ArticleAmerican journal of human genetics2019
A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.Bioinformatics (Oxford, England) · 2023Pooled it
- Schizophrenia: from mechanism to therapy.Science China. Life sciences · 2026Review
- Multi-omics Analysis Revealed the Diagnostic and Therapeutic Value of Immunogenic Cell Death-derived SCN5A in Hepatocellular Carcinoma.Molecular biotechnology · 2026Article
- SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations.Genome biology · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- Genome-wide interaction association analysis identifies interactive effects of childhood maltreatment and kynurenine pathway on depression.Nature communications · 2025Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Many roads to a gene-environment interaction.American journal of human genetics · 2024Review
- Genome-Wide Interaction Study of Dietary Intake and Colorectal Cancer Risk in the UK Biobank.JAMA network open · 2024Article
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
- GEM: scalable and flexible gene-environment interaction analysis in millions of samples.Bioinformatics (Oxford, England) · 2021Article
- Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c.Human molecular genetics · 2021Article
- Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes.American journal of human genetics · 2021Article
- Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data.Frontiers in genetics · 2021Review
- Efficient gene-environment interaction tests for large biobank-scale sequencing studies.Genetic epidemiology · 2020Article
- A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.American journal of human genetics · 2020Article
- UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test.American journal of human genetics · 2020Article
- Statistical methods for gene-environment interaction analysis.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.
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What Socratic 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 Socratic graph.