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Single Nucleotide Polymorphisms (SNPs)

By: Mihir Vishwarupe, Monta Vista High School

Introduction to SNPs & GWAS

Single Nucleotide Polymorphisms (SNPs) are biological markers in DNA that are associated with some diseases. SNPs are characterized as single nucleotide differences in the population that appear approximately every 300 base pairs, and can be found on either the coding or noncoding regions of a gene. [1] Genome-wide association studies, also known as GWAS, enable identification of genes associated with a particular disease (or a specific trait). The vast genome can make it difficult to understand how and where a particular hereditary-linked trait is found in the genome. Single nucleotide variations occurring in many individuals can be classified as SNPs if the variant is found in at least 1% of the population. [2]

In order to comprehend the entirety of GWAS and analyze multiple SNPs in an individual's DNA, Manhattan plots are often used. On the x-axis is the position of a SNP, and on the y axis is its respective p-value — which can be drawn as a horizontal line across the y-axis. These SNPs are then graphed on the plot and are typically shown for each chromosome. [3] Manhattan plots are particularly important for understanding GWAS, especially because it can be used to detect and/or analyze a cause of disease based on the SNPs that have been plotted. [3]

This Manhattan plot represents the genes and SNPs involved in kidney stone disease, one of the diseases that can be used to be represented with GWAS. [4]

Understanding the significance of specific SNPs in individuals is essential as differences that underlie a patient's SNPs can be used to identify the inheritance of a disease that runs in a family's history, and thus provide insights into why a certain individual may have developed Cardiovascular Disease (CVD), diabetes, obesity, among other diseases, while other individuals do not. [5]

GWAS and SNPs research has also identified risk SNPs for certain cancers. SNPs in both coding and noncoding regions of genes have been related to the development of cancer, and could provide key insights into how these nucleotides can influence the development of how such diseases can be detected. However, there is currently a lack of knowledge on the topic of SNPs tied with the development of cancer. [6]

Current Scientific Applications

Currently, scientists are building upon the use of SNPs through GWAS — targeting a risk SNPs in haplotypes from the wide variation of SNPs, and comparing a genetically linked disease in affected against non affected individuals. Through advancing technology, scientists can now use "microarray chips'' to scan DNA and analyze individuals' specific SNP sequence(s) by extracting somatic cells from a body and analyzing the DNA. [7] For example, in a 2018 study, researchers identified several key super enhancer SNPs related to Type 2 Diabetes (T2D), which could provide insight into the contraction of T2D in some patients, and not others. [8] These findings can be applied to different platforms as "microarray chips" that have been developed to detect specific SNPs to provide crucial significance into how a disease like T2D can be detected using these "chips." Ultimately, technological advances help to quickly identify variations between individuals and conclude the presence or absence of a disease in the affected person. [7] Thereby, an individual with a condition can get the treatment they need using these specific variants.

Similar to the research done on T2D, this model also examines T2D, however in a hypothetical scenario – the T allele contains SNPs that non-T2D patients have, which may show how SNPs are used for examining the cause for certain diseasesimg[9]

Therapeutics that involve the use of SNPs, such as the candidate gene approach, involve the use of a patient's prior history of disease and finding SNPs in the patient's genes that may relate to other diseased patients. Such novel approaches are transforming the medical field for developing therapeutics from the correlation of disease with a patient's genes. [10] This approach is also being used for Pharmacogenomics research to comprehend the reasons for severe adverse reactions in some patients when taking a particular medication, while the same drug has a different response for others. [10]

With increasing technological advances, the demand for therapeutic landscapes is also changing rapidly. As a result, it is equally important to figure out ways to effectively match successful drugs to a specific patient based on various approaches. [11]

Furthermore, 23andMe has developed tests to understand their genetic health history using DNA tests. More specifically, many corporations use Reference SNP cluster ID numbers (rsID), unique labels that allow scientists to detect a SNP sequence in an individual and compare them with "universal" dbSNPs, which involve a database where researchers can identify an individual's SNP sequence to detect genetic variation overlapping with a "template" sequence. [13] These tests essentially provide customers with genetic information about health history — among other pieces of genetic information that are essential for the public to understand family history.

The model shows how the dbSNP database is used for a wide variety of biological applications, such as identifying an individual’s gene(s) and matching them with a “universal template.” [14]*

The Future and Potential of GWAS & SNPs

While there is a handful of research on SNPs and GWAS, significant ethical and humanitarian issues also arise. For example, while there is a plentiful activity in researching humans for GWAS, a large percentage of participants for GWAS research have European ancestors. More specifically, 79% of research is used with this group of individuals.[15] This is a problem for not only research on GWAS, but how new advancements in medicine using GWAS and SNPs understanding revolve around a slim percentage (16%) of individuals from a specific region of the world.[15] As a result, the lack of diversity in conducting these experiments does not account for the likelihood of diseases in other population groups — only participants of European ancestry that are prone to diseases that other populations may not be susceptible to. Thus, new discoveries on how diseases can be studied further from the analysis of genes and SNP that play a role in disease contraction may be inhibited, partially by the absence of other participants of different ethnic backgrounds involved in the study of GWAS. This issue needs to be changed for the future of GWAS research to be further advanced in understanding the molecular role of diseases in humans. [15]

A need for diversification is essential to greater representation of participant research, thus allowing a less restricted amount of information on disease contraction, analysis and other details. [16]

Another major flaw lies in the errors that might result from advancing technology revolving around continued research in GWAS. For example, using "chips" that can detect diseases by analyzing an individual's DNA as mentioned previously, also generates false positives. In a 2021 study, researchers aimed to detect rare pathogenic variants using these SNP chips in a certain population. The final results were inaccurate in detecting the variants, and many false positives were present. [17] These findings reveal that while much research is currently taking place in developing new technology using SNPs, caveats in identifying diseases or pathogens can still occur, detrimental to accurately diagnosing patients.

