ArticleExperimental biology and medicine (Maywood, N.J.)2017
Challenges and progress in interpretation of non-coding genetic variants associated with human disease.
Article in Experimental biology and medicine (Maywood, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Systematic Benchmarking of DNA Sequence Encoding Strategies for Predicting Regulatory Effects of Non-Coding SNPs.International journal of molecular sciences · 2026Article
- TCF7L2 promotes abdominal aortic aneurysm through smooth muscle cell-mediated extracellular matrix remodeling.JCI insight · 2026Article
- Causal genes for osteoporosis: Mendelian randomization analysis with multilayer xQTL data.Medicine · 2026Article
- Integrative genomic analysis of 21 orofacial diseases identifies shared genetic architecture with systemic diseases.Nature communications · 2026Article
- Functional Variant Discovery Identifies a Novel Genetic Link between SPRY2, Wood Smoke, and Asthma.American journal of respiratory cell and molecular biology · 2026Article
- Multi-tissue transcriptome-wide association study identifies 29 risk genes associated with attention-deficit/hyperactivity disorder.medRxiv : the preprint server for health sciences · 2026Article
- Beyond the Exome: The Role of Noncoding and Regulatory Variants in Monogenic Diseases.Current issues in molecular biology · 2025Review
- AnGenes · 2025Article
- A single non-coding SNP in FPGS modulates folate drug efficacy in acute lymphoblastic leukemia: data-driven exploration and experimental validation.Molecular biomedicine · 2025Article
- Review
- Identification and Copy Number Variant Analysis of Enhancer Regions of Genes Causing Spinocerebellar Ataxia.International journal of molecular sciences · 2024Article
- G-quadruplexes as pivotal components of cis-regulatory elements in the human genome.BMC biology · 2024Article
- Improving genetic risk modeling of dementia from real-world data in underrepresented populations.Communications biology · 2024Article
- Increasing the diagnostic yield of childhood glaucoma cases recruited into the 100,000 Genomes Project.BMC genomics · 2024Article
- Analysis of long-range chromatin contacts, compartments and looping between mouse embryonic stem cells, lens epithelium and lens fibers.Epigenetics & chromatin · 2024Article
- Identification and functional validation of an enhancer variant in the 9p21.3 locus associated with glaucoma risk and elevated expression of p16Aging cell · 2023Article
- A functional mechanism for a non-coding variant near AGTR2 associated with risk for preterm birth.BMC medicine · 2023Article
- Computational Assessment of the Expression-modulating Potential for Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
- Identification and functional validation of an enhancer variant in the 9p21.3 locus associated with glaucoma risk and elevated expression ofbioRxiv : the preprint server for biology · 2023Article
- POSTRE: a tool to predict the pathological effects of human structural variants.Nucleic acids research · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.
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