ArticleAmerican journal of human genetics2008
Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.
Article in American journal of human genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,040 papers, 7 of them syntheses that pooled it.
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Who cites it
1,040 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Empirically determined baseline masking strategies and other considerations for gene-level burden tests.Nature genetics · 2026Pooled it
- Correcting for Genomic Inflation Leads to Loss of Power in Large-Scale Genome-Wide Association Study Meta-Analysis.Genetic epidemiology · 2025Pooled it
- Clinical spectrum of Transthyretin amyloidogenic mutations among diverse population origins.Human genomics · 2024Pooled it
- A gene based combination test using GWAS summary data.BMC bioinformatics · 2023Pooled it
- Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.Nature genetics · 2023Pooled it
- Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2.Molecular psychiatry · 2022Pooled it
- Variance-component-based meta-analysis of gene-environment interactions for rare variants.G3 (Bethesda, Md.) · 2021Pooled it
- OPA1 controls mitochondrial dysfunction-driven liver fibrosis in MASLD.bioRxiv : the preprint server for biology · 2026Article
- Article
- Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.Nature communications · 2026Article
- Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026Article
- Local ancestry-informed rare variant burden testing improves gene discovery in admixed populations.medRxiv : the preprint server for health sciences · 2026Article
- NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease.Cell genomics · 2026Article
- When network biology meets human genetics.Cell genomics · 2026Article
- Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.Briefings in bioinformatics · 2026Article
- Whole-Exome Analysis Identifies Candidate Genes Associated With Diabetic Retinopathy.Investigative ophthalmology & visual science · 2026Article
- Beyond complement: comprehensive assessment of factor XIII plasma dynamics and genetics in atypical hemolytic uremic syndrome.Research and practice in thrombosis and haemostasis · 2026Article
- HiFiMAP: High-resolution fast identity-by-descent mapping test.medRxiv : the preprint server for health sciences · 2026Article
- RAREsim2: flexible simulation of rare variant genetic data using real haplotypes.Bioinformatics (Oxford, England) · 2026Article
- High-Penetrance Rare Variants Underlying Familial Lung Cancer Risk: Insights From Genetic Epidemiology of Lung Cancer Consortium.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026Article
980 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.
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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.