Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests

Regev Schweiger (), Eyal Fisher, Omer Weissbrod, Elior Rahmani, Martina Müller-Nurasyid, Sonja Kunze, Christian Gieger, Melanie Waldenberger, Saharon Rosset and Eran Halperin
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Regev Schweiger: Tel Aviv University
Eyal Fisher: Tel Aviv University
Omer Weissbrod: Harvard T.H. Chan School of Public Health
Elior Rahmani: Tel Aviv University
Martina Müller-Nurasyid: Helmholtz Zentrum München—German Research Center for Environmental Health
Sonja Kunze: Helmholtz Zentrum München - German Research Center for Environmental Health
Christian Gieger: Helmholtz Zentrum München - German Research Center for Environmental Health
Melanie Waldenberger: partner site Munich Heart Alliance
Saharon Rosset: Tel Aviv University
Eran Halperin: University of California Los Angeles

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: Abstract 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.

Date: 2018
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DOI: 10.1038/s41467-018-07276-w

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