Evidence map›Paper›PMID 34983052›Full record

ArticleGenome biology and evolution2022

Nucleotide Usage Biases Distort Inferences of the Species Tree.

Rui Borges, Bastien Boussau, Gergely J Szöllősi, Carolin Kosiol

Abstract read
In one paragraph

Article in Genome biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

4 authors.

Rui BorgesInstitut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria.
Bastien BoussauUniversité de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5558, LBBE, Villeurbanne, France.ORCID 0000-0003-0776-4460
Gergely J SzöllősiDepartment of Biological Physics, Eötvös University, Budapest , Hungary.
Carolin KosiolInstitut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the importance of natural selection in species' evolutionary history, phylogenetic methods that take into account population-level processes typically ignore selection. The assumption of neutrality is often based on the idea that selection occurs at a minority of loci in the genome and is unlikely to compromise phylogenetic inferences significantly. However, genome-wide processes like GC-bias and some variation segregating at the coding regions are known to evolve in the nearly neutral range. As we are now using genome-wide data to estimate species trees, it is natural to ask whether weak but pervasive selection is likely to blur species tree inferences. We developed a polymorphism-aware phylogenetic model tailored for measuring signatures of nucleotide usage biases to test the impact of selection in the species tree. Our analyses indicate that although the inferred relationships among species are not significantly compromised, the genetic distances are systematically underestimated in a node-height-dependent manner: that is, the deeper nodes tend to be more underestimated than the shallow ones. Such biases have implications for molecular dating. We dated the evolutionary history of 30 worldwide fruit fly populations, and we found signatures of GC-bias considerably affecting the estimated divergence times (up to 23%) in the neutral model. Our findings call for the need to account for selection when quantifying divergence or dating species evolution.

Indexed as

Codon UsageEvolution, MolecularAnimalsDrosophilaNucleotidesPhylogenySelection, GeneticNucleotidesGC-biasmolecular datingnearly neutral evolutionselectionspecies tree

Identifiers

PMID34983052
PMCPMC8829901

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.