On the adjustment for covariates in genetic association analysis: A novel, simple principle to infer direct causal effects

• Type of Material: Article
• Authors: Stijn Vansteelandt, Ghent University; Sylvie Goetgeluk, Ghent University; Sharon Lutz, Harvard School of Public Health; Irwin Waldman, Emory University; Helen Lyon, Children's Hospital Boston; Eric E. Schadt, Rosetta Inpharmatics LLC; Scott T. Weiss, Harvard School of Public Health; Christoph Lange, Harvard School of Public Health
• Language: English
• Date: 2009-06-12
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2009 Wiley-Liss, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.20393
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 33
• Issue: 5
• Start Page: 394
• End Page: 405
• Grant/Funding Information: The first two authors acknowledge support from IAP research network grant nr. P06/03 from the Belgian government (Belgian Science Policy).
• Keywords: Genome, Human; Models, Genetic; Genome-Wide Association Study; Computer Simulation; Genotype; Models, Statistical; Genetic Variation; Humans; Genetic Predisposition to Disease; Cohort Studies; Research Design; Polymorphism, Single Nucleotide; Phenotype; Gene Frequency; Heart Diseases
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health

Abstract

In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non-genetic/ environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genet ic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non-marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome-wide association analyses.