ArticleStatistics in biosciences2015
An Adaptive Genetic Association Test Using Double Kernel Machines.
Article in Statistics in biosciences, 2015. 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.
- An Adaptive Multivariate Two-Sample Test With Application to Microbiome Differential Abundance Analysis.Frontiers in genetics · 2019Article
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- A novel copy number variants kernel association test with application to autism spectrum disorders studies.Bioinformatics (Oxford, England) · 2016Article
- Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data.BMC bioinformatics · 2015Article
- Incorporating auxiliary information for improved prediction using combination of kernel machines.Statistical methodology · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.
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