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Biom J. 2017 Jan;59(1):126-144. doi: 10.1002/bimj.201500222. Epub 2016 Oct 24.

Signal localization: a new approach in signal discovery.

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Theodosius Dobzhansky Center for Genome Bionformatics, St.-Petersburg State University, 199034, Sredniy avenue 41A, St.-Petersburg, Russia.
Department of Mathematics, St.-Petersburg Electrotechnical University "LETI", 197376, Prof. Popova str. 5, St.-Petersburg, Russia.
Wuzhou Health System Key Laboratory for Nasopharyngeal Carcinoma Etiology and Molecular Mechanism Wuzhou Red Cross Hospital, Wuzhou, Guangxi, P. R. China.
Université du Havre, UFR Sciences et Techniques, BP 540, 76058, Le Havre Cedex, France.
National Institute for Viral Diseases Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, P. R. China.
Guy Harvey Oceanographic Center, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, 8000 North Ocean Drive, FL, USA.


A new approach for statistical association signal identification is developed in this paper. We consider a strategy for nonprecise signal identification by extending the well-known signal detection and signal identification methods applicable to the multiple testing problem. Collection of statistical instruments under the presented approach is much broader than under the traditional signal identification methods, allowing more efficient signal discovery. Further assessments of maximal value and average statistics in signal discovery are improved. While our method does not attempt to detect individual predictors, it instead detects sets of predictors that are jointly associated with the outcome. Therefore, an important application would be in genome wide association study (GWAS), where it can be used to detect genes which influence the phenotype but do not contain any individually significant single nucleotide polymorphism (SNP). We compare power of the signal identification method based on extremes of single p-values with the signal localization method based on average statistics for logarithms of p-values. A simulation analysis informs the application of signal localization using the average statistics for wide signals discovery in Gaussian white noise process. We apply average statistics and the localization method to GWAS to discover better gene influences of regulating loci in a Chinese cohort developed for risk of nasopharyngeal carcinoma (NPC).


Average statistics; Genome wide association study; Multiple testing problem; Positive regression dependence; Signal discovery

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