Evidence map›Paper›PMID 30026534›Full record

ArticleHeredity2019

Subsampling reveals that unbalanced sampling affects STRUCTURE results in a multi-species dataset.

Patrick G Meirmans

Abstract read
In one paragraph

Article in Heredity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
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26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

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  12. The hindgut microbiota of coconut rhinoceros beetles (Applied and environmental microbiology · 2024
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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Patrick G MeirmansInstitute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands. p.g.meirmans@uva.nl.ORCID 0000-0002-6395-8107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying the genetic population structure of species can reveal important insights into several key evolutionary, historical, demographic, and anthropogenic processes. One of the most important statistical tools for inferring genetic clusters is the program STRUCTURE. Recently, several papers have pointed out that STRUCTURE may show a bias when the sampling design is unbalanced, resulting in spurious joining of underrepresented populations and spurious separation of overrepresented populations. Suggestions to overcome this bias include subsampling and changing the ancestry model, but the performance of these two methods has not yet been tested on actual data. Here, I use a data set of 12 high-alpine plant species to test whether unbalanced sampling affects the STRUCTURE inference of population differentiation between the European Alps and the Carpathians. For four of the 12 species, subsampling of the Alpine populations-to match the sample size between the Alps and the Carpathians-resulted in a drastically different clustering than the full data set. On the other hand, STRUCTURE results with the alternative ancestry model were indistinguishable from the results with the default model. Based on these results, the subsampling strategy seems a more viable approach to overcome the bias than the alternative ancestry model. However, subsampling is only possible when there is an a priori expectation of what constitute the main clusters. Though these results do not mean that the use of STRUCTURE should be discarded, it does indicate that users of the software should be cautious about the interpretation of the results when sampling is unbalanced.

Indexed as

Evolution, MolecularGenetics, PopulationSoftwareBayes TheoremEnvironmentGenotypeGeographyPlants

Identifiers

PMID30026534
PMCPMC6460757

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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.