ArticleStatistics in biosciences2018
A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression.
Article in Statistics in biosciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bayesian inference on high-dimensional multivariate binary responses.Journal of the American Statistical Association · 2024Article
- Robust kernel association testing (RobKAT).Genetic epidemiology · 2020Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Inference on phenotype-specific effects of genes using multivariate kernel machine regression.Genetic epidemiology · 2018Article
- A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Genetic epidemiology · 2015Article
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Authors and funding
4 authors.
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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 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.
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