{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:18:32Z","timestamp":1787851112306,"version":"build-2784847793"},"posted":{"date-parts":[[2026,8,14]]},"group-title":"Evolutionary Biology","reference-count":49,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"award":["R01HG014005"],"award-info":[{"award-number":["R01HG014005"]}],"id":[{"id":"https:\/\/ror.org\/00baak391","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Many questions in population genetics require reconstructing evolutionary history through time, such as inferring how population structure has changed throughout the past. Yet, many existing approaches have only an implicit temporal component, using quantities such as allele frequency or haplotype length as rough proxies for age. Recent advances in the inference of Ancestral Recombination Graphs (ARGs) have made it possible to estimate the entire sequence of local genealogies along the genome. These genealogies explicitly encode how samples are related to each other at different time points in the past, enabling the inference of how population structure has changed over time. To this end, recent work has used ARGs to define time-stratified versions of widely-used population genetics summary statistics in an attempt to capture the population structure present within a particular time window. Here, we show that naive approaches result in statistics that cannot isolate the population structure present solely within the targeted time window. To address this problem, we introduce a framework of\n                  <jats:bold>coalescent-based time-stratified statistics<\/jats:bold>\n                  , which use coalescence probabilities to partition classical summary statistics into interval-specific contributions. Using coalescent simulations, we demonstrate that these statistics accurately isolate population structure at different temporal depths and avoid spurious signals. Our results highlight the necessity of integrating coalescent theory into ARG-based temporal analyses and provide a principled and practical foundation for studying the dynamics of population structure through time.\n                <\/jats:p>","DOI":"10.64898\/2026.08.11.744210","type":"posted-content","created":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T02:25:13Z","timestamp":1786760713000},"source":"Crossref","is-referenced-by-count":0,"title":["Coalescent-Based Time-Stratified Statistics Reveal Population Structure Dynamics using the Ancestral Recombination Graph"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2802-2546","authenticated-orcid":false,"given":"Yun","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Genetics, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan K.","family":"Pritchard","sequence":"additional","affiliation":[{"name":"Department of Genetics, Stanford University"},{"name":"Department of Biology, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey P.","family":"Spence","sequence":"additional","affiliation":[{"name":"Institute for Human Genetics, University of California, San Francisco"},{"name":"Department of Epidemiology & Biostatistics, University of California, San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026082709251052000_2026.08.11.744210v2.1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.54967"},{"key":"2026082709251052000_2026.08.11.744210v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2015.07.012"},{"key":"2026082709251052000_2026.08.11.744210v2.3","doi-asserted-by":"crossref","unstructured":"Yun Deng , Rasmus Nielsen , and Yun S Song . Robust and accurate bayesian inference of genome-wide genealogies for hundreds of genomes. Nature Genetics, pages 1\u201312, 2025a.","DOI":"10.1101\/2024.03.16.585351"},{"key":"2026082709251052000_2026.08.11.744210v2.4","doi-asserted-by":"crossref","unstructured":"Yun Deng , Yun S Song , and Rasmus Nielsen . A general framework for branch length estimation in ancestral recombination graphs. bioRxiv, pages 2025\u201302, 2025b.","DOI":"10.1101\/2025.02.14.638385"},{"key":"2026082709251052000_2026.08.11.744210v2.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2022.03.016"},{"key":"2026082709251052000_2026.08.11.744210v2.6","doi-asserted-by":"publisher","DOI":"10.1126\/science.aag0776"},{"issue":"1","key":"2026082709251052000_2026.08.11.744210v2.7","doi-asserted-by":"crossref","first-page":"7945","DOI":"10.1038\/s41467-023-43522-6","article-title":"Haplotype-based inference of recent effective population size in modern and ancient DNA samples","volume":"14","year":"2023","journal-title":"Nature Communications"},{"key":"2026082709251052000_2026.08.11.744210v2.8","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/iyac131"},{"key":"2026082709251052000_2026.08.11.744210v2.9","doi-asserted-by":"publisher","DOI":"10.1126\/science.1188021"},{"key":"2026082709251052000_2026.08.11.744210v2.10","doi-asserted-by":"publisher","DOI":"10.1016\/0040-5809(81)90016-2"},{"key":"2026082709251052000_2026.08.11.744210v2.11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2609-116"},{"issue":"10","key":"2026082709251052000_2026.08.11.744210v2.12","doi-asserted-by":"crossref","first-page":"e1000695","DOI":"10.1371\/journal.pgen.1000695","article-title":"Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data","volume":"5","year":"2009","journal-title":"PLoS genetics"},{"key":"2026082709251052000_2026.08.11.744210v2.13","doi-asserted-by":"publisher","DOI":"10.1038\/nature14317"},{"key":"2026082709251052000_2026.08.11.744210v2.14","doi-asserted-by":"publisher","DOI":"10.1016\/0040-5809(83)90013-8"},{"key":"2026082709251052000_2026.08.11.744210v2.15","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/18.2.337"},{"key":"2026082709251052000_2026.08.11.744210v2.16","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06705-1"},{"key":"2026082709251052000_2026.08.11.744210v2.17","unstructured":"Ben Jeffery , Yan Wong , Kevin Thornton , Georgia Tsambos , Gertjan Bisschop , Yun Deng , E Castedo Ellerman , Thomas B Forest , Halley Fritze , Daniel Goldstein , et al. 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