title: Prioritizing individual genetic variants after kernel machine testing using variable selection.
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.
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Qianchuan
authors.initials: Q
authors.lastname: He
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Ni
authors.initials: N
authors.lastname: Zhao
authors.affiliation: Epidemiology Branch, NIEHS, Research Triangle Park, North Carolina, United States of America.
authors.forename: Quaker E
authors.initials: QE
authors.lastname: Harmon
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, United States of America.
authors.forename: Lynn M
authors.initials: LM
authors.lastname: Almli
authors.affiliation: Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany.
authors.forename: Elisabeth B
authors.initials: EB
authors.lastname: Binder
authors.affiliation: Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America.
authors.forename: Stephanie M
authors.initials: SM
authors.lastname: Engel
authors.affiliation: Division of Depression & Anxiety Disorders, McLean Hospital, Belmont, Massachusetts, United States of America.
authors.forename: Kerry J
authors.initials: KJ
authors.lastname: Ressler
authors.affiliation: Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, United States of America.
authors.forename: Karen N
authors.initials: KN
authors.lastname: Conneely
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Wu
mesh_terms: D001724:Birth Weight / Q000235:genetics*; D003627:Data Interpretation, Statistical; D019332:Endothelin-1 / Q000235:genetics; D056726:Genetic Association Studies; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D042965:Methylenetetrahydrofolate Reductase (NADPH2) / Q000235:genetics; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012641:Selection, Genetic / Q000235:genetics*; D014947:Wounds and Injuries / Q000235:genetics*
chemical_list: D019332:Endothelin-1; D005819:Genetic Markers; C573423:MTHFR protein, human; D042965:Methylenetetrahydrofolate Reductase (NADPH2)
keywords: KNIFE; genetic association studies; kernel machine methods; set-based; variable selection