Evidence map›Paper›PMID 29217523›Full record

ArticleGenetics2018

Detecting Recent Positive Selection with a Single Locus Test Bipartitioning the Coalescent Tree.

Zongfeng Yang, Junrui Li, Thomas Wiehe, Haipeng Li

Abstract read
In one paragraph

Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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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.

Zongfeng YangKey Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Gesellschaft (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.ORCID 0000-0001-9477-6843
Junrui LiKey Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Gesellschaft (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Thomas WieheInstitut für Genetik, Universität zu Köln, 50674 Germany.
Haipeng LiKey Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Gesellschaft (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China lihaipeng@picb.ac.cn.ORCID 0000-0001-7856-4488

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many population genomic studies have been conducted in the past to search for traces of recent events of positive selection. These traces, however, can be obscured by temporal variation of population size or other demographic factors. To reduce the confounding impact of demography, the coalescent tree topology has been used as an additional source of information for detecting recent positive selection in a population or a species. Based on the branching pattern at the root, we partition the hypothetical coalescent tree, inferred from a sequence sample, into two subtrees. The reasoning is that positive selection could impose a strong impact on branch length in one of the two subtrees while demography has the same effect on average on both subtrees. Thus, positive selection should be detectable by comparing statistics calculated for the two subtrees. Simulations demonstrate that the proposed test based on these principles has high power to detect recent positive selection even when DNA polymorphism data from only one locus is available, and that it is robust to the confounding effect of demography. One feature is that all components in the summary statistics ([Formula: see text]) can be computed analytically. Moreover, misinference of derived and ancestral alleles is seen to have only a limited effect on the test, and it therefore avoids a notorious problem when searching for traces of recent positive selection.

Indexed as

Genetics, PopulationModels, GeneticQuantitative Trait LociSelection, GeneticAdaptation, BiologicalDemographyModels, StatisticalZea maysadaptationdemographypositive selection

Identifiers

PMID29217523
PMCPMC5788538

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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.