ArticleBiometrics2011
Powerful tests for detecting a gene effect in the presence of possible gene-gene interactions using garrote kernel machines.
Article in Biometrics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.Briefings in bioinformatics · 2026Article
- Flexible Bayesian Inference for Identifying Significantly Correlated Multiple Pathway Sets.Statistics in medicine · 2026Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- TreeKernel: interpretable kernel machine tests for interactions between -omics and clinical predictors with applications to metabolomics and COPD phenotypes.BMC bioinformatics · 2023Article
- A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies.Journal of the American Statistical Association · 2022Article
- Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype.BMC genetics · 2019Article
- A weighted kernel machine regression approach to environmental pollutants and infertility.Statistics in medicine · 2019Article
- Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.Biostatistics (Oxford, England) · 2018Article
- Variance Component Selection With Applications to Microbiome Taxonomic Data.Frontiers in microbiology · 2018Article
- More accurate semiparametric regression in pharmacogenomics.Statistics and its interface · 2018Article
- Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures.Annals of epidemiology · 2017Review
- Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.Biometrics · 2016Article
- An Adaptive Genetic Association Test Using Double Kernel Machines.Statistics in biosciences · 2015Article
- Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.Genetic epidemiology · 2015Article
- Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.Biostatistics (Oxford, England) · 2015Article
- Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genetic epidemiology · 2015Article
- Design and analysis issues in gene and environment studies.Environmental health : a global access science source · 2012Review
- Power of a reproducing kernel-based method for testing the joint effect of a set of single-nucleotide polymorphisms.Genetica · 2012Article
- Multivariate phenotype association analysis by marker-set kernel machine regression.Genetic epidemiology · 2012Article
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2 authors.
Funding
Abstract
We propose in this article a powerful testing procedure for detecting a gene effect on a continuous outcome in the presence of possible gene-gene interactions (epistasis) in a gene set, e.g., a genetic pathway or network. Traditional tests for this purpose require a large number of degrees of freedom by testing the main effect and all the corresponding interactions under a parametric assumption, and hence suffer from low power. In this article, we propose a powerful kernel machine based test. Specifically, our test is based on a garrote kernel method and is constructed as a score test. Here, the term garrote refers to an extra nonnegative parameter that is multiplied to the covariate of interest so that our score test can be formulated in terms of this nonnegative parameter. A key feature of the proposed test is that it is flexible and developed for both parametric and nonparametric models within a unified framework, and is more powerful than the standard test by accounting for the correlation among genes and hence often uses a much smaller degrees of freedom. We investigate the theoretical properties of the proposed test. We evaluate its finite sample performance using simulation studies, and apply the method to the Michigan prostate cancer gene expression data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.