title: Many Phenotypes Without Many False Discoveries: Error Controlling Strategies for Multitrait Association Studies.
abstract: The genetic basis of multiple phenotypes such as gene expression, metabolite levels, or imaging features is often investigated by testing a large collection of hypotheses, probing the existence of association between each of the traits and hundreds of thousands of genotyped variants. Appropriate multiplicity adjustment is crucial to guarantee replicability of findings, and the false discovery rate (FDR) is frequently adopted as a measure of global error. In the interest of interpretability, results are often summarized so that reporting focuses on variants discovered to be associated to some phenotypes. We show that applying FDR-controlling procedures on the entire collection of hypotheses fails to control the rate of false discovery of associated variants as well as the expected value of the average proportion of false discovery of phenotypes influenced by such variants. We propose a simple hierarchical testing procedure that allows control of both these error rates and provides a more reliable basis for the identification of variants with functional effects. We demonstrate the utility of this approach through simulation studies comparing various error rates and measures of power for genetic association studies of multiple traits. Finally, we apply the proposed method to identify genetic variants that impact flowering phenotypes in Arabidopsis thaliana, expanding the set of discoveries.
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, California, United States of America.
authors.forename: Christine B
authors.initials: CB
authors.lastname: Peterson
authors.affiliation: Faculty of Industrial Engineering and Management, Technion, Haifa, Israel.
authors.forename: Marina
authors.initials: M
authors.lastname: Bogomolov
authors.affiliation: Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.
authors.forename: Yoav
authors.initials: Y
authors.lastname: Benjamini
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, California, United States of America.|Department of Statistics, Stanford University, Stanford, California, United States of America.
authors.forename: Chiara
authors.initials: C
authors.lastname: Sabatti
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000502:physiology; D003198:Computer Simulation; D035264:Flowers / Q000502:physiology; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results
keywords: error control; false discovery rate; genetic association study; multiple hypothesis testing; multiple phenotypes