Evidence map›Paper›PMID 33871576›Full record

ArticleG3 (Bethesda, Md.)2021

Effects of single nucleotide polymorphism ascertainment on population structure inferences.

Kotaro Dokan, Sayu Kawamura, Kosuke M Teshima

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Decoding Local Adaptation in the Exploited Native Marine MusselInternational journal of molecular sciences · 2025
    Article
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  8. Article
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  12. Article
  13. Fourth Report on Chicken Genes and Chromosomes 2022.Cytogenetic and genome research · 2022
    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.

Kotaro DokanGraduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan.
Sayu KawamuraGraduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan.
Kosuke M TeshimaDepartment of Biology, Kyushu University, Fukuoka 819-0395, Japan.ORCID 0000-0003-4854-1060

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single nucleotide polymorphism (SNP) data are widely used in research on natural populations. Although they are useful, SNP genotyping data are known to contain bias, normally referred to as ascertainment bias, because they are conditioned by already confirmed variants. This bias is introduced during the genotyping process, including the selection of populations for novel SNP discovery and the number of individuals involved in the discovery panel and selection of SNP markers. It is widely recognized that ascertainment bias can cause inaccurate inferences in population genetics and several methods to address these bias issues have been proposed. However, especially in natural populations, it is not always possible to apply an ideal ascertainment scheme because natural populations tend to have complex structures and histories. In addition, it was not fully assessed if ascertainment bias has the same effect on different types of population structure. Here, we examine the effects of bias produced during the selection of population for SNP discovery and consequent SNP marker selection processes under three demographic models: the island, stepping-stone, and population split models. Results show that site frequency spectra and summary statistics contain biases that depend on the joint effect of population structure and ascertainment schemes. Additionally, population structure inferences are also affected by ascertainment bias. Based on these results, it is recommended to evaluate the validity of the ascertainment strategy prior to the actual typing process because the direction and extent of ascertainment bias vary depending on several factors.

Indexed as

Genetics, PopulationPolymorphism, Single NucleotideBiasHumansascertainment biaspopulation geneticspopulation structure inferenceSNP

Identifiers

PMID33871576
PMCPMC8496283

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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.