Evidence map›Paper›PMID 24637351›Full record

ArticleG3 (Bethesda, Md.)2014

Effects of sample selection bias on the accuracy of population structure and ancestry inference.

Suyash Shringarpure, Eric P Xing

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
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  4. Review
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  9. Review
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  15. Genomic and geographical structure of human cytomegalovirus.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Genetic diversity patterns and domestication origin of soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019
    Article
  20. 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

2 authors.

Suyash ShringarpureDepartment of Genetics, Stanford University, Stanford, California 94305.
Eric P XingSchool of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 epxing@cs.cmu.edu.

Funding

Genome-Transcription-Phenome-Wide Association: a new paradigm for association stuR01GM087694 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI WU, WEI, XING, ERIC P · 2009 to 2014
$2.5M
Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for StudyinR01GM093156 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI XING, ERIC P · 2010 to 2014
$2.2M
NIGMS NIH HHS R01 GM087694NIGMS NIH HHS R01GM087694NIGMS NIH HHS R01 GM093156
6 · The paper itself

Abstract

Population stratification is an important task in genetic analyses. It provides information about the ancestry of individuals and can be an important confounder in genome-wide association studies. Public genotyping projects have made a large number of datasets available for study. However, practical constraints dictate that of a geographical/ethnic population, only a small number of individuals are genotyped. The resulting data are a sample from the entire population. If the distribution of sample sizes is not representative of the populations being sampled, the accuracy of population stratification analyses of the data could be affected. We attempt to understand the effect of biased sampling on the accuracy of population structure analysis and individual ancestry recovery. We examined two commonly used methods for analyses of such datasets, ADMIXTURE and EIGENSOFT, and found that the accuracy of recovery of population structure is affected to a large extent by the sample used for analysis and how representative it is of the underlying populations. Using simulated data and real genotype data from cattle, we show that sample selection bias can affect the results of population structure analyses. We develop a mathematical framework for sample selection bias in models for population structure and also proposed a correction for sample selection bias using auxiliary information about the sample. We demonstrate that such a correction is effective in practice using simulated and real data.

Indexed as

Genetics, PopulationGenome-Wide Association StudySelection BiasAlgorithmsAnimalsCattleDatasets as TopicGenotypeModels, TheoreticalRisk Factorsadmixture analysisbiased samplingglobal ancestrypopulation structure

Identifiers

PMID24637351
PMCPMC4025489

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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.