Evidence map›Paper›PMID 34186053›Full record

ArticleTheoretical population biology2021

The distribution of waiting distances in ancestral recombination graphs.

Yun Deng, Yun S Song, Rasmus Nielsen

Abstract read
In one paragraph

Article in Theoretical population biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

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25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Review
  6. A general framework for branch length estimation in Ancestral Recombination Graphs.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
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  9. Article
  10. Article
  11. Article
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  15. Article
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  17. Article
  18. A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024
    Article
  19. Estimating evolutionary and demographic parameters via ARG-derived IBD.bioRxiv : the preprint server for biology · 2024
    Article
  20. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Yun DengCenter for Computational Biology, University of California, Berkeley, CA 94720, United States of America. Electronic address: yun_deng@berkeley.edu.
Yun S SongDepartment of Statistics, University of California, Berkeley, CA 94720, United States of America; Computer Science Division, University of California, Berkeley, CA 94720, United States of America; Chan Zuckerberg Biohub, San Francisco, CA 94158, United States of America.
Rasmus NielsenDepartment of Statistics, University of California, Berkeley, CA 94720, United States of America; Department of Integrative biology, University of California, Berkeley, CA 94720, United States of America. Electronic address: rasmus_nielsen@berkeley.edu.

Funding

Robust and efficient statistical inference methods for genomicsR35GM134922 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2020 to 2024
$2.0M
Enabling Precision Genomics Using Adaptive VariationR01GM138634 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI NIELSEN, RASMUS · 2020 to 2023
$1.7M
NIGMS NIH HHS R01 GM138634NIGMS NIH HHS R35 GM134922
6 · The paper itself

Abstract

The ancestral recombination graph (ARG) contains the full genealogical information of the sample, and many population genetic inference problems can be solved using inferred or sampled ARGs. In particular, the waiting distance between tree changes along the genome can be used to make inference about the distribution and evolution of recombination rates. To this end, we here derive an analytic expression for the distribution of waiting distances between tree changes under the sequentially Markovian coalescent model and obtain an accurate approximation to the distribution of waiting distances for topology changes. We use these results to show that some of the recently proposed methods for inferring sequences of trees along the genome provide strongly biased distributions of waiting distances. In addition, we provide a correction to an undercounting problem facing all available ARG inference methods, thereby facilitating the use of ARG inference methods to estimate temporal changes in the recombination rate.

Indexed as

Models, GeneticRecombination, GeneticAlgorithmsGenomeMarkov ChainsPhylogenyAncestral recombination graphRecombination evolutionSequentially Markovian coalescentTree inference methods

Identifiers

PMID34186053
PMCPMC8556732

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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.