Evidence map›Paper›PMID 42391510›Full record

ArticleMolecular biology and evolution2026

Severe bottleneck of ancient Homo populations: insights from computational modeling and relevant fossil evidence.

Sumei Zhou, Yiwen Zhou, Fabio Di Vincenzo, Giorgio Manzi, Wangjie Hu, Ziqian Hao, Zhen Shao, Yi-Hsuan Pan, Haipeng Li

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Sumei ZhouShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0003-4290-6882
Yiwen ZhouShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0009-0007-8243-3051
Fabio Di VincenzoDepartment of Environmental Biology, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-2830-6971
Giorgio ManziDepartment of Environmental Biology, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-8611-1371
Wangjie HuDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-4102-4191
Ziqian HaoCollege of Medical Information and Artificial Intelligence, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.ORCID 0000-0002-5335-1918
Zhen ShaoShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0002-4934-4360
Yi-Hsuan PanKey Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, Shanghai, China.ORCID 0000-0001-5156-9887
Haipeng LiShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0001-7856-4488

Funding

CAS Youth Interdisciplinary TeamEastern Talent Plan Leading ProjectNational Natural Science Foundation of China 31100273National Natural Science Foundation of China 32270674
6 · The paper itself

Abstract

Reconstructing ancient population size history is essential for understanding the evolutionary origin of Homo sapiens. We recently developed the fast infinitesimal time coalescent process (FitCoal) and detected a severe population bottleneck occurring approximately 930 thousand years ago. However, two recent studies compared FitCoal and mushi and concluded that the severe bottleneck is a statistical artifact. In this study, we compared the two methods against a benchmark of 10 billion msprime coalescent simulations. We demonstrate that FitCoal achieves both superior speed and accuracy in expected site frequency spectrum (SFS) estimation. Analyses of simulated datasets confirmed that FitCoal reliably recovers the bottleneck, whereas mushi fails under identical conditions. Independent fossil and paleoclimate evidence is consistent with the timing and evolutionary impact of this bottleneck, including associations with hominin dispersals, speciation events, and a subsequent increase in brain size. These findings refine the demographic history of Homo during the Pleistocene and highlight the importance of high-precision SFS computation for revealing critical evolutionary transitions that shaped modern human ancestry.

Indexed as

HominidaeModels, GeneticAnimalsBiological EvolutionComputer SimulationFossilsHumansPopulation Densitydemographic inferenceEarly-Middle PleistoceneFitCoalSFS

Identifiers

PMID42391510
PMCPMC13367442

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