Title

Signal Localization: A New Approach in Signal Discovery

Authors

Sergey Malov, St. Petersburg State University - Russia; St. Petersburg Electrotechnical University - Russia
Alexey Antonik, St. Petersburg State University - Russia
Minzhong Tang, Wuzhou Red Cross Hospital - China
Alexandre Berred, Université do Havre - France
Yi Zeng, Chinese Center for Disease Control
Stephen J. O'Brien, St. Petersburg State University - Russia; Nova Southeastern UniversityFollow

Document Type

Article

Publication Date

1-2017

Publication Title

Biometrical Journal

Keywords

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

ISSN

0323-3847

Volume

59

Issue/No.

1

First Page

126

Last Page

144

Abstract

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).

Comments

©2016 WILEY-VCH Verlag GmbH & Co. KgaA, Weinheim

Additional Comments

St. Petersburg State University grant #: 1.37.165.2014; Russian Ministry of Education and Science mega-grant #: 11.G34.31.0068; Wuzhou Science and Technology grant #: 201301046

NSUWorks Citation

Malov, Sergey; Alexey Antonik; Minzhong Tang; Alexandre Berred; Yi Zeng; and Stephen J. O'Brien. 2017. "Signal Localization: A New Approach in Signal Discovery." Biometrical Journal 59, (1): 126-144. doi:10.1002/bimj.201500222.

ORCID ID

0000-0001-7353-8301

ResearcherID

N-1726-2015

DOI

10.1002/bimj.201500222