ArticleBioinformatics (Oxford, England)2018
VarExp: estimating variance explained by genome-wide GxE summary statistics.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploiting meta-analysis of genome-wide interaction with serum 25-hydroxyvitamin D to identify novel genetic loci associated with pulmonary function.The American journal of clinical nutrition · 2024Pooled it
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Gene-lifestyle interactions in the genomics of human complex traits.European journal of human genetics : EJHG · 2022Article
- Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure.Molecular psychiatry · 2021Article
- GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data.Genome biology · 2021Article
- Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.Behavior genetics · 2021Article
- Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits.HGG advances · 2021Article
- Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.Nature communications · 2019Article
- Gene × Environment Interaction in Developmental Disorders: Where Do We Stand and What's Next?Frontiers in psychology · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Summary: Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, 'VarExp', that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users. Availability and implementation: The R package is available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.