ArticleHuman heredity2013
A network-based kernel machine test for the identification of risk pathways in genome-wide association studies.
Article in Human heredity, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of Genetic Variation as a Predictive Biomarker for Chronic Graft-Versus-Host-Disease After Allogeneic Stem Cell Transplantation.Frontiers in immunology · 2020Pooled it
- TreeKernel: interpretable kernel machine tests for interactions between -omics and clinical predictors with applications to metabolomics and COPD phenotypes.BMC bioinformatics · 2023Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.Frontiers in genetics · 2022Article
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- Article
- KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.The annals of applied statistics · 2018Article
- Pathway-induced allelic spectra of diseases in the presence of strong genetic effects.Human genetics · 2018Article
- Relating drug response to epigenetic and genetic markers using a region-based kernel score test.BMC proceedings · 2018Article
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.Computational and mathematical methods in medicine · 2017Article
- META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies.PloS one · 2015Article
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Authors and funding
9 authors.
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Abstract
Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.
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