A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression

Clemontina A. Davenport (), Arnab Maity, Patrick F. Sullivan and Jung-Ying Tzeng
Additional contact information
Clemontina A. Davenport: Duke University Medical Center
Arnab Maity: North Carolina State University
Patrick F. Sullivan: University of North Carolina at Chapel Hill
Jung-Ying Tzeng: North Carolina State University

Statistics in Biosciences, 2018, vol. 10, issue 1, No 8, 117-138

Abstract: Abstract Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.

Keywords: Correlated binary responses; Generalized estimating equations; IBS kernel; Kernel machine; Non-parametric regression (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-017-9189-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9189-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-017-9189-9

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().