Detecting a weak association by testing its multiple perturbations: a data mining approach.

ABSTRACT: Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can be increased but unfortunately only to an extent. Here, we propose a novel method which hinges on increasing sample size in a different direction-the total number of variables (p). We construct a p-based 'multiple perturbation test', and conduct power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large. As a demonstration, we apply the method to analyze a genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significant

SUBMITTER: Lo MT 

PROVIDER: S-EPMC4035575 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature