|Title||A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Davenport, Clemontina A., Arnab Maity, Patrick F. Sullivan, and Jung-Ying Tzeng|
|Date Published||2018 Apr|
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 SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear 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 (GEEs) 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 demonstrated our methods using the CATIE antibody study data and the CoLaus Study data.
|Alternate Journal||Stat Biosci|
|Original Publication||A powerful test for SNP effects on multivariate binary outcomes using kernel machine regression.|
|PubMed Central ID||PMC6226013|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R00 ES017744 / ES / NIEHS NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression.