Biology subjects

Rannala, B.

Publications and source records attributed to Rannala, B..

8 recordsLinked to original sources

bioRxiv2025

Improved Bayesian inference of hybrids using genome sequences

A Bayesian hybrid inference method is developed which infers hybrids and back-crosses across two generations using sampled genomes from two populations. The method improves on that of Chakraborty and Rannala (2023) by accounting for uncertainty of population haplotype frequencies and correctly marginalizing over haplotypes while still modeling linkage and recombination across the genome. In analyses of simulated data the new method produced posterior probabilities nearly identical to the method of Chakraborty and Rannala (2023) when sample sizes were large. For small sample sizes, posterior probabilities produced by the new method tended to be lower as expected since it accounts for additional uncertainties of population haplotype frequencies. Statistical performance of the new method as measured by the ROC (Receiver Operation Characteristic) curve, appears equivalent to that of Chakraborty and Rannala (2023). The new method is applied to three recently published datasets for populations of kiwifruit (genus Actinidia), plateau fence lizard (Sceloporus tristichus) and puma (Puma concolor).

genetics↗

bioRxiv2025

Mutation ages and population origins inferred from genomes in structured populations

Inferring the time of origin (age) of mutations is an old question in population genetics and inferring their population of origin has become of particular interest with the sequencing of the Neanderthal genome. However, existing methods to infer mutation ages and populations of origin do not explicitly consider population structure, migration rates, and divergence times, which may bias estimates, and it is unclear how to even apply single-population estimators to structured populations. We develop a method to jointly estimate the time and population of origin of a mutation (as well as the ancestral and derived states) in a structured population using population genomic data and examine its statistical performance using simulations. Results indicate that mutation age and population of origin can be quite uncertain, even with long sequences or many samples, but this uncertainty is accurately captured using credible intervals/sets. The ancestral nucleotide state is relatively easy to infer. We apply our method to whole genome data from the 1000 Genomes Project, analyzing seven SNP mutations from six genes associated with human skin pigmentation for populations from Great Britain, China, and Kenya. Our results partially support previous conclusions, with the putative ancestral alleles from the literature matching our inferences, while the mutation age estimates only overlap in some cases. Furthermore, there are non-trivial posterior probabilities of recurrence for three of the mutations.

evolutionary biology↗

bioRxiv2022

PrioriTree: a utility for improving phylodynamic analyses in BEAST

SummaryPhylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models--which involve many parameters that must be inferred from minimal information--is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for--and summarize output from--BEAST analyses for performing robust Bayesian inference, data-cloning analyses, and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets. Availability and ImplementationPrioriTree is distributed as an R package available at https://github.com/jsigao/prioritree, with a comprehensive user manual provided at https://bookdown.org/jsigao/prioritree_manual/. Contactjsigao@ucdavis.edu

evolutionary biology↗

bioRxiv2022

Bayesian Phylogenetic Inference of HIV Latent Lineage Ages Using Serial Sequences

HIV evolves rapidly within individuals, allowing phylogenetic studies to infer the history of viral lineages on short time scales. Latent HIV sequences are an exception to this rapid evolution, as their transcriptional inactivity leads to negligible mutation rates in comparison to non-latent HIV lineages. Latent sequences are of keen interest as they provide insight into the formation, persistence, and decay of the latent reservoir. Different mutation rates in latent versus active HIV lineages generate potential information about the times at which sequences entered the latent reservoir. A Bayesian phylogenetic method is developed to infer integration times of latent HIV sequences. The method uses informative priors to incorporate biologically sensible bounds on inferences (such as requiring sequences to become latent before being sampled) that many existing methods lack. A new simulation method is also developed, based on widely-used epidemiological models of within-host viral dynamics, and applied to evaluate the new method, showing that point estimates and credible intervals are often more accurate by comparison with existing methods. Accurate estimates of latent integration dates are crucial in dating the formation of the latent reservoir relative to key events during HIV infection, such as the initiation of antiretroviral treatment. The method is applied to analyze publicly-available sequence data from 4 HIV patients, providing new insights regarding the temporal pattern of latent HIV integration events. Significance StatementPhylogenetic studies are increasingly being used to characterize within-host HIV evolution and the temporal dynamics of the HIV latent reservoir in particular, which is not targeted by current treatment methods and thus prevents a cure for HIV. Phylogenetic methods currently used to analyze HIV sequences suffer from conceptual and statistical problems that degrade their performance. A new Bayesian inference method to estimate the ages of latent sequences and a new simulation method based on within-host viral dynamics are developed. The new inference method outperforms existing methods, particularly in characterizing uncertainty. Understanding how the latent HIV reservoir changes overtime will allow researchers to better understand the nature of HIV infection and develop strategies for a cure.

evolutionary biology↗

bioRxiv2021

Phylodynamic insights on the early spread of the COVID-19 pandemic and the efficacy of intervention measures

We performed phylodynamic analyses of all available SARS-CoV-2 genomes from the early phase of the COVID-19 pandemic--combined with a novel dataset on contemporary global air-travel volume--to assess the efficacy of public-health measures on viral geographic spread. Globally, viral dispersal rates are significantly correlated with air-travel volume, and widespread international air-travel bans imposed against China by early February coincide with a significant reduction in geographic viral spread. In North America, the efficacy of this travel ban was temporary, possibly due to the lack of both containment measures against other infected regions and domestic mitigation measures. By contrast, in China, domestic mitigation measures were correlated with a long-term reduction in viral spread, despite repeated international introductions. Our study supports a role for both targeted international containment and domestic mitigation measures as critical components of a more comprehensive public-health strategy to mitigate future outbreaks caused by the emergence of novel SARS-CoV-2 variants. One sentence summaryPhylodynamic analyses reveal that variation in rates of early geographic spread of COVID-19 are correlated with intervention measures.

evolutionary biology↗

bioRxiv2019

The Impact of Cross-Species Gene Flow on Species Tree Estimation

Recent analyses of genomic sequence data suggest cross-species gene flow is common in both plants and animals, posing challenges to species tree inference. We examine the levels of gene flow needed to mislead species tree estimation with three species and either episodic introgressive hybridization or continuous migration between an outgroup and one ingroup species. Several species tree estimation methods are examined, including the majority-vote method based on the most common gene tree topology (with either the true or reconstructed gene trees used), the UPGMA method based on the average sequence distances (or average coalescent times) between species, and the full-likelihood method based on multi-locus sequence data. Our results suggest that the majority-vote method is more robust to gene flow than the UPGMA method and both are more robust than likelihood assuming a multispecies coalescent (MSC) model with no cross-species gene flow. A small amount of introgression or migration can mislead species tree methods if the species diverged through speciation events separated by short time intervals. Estimates of parameters under the MSC with gene flow suggest the Anopheles gambia African mosquito species complex is an example where gene flow greatly impacts species phylogeny.

evolutionary biology↗

bioRxiv2019

A Bayesian implementation of the multispecies coalescent model with introgression for comparative genomic analysis

Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here we implement the multispecies-coalescent-with-introgression (MSci) model, an extension of the multispecies-coalescent (MSC) model to incorporate introgression, in our Bayesian Markov chain Monte Carlo (MCMC) program BPP. The MSci model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Re-analysis of datasets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

evolutionary biology↗

bioRxiv2018

The Spectre of Too Many Species

Recent simulation studies examining the performance of Bayesian species delimitation as implemented in the BPP program have suggested that BPP may detect population splits but not species divergences and that it tends to over-split when data of many loci are analyzed. Here we confirm several of these results and provide their mathematical justifications. We point out that the distinction between population and species splits made in the protracted speciation model has no influence on the generation of gene trees and sequence data, which explains why no method can use such data to distinguish between population splits and speciation. We suggest that the the protracted speciation model is unrealistic and its mechanism for assigning species status contradicts prevailing taxonomic practice. We confirm the suggestion, based on simulation, that in the case of speciation with gene flow, Bayesian model selection as implemented in BPP tends to detect population splits when the amount of data (the number of loci) increases so over-splitting is a legitimate concern. We discuss the use of a recently proposed empirical genealogical divergence index (gdi) for species delimitation and illustrate that parameter estimates produced by a full likelihood analysis as implemented in BPP provide much more reliable inference under the gdi than the approximate method PHRAPL. We suggest that the Bayesian model-selection approach is useful for identifying sympatric cryptic species while Bayesian parameter estimation under the multispecies coalescent can be used to implement empirical criteria for determining species status among allopatric populations.

evolutionary biology↗