Evidence map›Paper›PMID 33970931›Full record

ArticlePloS one2021

Statistical inconsistency of the unrooted minimize deep coalescence criterion.

Ayed A R Alanzi, James H Degnan

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 41% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Ayed A R AlanziMathematics Department, College of Science and Human Studies of Hotat Sudair, Majmaah University, Majmaah, Saudi Arabia.
James H DegnanDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, United States of America.ORCID 0000-0001-7886-638X
Majmaah University · SAUniversity of New Mexico · US

Funding

Mathematical and computational analysis for species tree inferenceR01GM117590 · NIGMS · UNIVERSITY OF ALASKA FAIRBANKS · PI RHODES, JOHN · 2015 to 2018
$1.5M
NIGMS NIH HHS R01 GM117590
6 · The paper itself

Abstract

Species trees, which describe the evolutionary relationships between species, are often inferred from gene trees, which describe the ancestral relationships between sequences sampled at different loci from the species of interest. A common approach to inferring species trees from gene trees is motivated by supposing that gene tree variation is due to incomplete lineage sorting, also known as deep coalescence. One of the earliest methods motivated by deep coalescence is to find the species tree that minimizes the number of deep coalescent events needed to explain discrepancies between the species tree and input gene trees. This minimize deep coalescence (MDC) criterion can be applied in both rooted and unrooted settings. where either rooted or unrooted gene trees can be used to infer a rooted species tree. Previous work has shown that MDC is statistically inconsistent in the rooted setting, meaning that under a probabilistic model for deep coalescence, the multispecies coalescent, for some species trees, increasing the number of input gene trees does not make the method more likely to return a correct species tree. Here, we obtain analogous results in the unrooted setting, showing conditions leading to inconsistency of the MDC criterion using the multispecies coalescent model with unrooted gene trees for four taxa and five taxa.

Indexed as

Biological EvolutionGenetic SpeciationStatistics, NonparametricAlgorithmsAnimalsComputer SimulationHumansModels, GeneticModels, StatisticalPhylogenySoftware

Identifiers

PMID33970931
PMCPMC8109837
OpenAlexW3162565602

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.