ArticleGenetic epidemiology2014
Meta-analysis of sequencing studies with heterogeneous genetic associations.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 6 of them syntheses that pooled it.
What it found
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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.
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Who cites it
21 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures.Genome biology · 2025Pooled it
- Trans-ethnic meta-analysis of rare variants in sequencing association studies.Biostatistics (Oxford, England) · 2021Pooled it
- Subset testing and analysis of multiple phenotypes.Genetic epidemiology · 2019Pooled it
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.PLoS genetics · 2018Pooled it
- Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.American journal of human genetics · 2015Pooled it
- Comparison of contralateral muscle excitation in proximal versus distal muscles in the upper extremities.Frontiers in human neuroscience · 2025Article
- Multi-trait analysis of rare-variant association summary statistics using MTAR.Nature communications · 2020Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- Rare-variant collapsing analyses for complex traits: guidelines and applications.Nature reviews. Genetics · 2019Review
- metaFARVAT: An Efficient Tool for Meta-Analysis of Family-Based, Case-Control, and Population-Based Rare Variant Association Studies.Frontiers in genetics · 2019Article
- Meta-analysis approaches to combine multiple gene set enrichment studies.Statistics in medicine · 2018Article
- Integrative gene set enrichment analysis utilizing isoform-specific expression.Genetic epidemiology · 2017Article
- PreMeta: a tool to facilitate meta-analysis of rare-variant associations.BMC genomics · 2017Article
- A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.Biometrics · 2016Article
- Discovery of rare variants for complex phenotypes.Human genetics · 2016Review
- Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations.Genetic epidemiology · 2016Article
- SEQMINER: An R-Package to Facilitate the Functional Interpretation of Sequence-Based Associations.Genetic epidemiology · 2015Article
- Article
- A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing.Frontiers in genetics · 2015Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random-effects models which allow the genetic effects to vary among studies and develop the corresponding meta-analysis methods for gene-level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random-effects versions of all commonly used gene-level association tests, including burden, variable threshold, and variance-component tests. We demonstrate through extensive simulation studies that our random-effects tests are substantially more powerful than the fixed-effects tests in the presence of moderate and high between-study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.
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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.