{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:41:57Z","timestamp":1767181317184,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010897","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000}}],"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01-GM-131404"],"award-info":[{"award-number":["R01-GM-131404"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present\n                    <jats:monospace>adaPop<\/jats:monospace>\n                    , a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010897","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T13:29:27Z","timestamp":1679318967000},"page":"e1010897","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["adaPop: Bayesian inference of dependent population dynamics in coalescent models"],"prefix":"10.1371","volume":"19","author":[{"given":"Lorenzo","family":"Cappello","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5210-2004","authenticated-orcid":true,"given":"Jaehee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4501-7378","authenticated-orcid":true,"given":"Julia A.","family":"Palacios","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"issue":"5","key":"pcbi.1010897.ref001","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1093\/molbev\/msi103","article-title":"Bayesian coalescent inference of past population dynamics from molecular sequences","volume":"22","author":"AJ Drummond","year":"2005","journal-title":"Molecular Biology and Evolution"},{"issue":"4","key":"pcbi.1010897.ref002","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1534\/genetics.109.106021","article-title":"Phylodynamics of infectious disease epidemics","volume":"183","author":"EM Volz","year":"2009","journal-title":"Genetics"},{"issue":"4","key":"pcbi.1010897.ref003","doi-asserted-by":"crossref","first-page":"e1006650","DOI":"10.1371\/journal.pcbi.1006650","article-title":"BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis","volume":"15","author":"R Bouckaert","year":"2019","journal-title":"PLOS Computational Biology"},{"issue":"6526","key":"pcbi.1010897.ref004","doi-asserted-by":"crossref","first-page":"eaah6266","DOI":"10.1126\/science.aah6266","article-title":"Phylodynamics for cell biologists","volume":"371","author":"T Stadler","year":"2021","journal-title":"Science"},{"issue":"2","key":"pcbi.1010897.ref005","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1214\/22-STS853","article-title":"Statistical challenges in tracking the evolution of SARS-CoV-2","volume":"37","author":"L Cappello","year":"2022","journal-title":"Statistical Science"},{"issue":"1","key":"pcbi.1010897.ref006","doi-asserted-by":"crossref","first-page":"veac045","DOI":"10.1093\/ve\/veac045","article-title":"Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications","volume":"8","author":"LA Featherstone","year":"2022","journal-title":"Virus Evolution"},{"issue":"8","key":"pcbi.1010897.ref007","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1093\/molbev\/msw064","article-title":"Phylodynamics with migration: A computational framework to quantify population structure from genomic data","volume":"33","author":"D K\u00fchnert","year":"2016","journal-title":"Molecular Biology and Evolution"},{"issue":"11","key":"pcbi.1010897.ref008","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1093\/molbev\/msx186","article-title":"The structured coalescent and its approximations","volume":"34","author":"NF M\u00fcller","year":"2017","journal-title":"Molecular Biology and Evolution"},{"key":"pcbi.1010897.ref009","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1146\/annurev-cancerbio-060220-014137","article-title":"Molecular heterogeneity and evolution in breast cancer","volume":"5","author":"JL Caswell-Jin","year":"2021","journal-title":"Annual Review of Cancer Biology"},{"issue":"3","key":"pcbi.1010897.ref010","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/0304-4149(82)90011-4","article-title":"The coalescent","volume":"13","author":"JFC Kingman","year":"1982","journal-title":"Stochastic Processes and Their Applications"},{"issue":"1","key":"pcbi.1010897.ref011","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1534\/genetics.108.092460","article-title":"Extensions of the coalescent effective population size","volume":"181","author":"J Wakeley","year":"2009","journal-title":"Genetics"},{"issue":"3","key":"pcbi.1010897.ref012","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1111\/j.1755-0998.2011.02988.x","article-title":"Skyline-plot methods for estimating demographic history from nucleotide sequences","volume":"11","author":"SYW Ho","year":"2011","journal-title":"Molecular Ecology Resources"},{"issue":"7","key":"pcbi.1010897.ref013","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1093\/molbev\/msn090","article-title":"Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics","volume":"25","author":"VN