LeHavreV1, Main, Exploration, bibRecord, 000038

Signal localization: a new approach in signal discovery.

Identifieur interne : 000038 ( Main/Exploration ); précédent : 000037; suivant : 000039

Signal localization: a new approach in signal discovery.

Auteurs : Sergey V. Malov [Russie] ; Alexey Antonik [Russie] ; Minzhong Tang [République populaire de Chine] ; Alexandre Berred [France] ; Yi Zeng [République populaire de Chine] ; Stephen J. O'Brien [Russie]

Source :

Biometrical journal. Biometrische Zeitschrift [ 1521-4036 ] ; 2016.

RBID : pubmed:27775844

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).

DOI: 10.1002/bimj.201500222
PubMed: 27775844

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">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).</div>
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Signal localization: a new approach in signal discovery.

Signal localization: a new approach in signal discovery.

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Abstract

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