ReviewBioinformatics (Oxford, England)2010
Bioinformatics challenges for genome-wide association studies.
Review in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 232 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
232 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pathway Analysis of Renal Cell Carcinoma Genome-Wide Association Studies Identifies Novel Associations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Pooled it
- A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.PLoS genetics · 2016Pooled it
- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- Genome-wide association and MaODR-based multi-locus interaction analyses reveal a susceptibility gene network for newly identified metabolic syndrome.Genome biology · 2026Article
- SAGA (Simplified Association Genome-wide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association Studies.bioRxiv : the preprint server for biology · 2025Article
- deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.Scientific reports · 2025Article
- Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.Nutrients · 2025Review
- Review
- Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.Bioinformatics (Oxford, England) · 2025Article
- Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.BMC genomics · 2025Article
- How "Omics" Studies Contribute to a Better Understanding of Fuchs' Endothelial Corneal Dystrophy.Current issues in molecular biology · 2025Review
- OsLB2.2 negatively regulates rice disease resistance at seedling stage in rice.Frontiers in plant science · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.BMC public health · 2024Article
- High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma.Human molecular genetics · 2024Article
- Review
- SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings.Bioinformatics advances · 2024Article
- Identification of a regulatory pathway governing TRAF1 via an arthritis-associated non-coding variant.Cell genomics · 2023Article
- Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework.Mathematics (Basel, Switzerland) · 2023Article
172 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
3 authors.
Funding
Abstract
motivationThe sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype-phenotype relationship that is characterized by significant heterogeneity and gene-gene and gene-environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.
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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.