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

Clemontina A. Davenport, Arnab Maity, Patrick F. Sullivan, Jung Ying Tzeng

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)117-138
Number of pages22
JournalStatistics in Biosciences
Issue number1
Publication statusPublished - 2018 Apr 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)


Dive into the research topics of 'A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression'. Together they form a unique fingerprint.

Cite this