Evidence map›Paper›PMID 35596736›Full record

ArticleMolecular ecology resources2022

FSTruct: An F

Maike L Morrison, Nicolas Alcala, Noah A Rosenberg

Abstract read
In one paragraph

Article in Molecular ecology resources, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The Yemeni genetic structure revealed by the Y chromosome STRs.Forensic science, medicine, and pathology · 2025
    Article
  6. Quantifying compositional variability in microbial communities with FAVA.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Genetic Variation and Population Structure ofPathogens (Basel, Switzerland) · 2024
    Article
  8. Article
  9. Article
  10. Quantifying compositional variability in microbial communities with FAVA.bioRxiv : the preprint server for biology · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. FSTruct: An FMolecular ecology resources · 2022
    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.

Maike L MorrisonDepartment of Biology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0003-0430-1401
Nicolas AlcalaRare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), Lyon, France.ORCID https://orcid.org/0000-0002-5961-5064
Noah A RosenbergDepartment of Biology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0002-1829-8664

Funding

Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
NHGRI NIH HHS R01 HG005855NIH HHS R01 HG005855World Health Organization 001
6 · The paper itself

Abstract

In model-based inference of population structure from individual-level genetic data, individuals are assigned membership coefficients in a series of statistical clusters generated by clustering algorithms. Distinct patterns of variability in membership coefficients can be produced for different groups of individuals, for example, representing different predefined populations, sampling sites or time periods. Such variability can be difficult to capture in a single numerical value; membership coefficient vectors are multivariate and potentially incommensurable across predefined groups, as the number of clusters over which individuals are distributed can vary among groups of interest. Further, two groups might share few clusters in common, so that membership coefficient vectors are concentrated on different clusters. We introduce a method for measuring the variability of membership coefficients of individuals in a predefined group, making use of an analogy between variability across individuals in membership coefficient vectors and variation across populations in allele frequency vectors. We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector and does not depend on its mean. We apply the approach, which makes use of a normalized F

Indexed as

AlgorithmsGenetics, PopulationCluster AnalysisGene FrequencyHumansadmixtureF STpopulation structure

Identifiers

PMID35596736
PMCPMC9544611

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.