Evidence map›Paper›PMID 34718708›Full record

ArticleMolecular biology and evolution2022

Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants.

Marie Saitou, Naoki Masuda, Omer Gokcumen

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Human follicular mites: Ectoparasites becoming symbionts.Molecular biology and evolution · 2022
    Article
  10. Review
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.

Marie SaitouDepartment of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.ORCID 0000-0002-8794-3217
Naoki MasudaDepartment of Mathematics, University at Buffalo, State University of New York, Buffalo, NY, USA.ORCID 0000-0003-1567-801X
Omer GokcumenDepartment of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.ORCID 0000-0003-4371-679X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.

Indexed as

Genetics, PopulationGenomic Structural VariationSelection, GeneticAllelesGene FrequencyHaplotypesPhenotypecomplex traitscopy number variationDenisovanneutrality testpopulation genetics

Identifiers

PMID34718708
PMCPMC8896759

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.