title: An Optimal Bahadur-Efficient Method in Detection of Sparse Signals with Applications to Pathway Analysis in Sequencing Association Studies.
abstract: Next-generation sequencing data pose a severe curse of dimensionality, complicating traditional "single marker-single trait" analysis. We propose a two-stage combined p-value method for pathway analysis. The first stage is at the gene level, where we integrate effects within a gene using the Sequence Kernel Association Test (SKAT). The second stage is at the pathway level, where we perform a correlated Lancaster procedure to detect joint effects from multiple genes within a pathway. We show that the Lancaster procedure is optimal in Bahadur efficiency among all combined p-value methods. The Bahadur efficiency,[Formula: see text], compares sample sizes among different statistical tests when signals become sparse in sequencing data, i.e. ε →0. The optimal Bahadur efficiency ensures that the Lancaster procedure asymptotically requires a minimal sample size to detect sparse signals ([Formula: see text]). The Lancaster procedure can also be applied to meta-analysis. Extensive empirical assessments of exome sequencing data show that the proposed method outperforms Gene Set Enrichment Analysis (GSEA). We applied the competitive Lancaster procedure to meta-analysis data generated by the Global Lipids Genetics Consortium to identify pathways significantly associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol.
authors.affiliation: Health Services and Outcomes Research, Children's Mercy Hospital, Kansas City, MO, United States of America.|Department of Biomedical & Health Informatics, University of Missouri-Kansas City, Kansas City, MO, United States of America.
authors.forename: Hongying
authors.identifier: http://orcid.org/0000-0003-1395-7904
authors.initials: H
authors.lastname: Dai
authors.affiliation: Lovelace Respiratory Research Institute, Albuquerque, New Mexico, United States of America.
authors.forename: Guodong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Wu
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States of America.
authors.forename: Degui
authors.initials: D
authors.lastname: Zhi
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D050356:Lipid Metabolism / Q000235:genetics*; D015201:Meta-Analysis as Topic; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008957:Models, Genetic; D015398:Signal Transduction / Q000235:genetics*