Evidence map›Paper›PMID 21448230›Full record

ArticleHeredity2011

Investigating population stratification and admixture using eigenanalysis of dense genotypes.

D Shriner

Abstract read
In one paragraph

Article in Heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
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  5. Article
  6. Ancestry-specific and sex-specific risk alleles identified in a genome-wide gene-by-alcohol dependence interaction study of risky sexual behaviors.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2017
    Article
  7. Article
  8. Article
  9. Article
  10. F-box/LRR-repeat protein 7 is genetically associated with Alzheimer's disease.Annals of clinical and translational neurology · 2015
    Article
  11. Article
  12. Review
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  14. Article
  15. Article
  16. Article
  17. Article
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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

1 author.

D ShrinerCenter for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892-5635, USA. shrinerda@mail.nih.gov

Funding

UVDM01RR010284 · NCRR · HOWARD UNIVERSITY · PI TAYLOR, ROBERT E. · 1996 to 2010
$17.4M
The Howard University SCORE Program Supplemental GrantS06GM008016 · NIGMS · HOWARD UNIVERSITY · PI NELSON, KAREN ELIZABETH · 1989 to 2009
$16.1M
Genetic epidemiology of complex diseasesZ01HG200362 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ROTIMI, CHARLES · 2008 to 2008
$1.7M
Intramural NIH HHS Z01 HG200362Intramural NIH HHS Z99 HG999999NCRR NIH HHS 2M01RR010284NCRR NIH HHS M01 RR010284NIGMS NIH HHS S06GM008016-320107NIGMS NIH HHS S06GM008016-380111
6 · The paper itself

Abstract

Principal components analysis of genetic data is used to avoid inflation in type I error rates in association testing due to population stratification by covariate adjustment using the top eigenvectors and to estimate cluster or group membership independent of self-reported or ethnic identities. Eigendecomposition transforms correlated variables into an equal number of uncorrelated variables. Numerous stopping rules have been developed to identify which principal components should be retained. Recent developments in random matrix theory have led to a formal hypothesis test of the top eigenvalue, providing another way to achieve dimension reduction. In this study, I compare Velicer's minimum average partial test to a test on the basis of Tracy-Widom distribution as implemented in EIGENSOFT, the most widely used implementation of principal components analysis in genome-wide association analysis. By computer simulation of vicariance on the basis of coalescent theory, EIGENSOFT systematically overestimates the number of significant principal components. Furthermore, this overestimation is larger for samples of admixed individuals than for samples of unadmixed individuals. Overestimating the number of significant principal components can potentially lead to a loss of power in association testing by adjusting for unnecessary covariates and may lead to incorrect inferences about group differentiation. Velicer's minimum average partial test is shown to have both smaller bias and smaller variance, often with a mean squared error of 0, in estimating the number of principal components to retain. Velicer's minimum average partial test is implemented in R code and is suitable for genome-wide genotype data with or without population labels.

Indexed as

Computer SimulationGenetic VariationHaplotypesModels, GeneticPrincipal Component AnalysisAlgorithmsBlack or African AmericanHumansPedigreePolymorphism, Single NucleotideSoftware

Identifiers

PMID21448230
PMCPMC3128175

What OpenQuestion holds

Textmetadata
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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.