ReviewFrontiers in genetics2020
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.
Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 of them syntheses 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
82 citing papers in PubMed, 2 syntheses or guidelines pooled it, 167 citations in OpenAlex.
- Genome-Wide Meta-Analysis Identifies Multiple Novel Rare Variants to Predict Common Human Infectious Diseases Risk.International journal of molecular sciences · 2023Pooled it
- Machine learning-based meta-analysis of colorectal cancer and inflammatory bowel disease.PloS one · 2023Pooled it
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Machine learning-driven identification of virulence determinants in Borrelia burgdorferi associated with human dissemination.PLoS computational biology · 2026Article
- Genomic Analysis of Resistance toPathogens (Basel, Switzerland) · 2026Article
- Article
- Inferring genotype-phenotype maps using attention models.PNAS nexus · 2026Article
- Review
- Transformer-based InsightGWAS improves GERD genetic discovery via pretraining on GWAS for major depressive disorder.Communications biology · 2026Article
- Article
- Candidate genes for anthracnose resistance in Senegalese sorghum: a machine learning-based exploration.Functional & integrative genomics · 2025Article
- Transformer-based deep learning enhances discovery in migraine GWAS.Nature communications · 2025Article
- Potential synthetic associations created by epistasis.Genome biology · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Genetic Architecture of Ischemic Stroke: Insights from Genome-Wide Association Studies and Beyond.Journal of cardiovascular development and disease · 2025Review
- Bridging Genomics to Cardiology Clinical Practice: Artificial Intelligence in Optimizing Polygenic Risk Scores: A Systematic Review.JACC. Advances · 2025Review
- Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.Plant, cell & environment · 2025Article
- Combating Root-Knot Nematodes (Plants (Basel, Switzerland) · 2025Review
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft.Human mutation · 2025Review
22 more citing papers are in PubMed but not listed here.
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.
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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.