ArticleGenetic epidemiology2013
A unified mixed-effects model for rare-variant association in sequencing studies.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Discovery of common and rare genetic risk variants for colorectal cancer.Nature genetics · 2019Pooled it
- Monoallelic pathogenic variants in LEPR do not cause obesity.American journal of human genetics · 2024Trial
- An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.Briefings in bioinformatics · 2026Article
- cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions.Nature methods · 2026Article
- MIRAGE: A Bayesian statistical method for gene-level rare-variant analysis incorporating functional annotations.American journal of human genetics · 2026Article
- The role of SLC19A2 variants in the wide spectrum of non-autoimmune abnormalities of glucose homeostasis.Diabetologia · 2025Article
- Rare-variant association studies: When are aggregation tests more powerful than single-variant tests?American journal of human genetics · 2025Article
- Article
- Pathway-based genetic association analysis for overdispersed count data.Journal of applied statistics · 2025Article
- Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature.BMC medical research methodology · 2024Article
- Integration of variant annotations using deep set networks boosts rare variant association testing.Nature genetics · 2024Article
- Functional genetics reveals the contribution of delta opioid receptor to type 2 diabetes and beta-cell function.Nature communications · 2024Article
- Structured testing of genetic association with mixed clinical outcomes.Genetic epidemiology · 2024Article
- Review
- Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.Journal of translational medicine · 2024Article
- RAVAR: a curated repository for rare variant-trait associations.Nucleic acids research · 2024Article
- TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.Frontiers in genetics · 2024Article
- FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.PLoS genetics · 2023Article
- Elucidating the Risk of Colorectal Cancer for Variants in Hereditary Colorectal Cancer Genes.Gastroenterology · 2023Article
- Human GLP1R variants affecting GLP1R cell surface expression are associated with impaired glucose control and increased adiposity.Nature metabolism · 2023Article
28 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
Abstract
For rare-variant association analysis, due to extreme low frequencies of these variants, it is necessary to aggregate them by a prior set (e.g., genes and pathways) in order to achieve adequate power. In this paper, we consider hierarchical models to relate a set of rare variants to phenotype by modeling the effects of variants as a function of variant characteristics while allowing for variant-specific effect (heterogeneity). We derive a set of two score statistics, testing the group effect by variant characteristics and the heterogeneity effect. We make a novel modification to these score statistics so that they are independent under the null hypothesis and their asymptotic distributions can be derived. As a result, the computational burden is greatly reduced compared with permutation-based tests. Our approach provides a general testing framework for rare variants association, which includes many commonly used tests, such as the burden test [Li and Leal, 2008] and the sequence kernel association test [Wu et al., 2011], as special cases. Furthermore, in contrast to these tests, our proposed test has an added capacity to identify which components of variant characteristics and heterogeneity contribute to the association. Simulations under a wide range of scenarios show that the proposed test is valid, robust, and powerful. An application to the Dallas Heart Study illustrates that apart from identifying genes with significant associations, the new method also provides additional information regarding the source of the association. Such information may be useful for generating hypothesis in future studies.
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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.