ArticleAmerican journal of human genetics2010
Powerful SNP-set analysis for case-control genome-wide association studies.
Article in American journal of human genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 379 papers, 4 of them syntheses that pooled it.
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Who cites it
379 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Rare genetic variants involved in increased risk of paroxysmal atrial fibrillation in a Japanese population.Scientific reports · 2025Pooled it
- Identification and validation of supervariants reveal novel loci associated with human white matter microstructure.Genome research · 2024Pooled it
- A gene based combination test using GWAS summary data.BMC bioinformatics · 2023Pooled it
- A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.PLoS genetics · 2016Pooled it
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- Kernel-smoothed permutation for extreme P-value estimation in genetic association studies.Genetics · 2026Article
- 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
- Impact of control selection strategies on GWAS results: a study of prostate cancer in the UK Biobank.Briefings in bioinformatics · 2026Article
- MOKA: a pipeline for multiomics bridged SNP-set kernel association test.G3 (Bethesda, Md.) · 2026Article
- ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits.Briefings in bioinformatics · 2026Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- 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
- Introduction of FixFRM package for gene-based association test and its innovative application to mixture exposure analysis.Scientific reports · 2025Article
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- Genetic Architecture Underlying Response to the Fungal PathogenEvolutionary applications · 2025Article
- Genetic variations in extracellular matrix degradation pathways linking to major depressive disorder: Evidence from a large-scale genetic association study.BioMedicine · 2025Article
- Targeting the epigenome and tumor heterogeneity: advances in immunotherapy for chemoresistant metastatic colorectal cancer.Frontiers in immunology · 2025Review
- General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.Genetic epidemiology · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
319 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.
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