Minin","year":"2008","journal-title":"Molecular Biology and Evolution"},{"issue":"3","key":"pcbi.1010897.ref014","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1093\/molbev\/mss265","article-title":"Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci","volume":"30","author":"MS Gill","year":"2013","journal-title":"Molecular Biology and Evolution"},{"issue":"4","key":"pcbi.1010897.ref015","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1093\/sysbio\/syy007","article-title":"Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance","volume":"67","author":"EM Volz","year":"2018","journal-title":"Systematic Biology"},{"issue":"3","key":"pcbi.1010897.ref016","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1111\/biom.13276","article-title":"Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories","volume":"76","author":"JR Faulkner","year":"2020","journal-title":"Biometrics"},{"issue":"1","key":"pcbi.1010897.ref017","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/biom.12003","article-title":"Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies","volume":"69","author":"JA Palacios","year":"2013","journal-title":"Biometrics"},{"key":"pcbi.1010897.ref018","doi-asserted-by":"crossref","unstructured":"Adams RP, Murray I, MacKay DJ. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. In: Proceedings of the 26th Annual International Conference on Machine Learning. 2009;9\u201316.","DOI":"10.1145\/1553374.1553376"},{"issue":"1","key":"pcbi.1010897.ref019","doi-asserted-by":"crossref","first-page":"vey016","DOI":"10.1093\/ve\/vey016","article-title":"Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10","volume":"4","author":"MA Suchard","year":"2018","journal-title":"Virus Evolution"},{"issue":"1","key":"pcbi.1010897.ref020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v076.i01","article-title":"Stan: A probabilistic programming language","volume":"76","author":"B Carpenter","year":"2017","journal-title":"Journal of Statistical Software"},{"issue":"20","key":"pcbi.1010897.ref021","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.1093\/bioinformatics\/btv378","article-title":"An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics","volume":"31","author":"S Lan","year":"2015","journal-title":"Bioinformatics"},{"issue":"3","key":"pcbi.1010897.ref022","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11222-012-9373-1","article-title":"Split Hamiltonian Monte Carlo","volume":"24","author":"B Shahbaba","year":"2014","journal-title":"Statistics and Computing"},{"issue":"2","key":"pcbi.1010897.ref023","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1111\/j.1467-9868.2008.00700.x","article-title":"Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations","volume":"71","author":"H Rue","year":"2009","journal-title":"Journal of the Royal Statistical Society: Series B"},{"key":"pcbi.1010897.ref024","unstructured":"Palacios JA, Minin VN. Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics. In: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence. 2012;726\u2013735."},{"issue":"101","key":"pcbi.1010897.ref025","doi-asserted-by":"crossref","first-page":"20140945","DOI":"10.1098\/rsif.2014.0945","article-title":"Sampling through time and phylodynamic inference with coalescent and birth & death models","volume":"11","author":"EM Volz","year":"2014","journal-title":"Journal of the Royal Society Interface"},{"issue":"2","key":"pcbi.1010897.ref026","first-page":"191","article-title":"Geostatistical inference under preferential sampling","volume":"59","author":"PJ Diggle","year":"2010","journal-title":"Journal of the Royal Statistical Society: Series C"},{"issue":"3","key":"pcbi.1010897.ref027","doi-asserted-by":"crossref","first-page":"e1004789","DOI":"10.1371\/journal.pcbi.1004789","article-title":"Quantifying and mitigating the effect of preferential sampling on phylodynamic inference","volume":"12","author":"MD Karcher","year":"2016","journal-title":"PLOS Computational Biology"},{"issue":"10","key":"pcbi.1010897.ref028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1007774","article-title":"Estimating effective population size changes from preferentially sampled genetic sequences","volume":"16","author":"MD Karcher","year":"2020","journal-title":"PLOS Computational Biology"},{"issue":"8","key":"pcbi.1010897.ref029","doi-asserted-by":"crossref","first-page":"2414","DOI":"10.1093\/molbev\/msaa016","article-title":"Jointly inferring the dynamics of population size and sampling intensity from molecular sequences","volume":"37","author":"KV Parag","year":"2020","journal-title":"Molecular Biology and Evolution"},{"key":"pcbi.1010897.ref030","first-page":"1","article-title":"Adaptive preferential sampling in phylodynamics with an application to SARS-CoV-2","volume":"0","author":"L