https://scholars.lib.ntu.edu.tw/handle/123456789/521780
標題: | Detecting a weak association by testing its multiple perturbations: A data mining approach | 作者: | Lo M.-T. WEN-CHUNG LEE |
公開日期: | 2014 | 出版社: | Nature Publishing Groups | 卷: | 4 | 來源出版物: | Scientific Reports | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901719997&doi=10.1038%2fsrep05081&partnerID=40&md5=e02cbfb7a4eb6043da4b2f5abfa9f3cc https://scholars.lib.ntu.edu.tw/handle/123456789/521780 |
ISSN: | 2045-2322 | DOI: | 10.1038/srep05081 | SDG/關鍵字: | 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 |
顯示於: | 流行病學與預防醫學研究所 |
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