Evidence map›Paper›PMID 30651284›Full record

ArticleGenetics2019

Inferring Demography and Selection in Organisms Characterized by Skewed Offspring Distributions.

Andrew M Sackman, Rebecca B Harris, Jeffrey D Jensen

Abstract read
In one paragraph

Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Inferring the demographic history of aye-ayes (bioRxiv : the preprint server for biology · 2024
    Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. How Can We Resolve Lewontin's Paradox?Genome biology and evolution · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Effects of Selection at Linked Sites on Patterns of Genetic Variability.Annual review of ecology, evolution, and systematics · 2021
    Article
  18. Review
  19. Imposed mutational meltdown as an antiviral strategy.Evolution; international journal of organic evolution · 2020
    Review
  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

3 authors.

Andrew M SackmanSchool of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona 85287 amsackma@asu.edu.
Rebecca B HarrisSchool of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona 85287.
Jeffrey D JensenSchool of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona 85287.

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 recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the Ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew inherent to viral replication and identified putative drug-resistance mutations.

Indexed as

Models, GeneticPolymorphism, GeneticSelection, GeneticBiasDrug Resistance, ViralEvolution, MolecularInfluenza A viruscoalescent theorypopulation geneticsselectionsweepstakes reproductiontime-sampled inference

Identifiers

PMID30651284
PMCPMC6404255

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.