175 research outputs found
On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2
The continuous-time Markov chain (CTMC) is the mathematical workhorse of
evolutionary biology. Learning CTMC model parameters using modern,
gradient-based methods requires the derivative of the matrix exponential
evaluated at the CTMC's infinitesimal generator (rate) matrix. Motivated by the
derivative's extreme computational complexity as a function of state space
cardinality, recent work demonstrates the surprising effectiveness of a naive,
first-order approximation for a host of problems in computational biology. In
response to this empirical success, we obtain rigorous deterministic and
probabilistic bounds for the error accrued by the naive approximation and
establish a "blessing of dimensionality" result that is universal for a large
class of rate matrices with random entries. Finally, we apply the first-order
approximation within surrogate-trajectory Hamiltonian Monte Carlo for the
analysis of the early spread of SARS-CoV-2 across 44 geographic regions that
comprise a state space of unprecedented dimensionality for unstructured
(flexible) CTMC models within evolutionary biology
Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts
Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s. Author summary Both the growth of groups of species and the spread of infectious diseases through populations can be modeled as birth-death processes. Birth events correspond either to speciation or infection, and death events to extinction or becoming noninfectious. The rates of birth and death may vary over time, and by examining this variation researchers can pinpoint important events in the history of life on Earth or in the course of an outbreak. Time-calibrated phylogenies track the relationships between a set of species (or infections) and the times of all speciation (or infection) events, and can thus be used to infer birth and death rates. We develop two phylogenetic birth-death models with the goal of discerning signal of rate variation from noise due to the stochastic nature of birth-death models. Using a variety of simulated datasets, we show that one of these models can accurately infer slow and rapid rate shifts without sacrificing precision. Using real data, we demonstrate that our new methodology can be used for simultaneous inference of phylogeny and rates through time
Random-effects substitution models for phylogenetics via scalable gradient approximations
Phylogenetic and discrete-trait evolutionary inference depend heavily on an
appropriate characterization of the underlying character substitution process.
In this paper, we present random-effects substitution models that extend common
continuous-time Markov chain models into a richer class of processes capable of
capturing a wider variety of substitution dynamics. As these random-effects
substitution models often require many more parameters than their usual
counterparts, inference can be both statistically and computationally
challenging. Thus, we also propose an efficient approach to compute an
approximation to the gradient of the data likelihood with respect to all
unknown substitution model parameters. We demonstrate that this approximate
gradient enables scaling of sampling-based inference, namely Bayesian inference
via Hamiltonian Monte Carlo, under random-effects substitution models across
large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences,
an HKY model with random-effects shows strong signals of nonreversibility in
the substitution process, and posterior predictive model checks clearly show
that it is a more adequate model than a reversible model. When analyzing the
pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences
between 14 regions, a random-effects phylogeographic substitution model infers
that air travel volume adequately predicts almost all dispersal rates. A
random-effects state-dependent substitution model reveals no evidence for an
effect of arboreality on the swimming mode in the tree frog subfamily Hylinae.
Simulations reveal that random-effects substitution models can accommodate both
negligible and radical departures from the underlying base substitution model.
We show that our gradient-based inference approach is over an order of
magnitude more time efficient than conventional approaches
The Dawn of Open Access to Phylogenetic Data
The scientific enterprise depends critically on the preservation of and open
access to published data. This basic tenet applies acutely to phylogenies
(estimates of evolutionary relationships among species). Increasingly,
phylogenies are estimated from increasingly large, genome-scale datasets using
increasingly complex statistical methods that require increasing levels of
expertise and computational investment. Moreover, the resulting phylogenetic
data provide an explicit historical perspective that critically informs
research in a vast and growing number of scientific disciplines. One such use
is the study of changes in rates of lineage diversification (speciation -
extinction) through time. As part of a meta-analysis in this area, we sought to
collect phylogenetic data (comprising nucleotide sequence alignment and tree
files) from 217 studies published in 46 journals over a 13-year period. We
document our attempts to procure those data (from online archives and by direct
request to corresponding authors), and report results of analyses (using
Bayesian logistic regression) to assess the impact of various factors on the
success of our efforts. Overall, complete phylogenetic data for ~60% of these
studies are effectively lost to science. Our study indicates that phylogenetic
data are more likely to be deposited in online archives and/or shared upon
request when: (1) the publishing journal has a strong data-sharing policy; (2)
the publishing journal has a higher impact factor, and; (3) the data are
requested from faculty rather than students. Although the situation appears
dire, our analyses suggest that it is far from hopeless: recent initiatives by
the scientific community -- including policy changes by journals and funding
agencies -- are improving the state of affairs
Author Correction: Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts
Correction to: Scientific Reports https://doi.org/10.1038/s41598-021-86452-3, published online 25 March 2021
The Funding section in the original version of this Article was incomplete
Environmental Factors in the Relapse and Recurrence of Inflammatory Bowel Disease:A Review of the Literature
The causes of relapse in patients with Crohn's disease (CD) and ulcerative colitis (UC) are largely unknown. This paper reviews the epidemiological and clinical data on how medications (non-steroidal anti-inflammatory drugs, estrogens and antibiotics), lifestyle factors (smoking, psychological stress, diet and air pollution) may precipitate clinical relapses and recurrence. Potential biological mechanisms include: increasing thrombotic tendency, imbalances in prostaglandin synthesis, alterations in the composition of gut microbiota, and mucosal damage causing increased permeability
Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer
The zoonotic origin of the COVID-19 pandemic virus highlights the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected that SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2–8 months, disseminating across hundreds of kilometers. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only three-times faster in white-tailed deer compared to the rate observed in humans but also driven by different mutational biases and selection pressures. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal models using white-tailed deer origin viruses. Still, SARS-CoV-2 has transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock
The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2
Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October–8 December), while the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans prior to November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events
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