ReviewTrends in genetics : TIG2014
Functional and genomic context in pathway analysis of GWAS data.
Review in Trends in genetics : TIG, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 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
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Circadian pathway genetic variation and cancer risk: evidence from genome-wide association studies.BMC medicine · 2018Pooled it
- Gene set enrichment analysis to create polygenic scores: a developmental examination of aggression.Translational psychiatry · 2019Trial
- Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis.American journal of respiratory cell and molecular biology · 2017Trial
- An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models.Nature communications · 2026Article
- Knowledge-guided contextual gene set analysis with large language models.Bioinformatics (Oxford, England) · 2026Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.Cell genomics · 2023Article
- Multi-scale systems genomics analysis predicts pathways, cell types, and drug targets involved in normative variation in peri-adolescent human cognition.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Modern drug discovery for inflammatory bowel disease: The role of computational methods.World journal of gastroenterology · 2023Review
- Hierarchical association of COPD to principal genetic components of biological systems.PloS one · 2023Article
- Review
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association.Circulation · 2021Review
- ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles.Patterns (New York, N.Y.) · 2021Article
- Mantis: flexible and consensus-driven genome annotation.GigaScience · 2021Article
- A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.American journal of human genetics · 2021Article
- CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.PLoS computational biology · 2021Article
- Bioinformatics analysis of epigenetic and SNP-related molecular markers in systemic lupus erythematosus.American journal of translational research · 2021Article
- Article
- Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.Scientific reports · 2020Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA - such as how gene sets are defined, how single-nucleotide polymorphisms (SNPs) are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects - will improve the interpretability and comparability of results across methods and studies. In this review we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results.
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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.