Evidence map›Paper›PMID 24721987›Full record

ArticlePLoS genetics2014

Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples.

Peter M Visscher, Gibran Hemani, Anna A E Vinkhuyzen, Guo-Bo Chen, Sang Hong Lee, Naomi R Wray, Michael E Goddard, Jian Yang

Abstract read
In one paragraph

Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 210 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
210citing papers in PubMed, 11 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

210 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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  17. GWAS for behavioral traits in golden retrievers identifies genes implicated in human temperament, mental health, and cognition.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  18. Article
  19. Mathematical bounds on rTheoretical population biology · 2025
    Article
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150 more citing papers are in PubMed but not listed here.

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

8 authors.

Peter M VisscherThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia; The University of Queensland Diamantina Institute, The Translational Research Institute, Brisbane, Queensland, Australia.
Gibran HemaniThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia; The University of Queensland Diamantina Institute, The Translational Research Institute, Brisbane, Queensland, Australia.
Anna A E VinkhuyzenThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia.
Guo-Bo ChenThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia.
Sang Hong LeeThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia.
Naomi R WrayThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia.
Michael E GoddardUniversity of Melbourne, Department of Food and Agricultural Systems, Parkville, Victoria, Australia; Biosciences Research Division, Department of Primary Industries, Bundoora, Victoria, Australia.
Jian YangThe University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia; The University of Queensland Diamantina Institute, The Translational Research Institute, Brisbane, Queensland, Australia.

Funding

Statistical Methods for X-Chromosome Genetic DataP01GM099568 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2012 to 2016
$7.3M
Estimating the genetic and environmental architecture of psychiatric disordersR01MH100141 · NIMH · UNIVERSITY OF COLORADO · PI KELLER, MATTHEW CHARLES · 2013 to 2022
$5.3M
Theoretical Population Genetics SupplementR01GM075091 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2006 to 2020
$4.7M
NIGMS NIH HHS P01 GM099568NIGMS NIH HHS R01 GM075091NIMH NIH HHS R01 MH100141
6 · The paper itself

Abstract

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.

Indexed as

Case-Control StudiesGenome-Wide Association StudyHumansModels, GeneticPhenotypePolymorphism, Single NucleotideQuantitative Trait LociSoftware

Identifiers

PMID24721987
PMCPMC3983037

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.