ArticleAmerican journal of human genetics2012
HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis.
Article in American journal of human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 13 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 13 syntheses or guidelines pooled it.
- A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis.Nature communications · 2019Pooled it
- Genome-wide association meta-analysis of age at first cannabis use.Addiction (Abingdon, England) · 2018Pooled it
- Novel genetic loci associated with hippocampal volume.Nature communications · 2017Pooled it
- Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis.Molecular psychiatry · 2017Pooled it
- A genome-wide association meta-analysis on apolipoprotein A-IV concentrations.Human molecular genetics · 2016Pooled it
- Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus.Nature genetics · 2016Pooled 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
- Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium.Translational psychiatry · 2016Pooled it
- Meta-analysis of Positive and Negative Symptoms Reveals Schizophrenia Modifier Genes.Schizophrenia bulletin · 2016Pooled it
- Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium.Behavior genetics · 2016Pooled it
- Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects, and tissue-specific enrichment of eQTLs.Scientific reports · 2016Pooled it
- Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults.Journal of Alzheimer's disease : JAD · 2015Pooled it
- Meta-analysis of genome-wide association studies identifies two loci associated with circulating osteoprotegerin levels.Human molecular genetics · 2014Pooled it
- The common variants implicated in microstructural abnormality of first episode and drug-naïve patients with schizophrenia.Scientific reports · 2017Trial
- Parametric hypothesis testing for pathway based hierarchical structural component models.Genes & genomics · 2026Article
- MARSweb: a fully automated web service for set-based association testing.BMC genomics · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method.Genetic epidemiology · 2021Article
- MARS: leveraging allelic heterogeneity to increase power of association testing.Genome biology · 2021Article
- Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning.Patterns (New York, N.Y.) · 2020Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.