Prioritizing individual genetic variants after kernel machine testing using variable selection

• Type of Material: Article
• Authors: Qianchuan He, Fred Hutchinson Cancer Research Center; Tianxi Cai, Harvard University; Yan Liu, Emory University; Ni Zhao, Fred Hutchinson Cancer Research Center; Quaker E. Harmon, National Institute of Environmental Health Sciences; Lynn Almli, Emory University; Elisabeth Binder, Emory University; Stephanie M. Engel, University of North Carolina; Kerry Ressler, Emory University; Karen Conneely, Emory University; Xihong Lin, Harvard University; Michael C. Wu, Fred Hutchinson Cancer Research Center
• Language: English
• Date: 2016-12-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 WILEY PERIODICALS, INC.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21993
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 40
• Issue: 8
• Start Page: 722
• End Page: 731
• Grant/Funding Information: This research was supported in part by NIH R21HD060207, R01HG007508, R01HG006292, the Fred Hutchinson Cancer Research Center Institutional Research Support, and the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.
• Keywords: REGRESSION; KNIFE; Science & Technology; kernel machine methods; Genetics & Heredity; EXPRESSION; Life Sciences & Biomedicine; set-based; variable selection; RARE; GENOME-WIDE ASSOCIATION; Mathematical & Computational Biology; OUTCOMES; genetic association studies; STRESS; CANCER; REGULARIZATION
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health; Biology, Genetics

Abstract

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach.