ReviewFrontiers in genetics2020
From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases.
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 366 papers, 5 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
366 citing papers in PubMed, 5 syntheses or guidelines pooled it, 607 citations in OpenAlex.
- Variants in the DDX6-CXCR5 autoimmune disease risk locus influence the regulatory network in immune cells and salivary gland.Annals of the rheumatic diseases · 2025Pooled it
- A meta-analysis of genome-wide association studies revealed significant QTL and candidate genes for loin muscle area in three breeding pigs.Scientific reports · 2025Pooled it
- Colorectal Cancer Risk Loci: Prognostic Factors for Clinical Outcomes? A Systematic Review and Meta-Analysis.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.Genome medicine · 2024Pooled it
- Inflammatory cytokines and oral lichen planus: a Mendelian randomization study.Frontiers in immunology · 2024Pooled it
- Genetic background and multidomain interventions in mild cognitive impairment.Alzheimer's research & therapy · 2025Trial
- Single-allele nanoscale mapping of regulatory variants.Nature genetics · 2026Article
- Functional characterization of the 9q34.13 locus identifies RAPGEF1 as a candidate gene modulating risk for melanoma and nevi via RAS activation.American journal of human genetics · 2026Article
- Using Polygenic Risk Scores to Refine Risk and Outcomes in High-grade Serous Ovarian Cancer in Australia.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Point mutations and complex variants impact gene expression and addiction-related behaviors in Heterogeneous Stock rats.bioRxiv : the preprint server for biology · 2026Article
- A new era of genome-wide association studies in the field of Alzheimer's disease and overlapping co-pathologies: lessons learned from a neuropathology-centered approach.Acta neuropathologica · 2026Review
- Genetic and molecular evidence linking CTSH to Alzheimer's disease pathophysiology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Tissue-resident immune cells drive genetic risk in autoimmune and lung diseases.Nature immunology · 2026Article
- From polygenic risk to functional genomics: a framework for precision gynecological disease modeling.Nature communications · 2026Review
- Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.Journal of translational medicine · 2026Article
- Integrative genetic analysis identifies shared regulation of DNA methylation and gene expression in migraine risk.The journal of headache and pain · 2026Article
- Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.Science advances · 2026Article
- Mendelian randomization in ovarian research: applications in PCOS, ovarian aging, and assisted reproductive technology.Journal of ovarian research · 2026Review
- Advancing risk gene discovery across the allele frequency spectrum.HGG advances · 2026Review
- Population epigenetics: deciphering DNA methylation diversity and its implications for health, disease, and evolution.Molecular biology and evolution · 2026Review
306 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.
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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.