TY - JOUR

T1 - Signal localization: a new approach in signal discovery

AU - Malov, Sergey V.

AU - Antonik, Alexey

AU - Tang, Minzhong

AU - Berred, Alexandre

AU - Zeng, Yi

AU - O’Brien, Stephen J.

PY - 2017

Y1 - 2017

N2 - 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

AB - 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

KW - Average statistics

KW - Genome wide association study

KW - Multiple testing problem

KW - Positive regression dependence

KW - Signal discovery

U2 - 10.1002/bimj.201500222

DO - 10.1002/bimj.201500222

M3 - Article

VL - 59

SP - 126

EP - 144

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 1

ER -