ArticleGenetics2014
A powerful and adaptive association test for rare variants.
Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 2 of them syntheses that pooled it.
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Who cites it
114 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.Genetic epidemiology · 2022Pooled it
- Hybrid test for publication bias in meta-analysis.Statistical methods in medical research · 2020Pooled it
- Parametric hypothesis testing for pathway based hierarchical structural component models.Genes & genomics · 2026Article
- Nonlinear kernel-based high-dimensional inference for set-based genetic association studies.Briefings in bioinformatics · 2026Article
- Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.Human brain mapping · 2026Article
- Flexible nonparametric assessment of time-lag bias in meta-analysis.Biostatistics (Oxford, England) · 2026Article
- Alternative tests and measures for between-study inconsistency in meta-analysis.BMC medical research methodology · 2025Article
- A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer.Entropy (Basel, Switzerland) · 2025Article
- Health utility adjusted survival: A composite endpoint for clinical trial designs.Statistical methods in medical research · 2025Article
- Hierarchical structural component model for pathway analysis of multinomial phenotypes.Genes & genomics · 2025Article
- Health Utility Survival for Randomized Clinical Trials: Extensions and Statistical Properties.Statistics in medicine · 2025Article
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- A Novel, Variance Component-Based Method for Detecting Brain-Behavior Associations in Neuroimaging Data.Statistics and data science in imaging · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Powerful Test of Heterogeneity in Two-Sample Summary-Data Mendelian Randomization.Statistics in medicine · 2024Article
- RAVAR: a curated repository for rare variant-trait associations.Nucleic acids research · 2024Article
- Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.Bioinformatics (Oxford, England) · 2023Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Haplotype based testing for a better understanding of the selective architecture.BMC bioinformatics · 2023Article
- Two-stage multivariate Mendelian randomization on multiple outcomes with mixed distributions.Statistical methods in medical research · 2023Article
54 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.
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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.