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Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests.
Nat. Commun. 9:4919 (2018)
Testing for association between a set of genetic markers and a phenotype is a fundamental task in genetic studies. Standard approaches for heritability and set testing strongly rely on parametric models that make specific assumptions regarding phenotypic variability. Here, we show that resulting p-values may be inflated by up to 15 orders of magnitude, in a heritability study of methylation measurements, and in a heritability and expression quantitative trait loci analysis of gene expression profiles. We propose FEATHER, a method for fast permutation-based testing of marker sets and of heritability, which properly controls for false-positive results. FEATHER eliminated 47% of methylation sites found to be heritable by the parametric test, suggesting a substantial inflation of false-positive findings by alternative methods. Our approach can rapidly identify heritable phenotypes out of millions of phenotypes acquired via high-throughput technologies, does not suffer from model misspecification and is highly efficient.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Linear Mixed Models; Kernel Association Test; Variance-components; Gene-expression; Stochastic-approximation; Peripheral-blood; Dna Methylation; Monte-carlo; Powerful; Traits
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Journal
Nature Communications
Quellenangaben
Volume: 9,
Issue: 1,
Article Number: 4919
Publisher
Nature Publishing Group
Publishing Place
London
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Reviewing status
Peer reviewed