Evidence map›Paper›PMID 24473328›Full record

ArticleNature genetics2014

Advantages and pitfalls in the application of mixed-model association methods.

Jian Yang, Noah A Zaitlen, Michael E Goddard, Peter M Visscher, Alkes L Price

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Nature genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 572 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
572citing papers in PubMed, 6 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

572 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  10. Genetic dissection of grain yield and correlated proxy traits under suboptimal conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  18. bioRxiv : the preprint server for biology · 2026
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512 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

5 authors.

Jian Yang1] Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. [2] University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia. [3].
Noah A Zaitlen1] Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, California, USA. [2].
Michael E Goddard1] Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia. [2].
Peter M Visscher1] Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. [2] University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia. [3].
Alkes L Price1] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. [2] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [4].

Funding

GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9M
Statistical Methods for X-Chromosome Genetic DataP01GM099568 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2012 to 2016
$7.3M
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
Theoretical Population Genetics SupplementR01GM075091 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2006 to 2020
$4.7M
NHGRI NIH HHS R01 HG006399NIEHS NIH HHS T32 ES007142NIGMS NIH HHS P01 GM099568NIGMS NIH HHS R01 GM075091Wellcome Trust
6 · The paper itself

Abstract

Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.

Indexed as

Linear ModelsResearch DesignColitis, UlcerativeComputer SimulationGenetic Association StudiesHumansMultiple Sclerosis

Identifiers

PMID24473328
PMCPMC3989144

What Socratic holds

Textmetadata
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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 Socratic graph.