ArticleHuman heredity2010
A data-adaptive sum test for disease association with multiple common or rare variants.
Article in Human heredity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 2 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
206 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Functional variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene are associated with increased risk of colorectal cancer.Human molecular genetics · 2025Pooled it
- Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.Genetics · 2016Pooled it
- Divided-and-combined association test for pleiotropic effects with GWAS summary statistics.BMC genomics · 2026Article
- Archipelago Method for Variant Set Association Test Statistics.Genetic epidemiology · 2026Article
- MIRAGE: A Bayesian statistical method for gene-level rare-variant analysis incorporating functional annotations.American journal of human genetics · 2026Article
- Rare-variant association studies: When are aggregation tests more powerful than single-variant tests?American journal of human genetics · 2025Article
- Empowering genome-wide association studies via a visualizable test based on the regional association score.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application.Mitochondrion · 2024Article
- Association analysis of mitochondrial DNA heteroplasmic variants: methods and application.medRxiv : the preprint server for health sciences · 2024Article
- SIGNET: transcriptome-wide causal inference for gene regulatory networks.Scientific reports · 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
- SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory Networks.Research square · 2023Article
- Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.Bioinformatics (Oxford, England) · 2023Article
- Article
- A Whole-Genome Sequencing Study Implicates GRAMD1B in Multiple Sclerosis Susceptibility.Genes · 2022Article
- Gene-Based Methods for Estimating the Degree of the Skewness of X Chromosome Inactivation.Genes · 2022Article
- Article
- Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine.Frontiers in molecular medicine · 2022Review
- Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model.Frontiers in genetics · 2022Article
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
146 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
2 authors.
Funding
Abstract
Since associations between complex diseases and common variants are typically weak, and approaches to genotyping rare variants (e.g. by next-generation resequencing) multiply, there is an urgent demand to develop powerful association tests that are able to detect disease associations with both common and rare variants. In this article we present such a test. It is based on data-adaptive modifications to a so-called Sum test originally proposed for common variants, which aims to strike a balance between utilizing information on multiple markers in linkage disequilibrium and reducing the cost of large degrees of freedom or of multiple testing adjustment. When applied to multiple common or rare variants in a candidate region, the proposed test is easy to use with 1 degree of freedom and without the need for multiple testing adjustment. We show that the proposed test has high power across a wide range of scenarios with either common or rare variants, or both. In particular, in some situations the proposed test performs better than several commonly used methods.
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.