Evidence map›Paper›PMID 41646655›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Synonymous substitution rate slowdown preceding the emergence of SARS-CoV-2 variants and during persistent infections.

Jennifer L Havens, Karthik Gangavarapu, Jade C Wang, Faten Taki, Elizabeth Luoma, Jonathan E Pekar, Helly Amin, Steve Di Lonardo, Enoma Omoregie, Scott Hughes and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Jennifer L HavensBioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0003-3095-5900
Karthik GangavarapuDepartment of Translational Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.
Jade C WangBioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.
Faten TakiNew York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Elizabeth LuomaBureau of Communicable Disease, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Jonathan E PekarBioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0003-0977-2886
Helly AminNew York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Steve Di LonardoNew York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Enoma OmoregieNew York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Scott HughesNew York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Kristian G AndersenDepartment of Translational Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA.
Tetyana I VasylyevaDepartment of Population Health and Disease Prevention, University of California Irvine, Irvine, California, USA.
Marc A SuchardDepartment of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Joel O WertheimDepartment of Medicine, University of California San Diego, La Jolla, CA 92093, USA.

Funding

Technology CoreU19AI135995 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI Laura D. Hughes · 2018 to 2026
$32.0M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Automation and Evaluation of Real-Time Transmission Network-Based HIV Prevention Services in New York CityR01AI135992 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WERTHEIM, JOEL OKRENT · 2018 to 2022
$3.6M
Statistical Innovation to Integrate Sequences and Phenotypes for Scalable Phylodynamic InferenceR01AI153044 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Marc A. Suchard · 2021 to 2026
$2.4M
NIAID NIH HHS R01 AI135992NIAID NIH HHS R01 AI153044NIAID NIH HHS U19 AI135995NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

The emergence of variants has shaped the COVID-19 pandemic. The lack of directly observed precursors to these variants has led to proposals that variants emerge from either persistent infections, transmission in non-human animal populations after reverse-zoonosis, or cryptic transmission in the human population. We investigated the origin of variants by analyzing the molecular clock and rate of nonsynonymous and synonymous substitutions in SARS-CoV-2 circulating in human population, persistently infected individuals, non-human animals, and along variant stems: the branches preceding emergence of SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Epsilon, Iota, B.1.637, Mu, and Omicron: BA.1, BA.2/BA.4/BA.5). Along the variant stems we find evidence for an acceleration in the non-synonymous substitution rate, as compared with non-synonymous substitution rate along the branches that represent the genetic diversity of circulating virus. We also find evidence for a slowdown in the synonymous substitution rate preceding the emergence of multiple named variants (e.g., Beta, Delta, Iota, Mu, Omicron BA.1); a similar pattern was observed in some individuals with persistent infections, suggesting that the viral replication rate can slow down during persistent infection. However, the synonymous rate slowdown was not observed for all variants, with some exhibiting an increase in synonymous substitution rates preceding their emergence compared with typical viral transmission (e.g., Alpha, Epsilon). The similarity in evolutionary dynamics preceding some variant emergence and during persistent infections supports the hypothesis that persistent infections were the likely source of many COVID-19 variants.

Identifiers

PMID41646655
PMCPMC12870713

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.