Lo M.-T.WEN-CHUNG LEE2020-11-192020-11-1920142045-2322https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901719997&doi=10.1038%2fsrep05081&partnerID=40&md5=e02cbfb7a4eb6043da4b2f5abfa9f3cchttps://scholars.lib.ntu.edu.tw/handle/123456789/521780Many 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.English[SDGs]SDG3biological 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 Nucleotidejournal article10.1038/srep05081248663192-s2.0-84901719997