SynthesisAmerican journal of human genetics2015
Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.
Synthesis in American journal of human genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 4 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Scalable and accurate rare variant meta-analysis with Meta-SAIGE.Nature genetics · 2025Pooled it
- Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.Genes · 2020Pooled 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
- Fast and reliable association discovery in large-scale microbiome studies and meta-analyses using PALM.bioRxiv : the preprint server for biology · 2026Article
- Whole genome sequence analysis of pulmonary function and COPD in 44,287 multi-ancestry participants.Genome biology · 2026Article
- Environmental data provide marginal benefit for predicting climate adaptation.PLoS genetics · 2025Article
- Rare coding variants in RCN3 are associated with blood pressure.BMC genomics · 2022Article
- Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed.Cell genomics · 2022Article
- Examining the effect of mitochondrial DNA variants on blood pressure in two Finnish cohorts.Scientific reports · 2021Article
- PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.Genome biology · 2020Article
- Multi-trait analysis of rare-variant association summary statistics using MTAR.Nature communications · 2020Article
- Combining sequence data from multiple studies: Impact of analysis strategies on rare variant calling and association results.Genetic epidemiology · 2020Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies.PloS one · 2020Article
- Review
- Novel serum metabolites associate with cognition phenotypes among Bogalusa Heart Study participants.Aging · 2019Article
- A fully adjusted two-stage procedure for rank-normalization in genetic association studies.Genetic epidemiology · 2019Article
- metaFARVAT: An Efficient Tool for Meta-Analysis of Family-Based, Case-Control, and Population-Based Rare Variant Association Studies.Frontiers in genetics · 2019Article
- Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern.Briefings in bioinformatics · 2018Article
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 at 2 institutions in 1 country.
Funding
Abstract
There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.
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.