Cappello","year":"2021","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"pcbi.1010897.ref031","doi-asserted-by":"crossref","DOI":"10.1201\/9780429258411","volume-title":"Bayesian Data Analysis","author":"A Gelman","year":"1995"},{"issue":"1","key":"pcbi.1010897.ref032","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1214\/17-BA1050","article-title":"Locally adaptive smoothing with Markov random fields and shrinkage priors","volume":"13","author":"JR Faulkner","year":"2018","journal-title":"Bayesian Analysis"},{"issue":"1","key":"pcbi.1010897.ref033","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.cell.2020.11.020","article-title":"Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity","volume":"184","author":"E Volz","year":"2021","journal-title":"Cell"},{"issue":"6538","key":"pcbi.1010897.ref034","doi-asserted-by":"crossref","DOI":"10.1126\/science.abg3055","article-title":"Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England","volume":"372","author":"NG Davies","year":"2021","journal-title":"Science"},{"key":"pcbi.1010897.ref035","doi-asserted-by":"crossref","first-page":"577","DOI":"10.2307\/1913267","article-title":"Identification in parametric models","volume":"39","author":"TJ Rothenberg","year":"1971","journal-title":"Econometrica"},{"key":"pcbi.1010897.ref036","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006"},{"key":"pcbi.1010897.ref037","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511800474","volume-title":"Algebraic Geometry and Statistical Learning Theory","author":"S Watanabe","year":"2009"},{"issue":"1","key":"pcbi.1010897.ref038","doi-asserted-by":"crossref","first-page":"e8915","DOI":"10.1371\/journal.pone.0008915","article-title":"Parameter identifiability and redundancy: theoretical considerations","volume":"5","author":"MP Little","year":"2010","journal-title":"PLOS One"},{"issue":"5","key":"pcbi.1010897.ref039","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1093\/sysbio\/syz008","article-title":"Robust design for coalescent model inference","volume":"68","author":"KV Parag","year":"2019","journal-title":"Systematic Biology"},{"issue":"3","key":"pcbi.1010897.ref040","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1093\/genetics\/155.3.1429","article-title":"An integrated framework for the inference of viral population history from reconstructed genealogies","volume":"155","author":"OG Pybus","year":"2000","journal-title":"Genetics"},{"issue":"5","key":"pcbi.1010897.ref041","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/sysbio\/syaa016","article-title":"A multitype birth\u2013death model for Bayesian inference of lineage-specific birth and death rates","volume":"69","author":"J Barido-Sottani","year":"2020","journal-title":"Systematic Biology"},{"issue":"7","key":"pcbi.1010897.ref042","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/s41579-021-00573-0","article-title":"SARS-CoV-2 variants, spike mutations and immune escape","volume":"19","author":"WT Harvey","year":"2021","journal-title":"Nature Reviews Microbiology"},{"issue":"20","key":"pcbi.1010897.ref043","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.1016\/j.cell.2021.09.010","article-title":"COVID-19 vaccines: Keeping pace with SARS-CoV-2 variants","volume":"184","author":"M Cevik","year":"2021","journal-title":"Cell"},{"issue":"11","key":"pcbi.1010897.ref044","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1038\/s41564-020-0770-5","article-title":"A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology","volume":"5","author":"A Rambaut","year":"2020","journal-title":"Nature Microbiology"},{"issue":"13","key":"pcbi.1010897.ref045","first-page":"30494","article-title":"GISAID: Global initiative on sharing all influenza data\u2013from vision to reality","volume":"22","author":"Y Shu","year":"2017","journal-title":"Eurosurveillance"},{"issue":"7883","key":"pcbi.1010897.ref046","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41586-021-03944-y","article-title":"SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion","volume":"599","author":"P Mlcochova","year":"2021","journal-title":"Nature"},{"issue":"11","key":"pcbi.1010897.ref047","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1001\/jama.2021.14811","article-title":"Confronting the Delta Variant of SARS-CoV-2, Summer 2021","volume":"326","author":"C del Rio","year":"2021","journal-title":"JAMA"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010897","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010897","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T14:00:08Z","timestamp":1680184808000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010897"}},"subtitle":[],"editor":[{"given":"Jennifer A.","family":"Flegg","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,3,20]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010897","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,20]]}}}