Evidence map›Paper›PMID 32152045›Full record

ArticleGenetics2020

Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection.

Parul Johri, Brian Charlesworth, Jeffrey D Jensen

Abstract read
In one paragraph

Article in Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers.

0numbers the graph read from it
0cells of the map it votes in
127citing 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

127 citing papers in PubMed.

  1. Article
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  4. Review
  5. Article
  6. Evolution of quantitative traits with background selection.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026
    Article
  14. ThebioRxiv : the preprint server for biology · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Towards an evolutionary baseline model ofbioRxiv : the preprint server for biology · 2025
    Article

67 more citing papers are in PubMed but not listed here.

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

3 authors.

Parul JohriSchool of Life Sciences, Arizona State University, Tempe, Arizona 85287 pjohri1@asu.edu Jeffrey.D.Jensen@asu.edu.
Brian CharlesworthInstitute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3FL, United Kingdom.ORCID 0000-0002-2706-355X
Jeffrey D JensenSchool of Life Sciences, Arizona State University, Tempe, Arizona 85287 pjohri1@asu.edu Jeffrey.D.Jensen@asu.edu.

Funding

Developing novel statistics and coalescent approaches for the improved study of virus evolution R01GM135899 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI JENSEN, JEFFREY D · 2019 to 2020
$367k
NIGMS NIH HHS R01 GM135899
6 · The paper itself

Abstract

The question of the relative evolutionary roles of adaptive and nonadaptive processes has been a central debate in population genetics for nearly a century. While advances have been made in the theoretical development of the underlying models, and statistical methods for estimating their parameters from large-scale genomic data, a framework for an appropriate null model remains elusive. A model incorporating evolutionary processes known to be in constant operation, genetic drift (as modulated by the demographic history of the population) and purifying selection, is lacking. Without such a null model, the role of adaptive processes in shaping within- and between-population variation may not be accurately assessed. Here, we investigate how population size changes and the strength of purifying selection affect patterns of variation at "neutral" sites near functional genomic components. We propose a novel statistical framework for jointly inferring the contribution of the relevant selective and demographic parameters. By means of extensive performance analyses, we quantify the utility of the approach, identify the most important statistics for parameter estimation, and compare the results with existing methods. Finally, we reanalyze genome-wide population-level data from a Zambian population of

Indexed as

Evolution, MolecularModels, GeneticSelection, GeneticAnimalsDrosophila melanogasterGenetic Variationapproximate Bayesian computationbackground selectiondemographic inferencedistribution of fitness effects

Identifiers

PMID32152045
PMCPMC7198275

What OpenQuestion holds

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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.