Cardiovascular disease (CVD) research has benefited from the understanding of genetic variation leading to metabolism differences. Recent research on SNPs reveals a possible genetic component of obesity. It provides significant insight on how chronic diseases that are prevalent in the US can be benefited from changes in dietary lifestyles. [18] For instance, in 2018, researchers discovered 89 SNPs linked with obesity, neuroimmune disease and cardiometabolic disease — representing the "first genetic screening" in normally healthy patients with a good cardiovascular system, however still exhibiting obesity. Additionally, the researchers identified two SNPs: NTRK2 and rs9736016. The first SNP that was discovered revealed that there is a genetic component to developing extreme obesity and depression, while the other showed a factor for the development of sclerosis. [18]

Greater research on the genetic factors for a disease using GWAS studies may help scientists in the future to develop more effective medications. [19]

Overall, through the power of GWAS and SNPs analysis, the future for this field of research has high potential yet is still obscure. For example, not only will researchers be able to find underlying diseases for an individual, the medical community can now use these pieces of information to help develop targeted therapy for those who have or are susceptible to a disease like cancer, and intervene early on through precision medicine, thus providing medications specific to an individual. [20] Increasing comprehension of the vast amount of information gathered from genomic variations over the past 1½ decade is continuing to excel at higher rates, and the growth in technological advancement may help propel further medical solutions [12] using SNPs and GWAS research. However, there are also issues with GWAS advancement, as more research still needs to be conducted in order to provide treatment to individuals currently suffering from diseases like cancer [6] and introduce more ethnic representation [15] in GWAS experiments. Hence, while the scientific community has made great strides in understanding a person's genetic predispositions from SNP analysis, the development of drugs which could cause severe adverse drug reactions (ADRs) is not fully predictable yet, as to how these reactions can be reduced or eliminated. [11] Certain biological and environmental components and other elements also influence the degree of variability in reactions to a certain drug. [12] Thus, as the applications of GWAS and SNPs research become more advanced, greater research in the field of developing medications shifting from a universal approach to treatment through trial and error to the prioritization of specific patients' needs, is the change that needs to be initiated further. [21]

By: Mihir Vishwarupe, Monta Vista High School (External student, guest post on the request of Mentors)


  1. MedlinePlus (National Library of Medicine). “What are single nucleotide polymorphisms (SNPs)?” MedlinePlus, National Library of Medicine, 22 Mar. 2022, https://medlineplus.gov/genetics/understanding/genomicresearch/snp/. Accessed 29 Dec. 2022.
  2. “Making SNPs Make Sense.” Learn Genetics-Utah, University of Utah, https://learn.genetics.utah.edu/content/precision/snips. Accessed 29 Dec. 2022.
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  6. Fagny, Maud, et al. “Nongenic Cancer-Risk Snps Affect Oncogenes, Tumour-Suppressor Genes, and Immune Function.” Nature, British Journal of Cancer, 6 Dec. 2019, https://www.nature.com/articles/s41416-019-0614-3. Accessed 29 Dec. 2022.
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  8. Sun, Weiping, et al. “Integrative Analysis of Super Enhancer Snps for Type 2 Diabetes.” National Library of Medicine, PLoS One, 31 Jan. 2018, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792005/. Accessed 29 Dec. 2022
  9. Genomics Education Programme. “How SNPs Are Used in Genome-Wide Association Studies (GWAS).” SNPs in Genome Wide Association, Flickr, Inc., 10 Mar. 2014, https://www.flickr.com/photos/119980645@N06/13062250605. Accessed 29 Dec. 2022
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  11. Brazell, C., et al. “Maximizing the value of medicines by including pharmacogenetic research in drug development and surveillance.” National Library of Medicine, British Journal of Clinical Pharmacology, Mar. 2002, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1874316/. Accessed 29 Dec. 2022.
  12. Shastry, Barkur S. “SNPs in Disease Gene Mapping, Medicinal Drug Development and Evolution.” Nature, Journal of Human Genetics, 11 Oct. 2007, https://www.nature.com/articles/jhg2007117. Accessed 29 Dec. 2022.
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  14. MacInnis, Martin, and Greg Baute. “DbSNP Diagram No Caption.” Wikipedia, Wikimedia Foundation, Inc., 5 Mar. 2010, https://en.wikipedia.org/wiki/File:DbSNP_diagram_no_caption.jpg. Accessed 29 Dec. 2002
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  16. Altmann, Gerd. “Colorful Diversity.” PublicDomainPictures, Bobek, Ltd., https://www.publicdomainpictures.net/en/view-image.php?image=364726&picture=colorful-diversity. Accessed 29 Dec. 2022.
  17. MN, Weedon, et al. “Use of SNP Chips to Detect Rare Pathogenic Variants: Retrospective, Population Based Diagnostic Evaluation.” The BMJ, British Medical Journal Publishing Group, 16 Feb. 2021, https://www.bmj.com/content/372/bmj.n214. Accessed 29 Dec. 2022
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The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of Elio Academy.