree. We show that this algorithm is able to increase the probability attributed to the true node states. Furthermore we use improved integration techniques, such that our method is now able to analyse larger datasets, including a H3N2 dataset with 433 sequences sampled from 5 different locations.\n\nAvailabilityThe here presented methods are combined into the BEAST2 package MASCOT, the Marginal Approximation of the Structured COalescenT. This package can be downloaded via the BEAUti package manager. The source code is available at https://github.com/nicfel/Mascot.git.

evolutionary biology↗

bioRxiv2017

Directly Estimating Epidemic Curves From Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

epidemiology↗

bioRxiv2017

Bayesian Inference Of Species Networks From Multilocus Sequence Data

Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network. We assume a multispecies network coalescent (MSNC) prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. We reanalyze a large dataset of genes from closely related spruces, and verify the previously suggested homoploid hybridization event in this clade. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides a general framework for Bayesian inference of reticulate evolution.

evolutionary biology↗

bioRxiv2017

External introductions helped drive and sustain the high incidence of HIV-1 in rural KwaZulu-Natal, South Africa

Despite increasing access to antiretroviral therapy, HIV incidence in rural KwaZulu-Natal communities remains among the highest ever reported in Africa. While many epidemiological factors have been invoked to explain this high incidence, widespread human mobility and movement of viral lineages between geographic locations have implicated high rates of transmission across communities. High rates of crosscommunity transmission call into question how effective increasing local coverage of antiretroviral therapy will be at preventing new infections, especially if many new cases arise from external introductions. To help address this question, we use a new phylodynamic modeling approach to estimate both changes in epidemic dynamics through time and the relative contribution of local transmission versus external introductions to overall incidence from HIV-1 subtype C phylogenies. Our phylodynamic estimates of HIV prevalence and incidence are remarkably consistent with population-based surveillance data. Our analysis also reveals that early epidemic dynamics in this population were largely driven by a wave of external introductions. More recently, we estimate that anywhere between 20-60% of all new infections arise from external introductions from outside the local community. These results highlight the power of using phylodynamic methods to study generalized HIV epidemics and the growing need to consider larger-scale regional transmission dynamics above the level of local communities when designing and testing prevention strategies.

epidemiology↗

bioRxiv2017

POUMM: An R-package for Bayesian Inference of Phylogenetic Heritability

AO_SCPLOWBSTRACTC_SCPLOWPhylogenetic comparative methods have been used to model trait evolution, to test selection versus neutral hypotheses, to estimate optimal trait-values, and to quantify the rate of adaptation towards these optima. Several authors have proposed algorithms calculating the likelihood for trait evolution models, such as the Ornstein-Uhlenbeck (OU) process, in time proportional to the number of tips in the tree. Combined with gradient-based optimization, these algorithms enable maximum likelihood (ML) inference within seconds, even for trees exceeding 10,000 tips. Despite its useful statistical properties, ML has been criticised for being a point estimator prone to getting stuck in local optima. As an elegant alternative, Bayesian inference explores the entire information in the data and compares it to prior knowledge but, usually, runs in much longer time, even for small trees. Here, we propose an approach to use the full potential of ML and Bayesian inference, while keeping the runtime within minutes. Our approach combines (i) a new algorithm for parallel likelihood calculation; (ii) a previously published method for adaptive Metropolis sampling. In principle, the strategy of (i) and (ii) can be applied to any likelihood calculation on a tree which proceeds in a pruning-like fashion leading to enormous speed improvements. As a showcase, we implement the phylogenetic Ornstein-Uhlenbeck mixed model (POUMM) in the form of an easy-to-use and highly configurable R-package. In addition to the above-mentioned usage of comparative methods, the POUMM allows to estimate non-heritable variance and phylogenetic heritability. Using simulations and empirical data from 487 mammal species, we show that the POUMM is far more reliable in terms of unbiased estimates and false positive rate for stabilizing selection, compared to its alternative - the non-mixed Ornstein-Uhlenbeck model, which assumes a fully heritable and perfectly measurable trait. Further, our analysis reveals that the phylogenetic mixed model (PMM), which assumes neutral evolution (Brownian motion) can be a very unstable estimator of phylogenetic heritability, even if the Brownian motion assumption is only weakly violated. Our results prove the need for a simultaneous account for selection and non-heritable variance in phylogenetic evolutionary models and challenge stabilizing selection hypotheses stated in numerous macro-evolutionary studies.

evolutionary biology↗

bioRxiv2016

The Structured Coalescent and its Approximations

Phylogenetics can be used to elucidate the movement of genes between populations of organisms, using phylogeographic methods. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or the gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for datasets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex datasets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. While this solution is computationally unfeasible for large datasets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.

evolutionary biology↗