Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Schweiger, Regev; Fisher, Eyal; Weissbrod, Omer; Rahmani, Elior; Müller-Nurasyid, Martina; Kunze, Sonja; Gieger, Christian; Waldenberger, Melanie; Rosset, Saharon; Halperin, Eran
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 9
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07276-w
• Languages: English
• ISSNs: 2041-1723

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 resultingp -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. Standard approaches for heritability and set testing in statistical genetics rely on parametric models that might not hold in reality and give inflatedp -values. Here, the authors develop a fast method for permutation-based testing of marker sets and of heritability that does not suffer from model misspecification.