ArticleGenetic epidemiology2013
Kernel machine SNP-set testing under multiple candidate kernels.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.Briefings in bioinformatics · 2026Article
- Multi-marker testing based on accelerated failure time models under possible left truncation and competing risks.Briefings in bioinformatics · 2026Article
- Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for whole-genome sequencing data.bioRxiv : the preprint server for biology · 2025Article
- General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.Genetic epidemiology · 2025Article
- A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.Bioinformatics (Oxford, England) · 2024Article
- BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.Briefings in bioinformatics · 2024Article
- Article
- A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.BMC bioinformatics · 2022Article
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- Genetic architecture of salt tolerance in a Multi-Parent Advanced Generation Inter-Cross (MAGIC) cowpea population.BMC genomics · 2022Article
- 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
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
- A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics.BMC medicine · 2021Article
- Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.Journal of translational medicine · 2021Article
- IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.PLoS computational biology · 2021Article
- Convex combination sequence kernel association test for rare-variant studies.Genetic epidemiology · 2020Article
- Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets.BMC medical genomics · 2020Article
- Presence of CC Genotype for rs17773430 Could Affect the Percentage of Excess Weight Loss 1 Year After Bariatric Surgery: Tehran Obesity Treatment Study (TOTS).Obesity surgery · 2020Article
- Article
- Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.Genetic epidemiology · 2019Article
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9 authors.
Funding
Abstract
Joint testing for the cumulative effect of multiple single-nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large-scale genetic association studies. The kernel machine (KM)-testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.
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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.