Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: Current and developing technologies
Open accessAbstract
The most common form of genetic variation, single nucleotide polymorphisms or SNPs, can affect the way an individual responds to the environment and modify disease risk. Although most of the millions of SNPs have little or no effect on gene regulation and protein activity, there are many circumstances where base changes can have deleterious effects. Non-synonymous SNPs that result in amino acid changes in proteins have been studied because of their obvious impact on protein activity. It is well known that SNPs within regulatory regions of the genome can result in disregulation of gene transcription. However, the impact of SNPs located in putative regulatory regions, or rSNPs, is harder to predict for two primary reasons. First, the mechanistic roles of non-coding genomic sequence remain poorly defined. Second, experimental validation of the functional consequences of rSNPs is often slow and laborious. In this review, we summarize traditional and novel methodologies for candidate rSNPs selection, in particular in silico techniques that aid in candidate rSNP selection. Additionally we will discuss molecular biological techniques that assess the impact of rSNPs on binding of regulatory machinery, as well as functional consequences on transcription. Standard techniques such as EMSA and luciferase reporter constructs are still widely used to assess effects of rSNPs on binding and gene transcription; however, these protocols are often bottlenecks in the discovery process. Therefore, we highlight novel and developing high-throughput protocols that promise to aid in shortening the process of rSNP validation. Given the large amount of genomic information generated from a multitude of re-sequencing and genome-wide SNP array efforts, future focus should be to develop validation techniques that will allow greater understanding of the impact these polymorphisms have on human health and disease.
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We still lack efficient procedures for identifying enhancers and other regulatory regions that polymorphisms could disrupt.
“we still lack efficient procedures for identifying enhancers and other regulatory regions that polymorphisms could disrupt38.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [38]
Few GWAS-significant associations have been confirmed as the true risk variant through molecular, biochemical, or biophysical work.
“Very few of the thousands of GWAS significant associations have been shown using molecular genetics, biochemistry or biophysics to be the actual risk variant38, 109.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [38]
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Flagged first, then by citations
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- Authors
- CHORLEY, B., WANG, X., CAMPBELL, M., PITTMAN, G., NOUREDDINE, M., & BELL, D.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- BELL, D
- Venue
- Mutation Research/Reviews in Mutation Research
- Publisher
- Elsevier BV
- Published
- 2008-07
- Volume
- 659
- Issue
- 1-2
- Pages
- 147-157
- ISSN
- 1383-5742 (ISSN-L)
- PMC
- PMC2676583
- OpenAlex
- W2014783044
- Cited by
- 177
- References
- 109
- Citations by year
- 2025: 5 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 3 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 7 · 2016: 19 · 2015: 8 · 2014: 16 · 2013: 14 · 2012: 19
- FWCI
- 10.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Mutation Research/Reviews in Mutation Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z01-ES100475)
- National Institute of Environmental Health Sciences (Z01ES100475)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- single nucleotide polymorphisms, regulatory single nucleotide polymorphisms, gene expression regulation, transcriptional regulation, in silico prediction, transcription factor binding, electrophoretic mobility shift assay, luciferase reporter assay, disease risk
- MeSH terms
- Gene Expression Regulation, Genotype, Humans, Phenotype, Regulatory Sequences, Nucleic Acid, Computational Biology, Polymorphism, Single Nucleotide, Genomic Instability, Toxicogenetics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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