Evidence map›Paper›PMID 24788602›Full record

ArticleGenetics2014

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.

George Tucker, Alkes L Price, Bonnie Berger

Abstract read
In one paragraph

Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 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

36 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  11. A polygenic resilience score moderates the genetic risk for schizophrenia: Replication in 18,090 cases and 28,114 controls from the Psychiatric Genomics Consortium.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2024
    Article
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  20. Genome-Wide Association Study Statistical Models: A Review.Methods in molecular biology (Clifton, N.J.) · 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.

George Tucker*Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.
Alkes L Price†Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Bonnie Berger*Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 bab@mit.edu.

Funding

Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
Compressive genomics for large omics data sets: Algorithms applications & toolsR01GM108348 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2013 to 2019
$2.1M
Medical Research Council G1001799NHGRI NIH HHS R01 HG006399NIGMS NIH HHS R01 GM108348
6 · The paper itself

Abstract

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Indexed as

Genome-Wide Association StudyPrincipal Component AnalysisConfounding Factors, EpidemiologicGenetics, PopulationHumansLinear ModelsModels, GeneticMultiple SclerosisPolymorphism, Single NucleotideGWASmixed modelspopulation stratification

Identifiers

PMID24788602
PMCPMC4096359

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.