title: 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 significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests.
authors.affiliation: Research Center for Genes, Environment and Human Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Min-Tzu
authors.initials: MT
authors.lastname: Lo
authors.affiliation: Research Center for Genes, Environment and Human Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
mesh_terms: D057225:Data Mining; D055106:Genome-Wide Association Study; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics* / Q000473:pathology; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide