title: Controlling the Rate of GWAS False Discoveries.
abstract: With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study.
authors: Institute of Mathematics, Jagiellonian University, 30-348 Kraków, Poland.|Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana 47405.
authors: Damian
authors: D
authors: Brzyski
authors: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors: Christine B
authors: CB
authors: Peterson
authors: Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland.
authors: Piotr
authors: P
authors: Sobczyk
authors: Department of Statistics, Stanford University, California 94305.
authors: Emmanuel J
authors: EJ
authors: Candès
authors: Institute of Mathematics, University of Wrocław, 50-384 Wroclaw, Poland.
authors: Malgorzata
authors: M
authors: Bogdan
authors: Department of Biomedical Data Science, Stanford University, California sabatti@stanford.edu.
authors: Chiara
authors: C
authors: Sabatti
mesh_terms: D015331:Cohort Studies; D005189:False Positive Reactions; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods; D006801:Humans; D016014:Linear Models; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests
keywords: FDR; association studies; linkage disequilibrium; multiple penalized regression