ReviewBriefings in bioinformatics2019
Post genome-wide association analysis: dissecting computational pathway/network-based approaches.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reviewing and assessing existing meta-analysis models and tools.Briefings in bioinformatics · 2021Pooled it
- Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.Briefings in bioinformatics · 2026Article
- Proteome-wide association study of prostate cancer risk across populations.Nature communications · 2025Article
- A review of post-GWAS studies in schizophrenia.Translational psychiatry · 2025Review
- Data simulation to optimize frameworks for genome-wide association studies in diverse populations.Frontiers in genetics · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.Human genomics · 2024Article
- Multiple sclerosis and computational biology (Review).Biomedical reports · 2022Review
- Polygenic Risk Score in African populations: progress and challenges.F1000Research · 2022Review
- Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques.Science China. Life sciences · 2021Review
- IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.Briefings in bioinformatics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.
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.