Evidence map›Paper›PMID 35018888›Full record

ArticleeLife2022

The Mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates.

Lucie A Bergeron, Søren Besenbacher, Tychele Turner, Cyril J Versoza, Richard J Wang, Alivia Lee Price, Ellie Armstrong, Meritxell Riera, Jedidiah Carlson, Hwei-Yen Chen and 10 more

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Article
  2. Article
  3. EstimatingbioRxiv : the preprint server for biology · 2026
    Article
  4. Review
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  6. Article
  7. Article
  8. Article
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  12. Methylation-associated mutagenesis underlies variation in the mutation spectrum across eukaryotes.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  13. Article
  14. Article
  15. Ubiquitin-conjugating Enzymes in Cancer.International journal of biological sciences · 2026
    Review
  16. What sets the mutation rate of a cell type in an animal species?bioRxiv : the preprint server for biology · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Lucie A BergeronSection for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0003-1877-1690
Søren BesenbacherDepartment of Molecular Medicine, Aarhus University, Aarhus, Denmark.ORCID https://orcid.org/0000-0003-1455-1738
Tychele TurnerDepartment of Genetics, Washington University School of Medicine, St. Louis, United States.
Cyril J VersozaCenter for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, United States.
Richard J WangDepartment of Biology and Department of Computer Science, Indiana University, Bloomington, United States.
Alivia Lee PriceSection for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Ellie ArmstrongDepartment of Biology, Stanford University, Stanford, United States.ORCID https://orcid.org/0000-0001-7107-6318
Meritxell RieraBioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
Jedidiah CarlsonDepartment of Genome Sciences, University of Washington, Computational Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States.
Hwei-Yen ChenSection for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Matthew W HahnDepartment of Biology and Department of Computer Science, Indiana University, Bloomington, United States.ORCID https://orcid.org/0000-0002-5731-8808
Kelley HarrisDepartment of Genome Sciences, University of Washington, Computational Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States.ORCID https://orcid.org/0000-0003-0302-2523
April Snøfrid KleppeDepartment of Molecular Medicine, Aarhus University, Aarhus, Denmark.ORCID https://orcid.org/0000-0001-7866-3056
Elora H López-NandamCalifornia Academy of Sciences, San Francisco, United States.
Priya MoorjaniDepartment of Molecular and Cell Biology, Center for Computational Biology, University of California, Berkeley, Berkeley, United States.
Susanne P PfeiferCenter for Evolution and Medicine, Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, United States.ORCID https://orcid.org/0000-0003-1378-2913
George P TileyDepartment of Biology, Duke University, Durham, United States.ORCID https://orcid.org/0000-0003-0053-0207
Anne D YoderDepartment of Biology, Duke University, Durham, United States.
Guojie ZhangSection for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Mikkel H SchierupBioinformatics Research Centre, Aarhus University, Aarhus, Denmark.ORCID https://orcid.org/0000-0002-5028-1790

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon,' a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

Indexed as

Genetic TechniquesGerm-Line MutationMutation RateAnimalsGerm CellsLaboratoriesMacaca mulattaPedigreeReference Standardscomputational pipelineevolutionary biologygeneticsgenomicsmutation ratengs analysispedigree-based estimationrhesus macaque

Identifiers

PMID35018888
PMCPMC8830884

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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.