Detecting a weak association by testing its multiple perturbations: A data mining approach
Journal
Scientific Reports
Journal Volume
4
Date Issued
2014
Author(s)
Lo M.-T.
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 significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests.
SDGs
Other Subjects
biological model; data mining; genetic association; genetics; human; macular degeneration; pathology; single nucleotide polymorphism; Data Mining; Genome-Wide Association Study; Humans; Macular Degeneration; Models, Genetic; Polymorphism, Single Nucleotide
Publisher
Nature Publishing Groups
Type
journal article