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Statistics in Biosciences

, Volume 10, Issue 1, pp 117–138 | Cite as

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

  • Clemontina A. DavenportEmail author
  • Arnab Maity
  • Patrick F. Sullivan
  • Jung-Ying Tzeng
Article
  • 177 Downloads

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 

Notes

Acknowledgements

The authors thank Dr. Robert Yolken at Johns Hopkins University for providing the antibody data. The authors also thank Drs. Peter Vollenweider and Gerard Waeber, PIs of the CoLaus study, and Drs. Meg Ehm and Matthew Nelson, collaborators at GlaxoSmithKline for providing the CoLaus phenotype and sequence data. This work was supported by National Institutes of Health Grants R00 ES017744 (to A.M.), R01 MH084022 (to J.Y.T. and P.F.S.), and P01 CA142538 (to J.Y.T.).

Compliance with Ethical Standards

Conflicts of interest

The authors have no conflicts of interest to declare.

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Copyright information

© International Chinese Statistical Association 2017

Authors and Affiliations

  • Clemontina A. Davenport
    • 1
    Email author
  • Arnab Maity
    • 2
  • Patrick F. Sullivan
    • 3
  • Jung-Ying Tzeng
    • 4
    • 5
    • 6
  1. 1.Department of Biostatistics and BioinformaticsDuke University Medical CenterDurhamUSA
  2. 2.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  3. 3.Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of Statistics, Bioinformatics Research CenterNorth Carolina State UniversityRaleighUSA
  5. 5.Department of StatisticsNational Cheng-Kung UniversityTainanTaiwan
  6. 6.Institute of Epidemiology and Preventive MedicineNational Taiwan UniversityTaipeiTaiwan

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