SynthesisAmerican journal of human genetics2013
Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
Synthesis in American journal of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 11 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
47 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies.Nature genetics · 2025Pooled it
- Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.Nature genetics · 2023Pooled it
- Trans-ethnic meta-analysis of rare variants in sequencing association studies.Biostatistics (Oxford, England) · 2021Pooled it
- Variance-component-based meta-analysis of gene-environment interactions for rare variants.G3 (Bethesda, Md.) · 2021Pooled it
- Subset testing and analysis of multiple phenotypes.Genetic epidemiology · 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
- A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.Genetic epidemiology · 2018Pooled it
- A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.PLoS genetics · 2016Pooled it
- Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.Genetics · 2016Pooled it
- Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.American journal of human genetics · 2015Pooled it
- Meta-analysis of gene-level tests for rare variant association.Nature genetics · 2014Pooled it
- A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.PLoS genetics · 2024Article
- eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.PLoS genetics · 2022Article
- Simultaneous detection of novel genes and SNPs by adaptiveFrontiers in genetics · 2022Article
- Exome Sequencing of 21 Bardet-Biedl Syndrome (BBS) Genes to Identify Obesity Variants in 6,851 American Indians.Obesity (Silver Spring, Md.) · 2021Article
- Patterns of Human Leukocyte Antigen Class I and Class II Associations and Cancer.Cancer research · 2021Article
- BTOB: Extending the Biased GWAS to Bivariate GWAS.Frontiers in genetics · 2021Article
- A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.PLoS genetics · 2020Article
- Multi-trait analysis of rare-variant association summary statistics using MTAR.Nature communications · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.
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.