The Role of Single-nucleotide Polymorphisms in Complex Disease Susceptibility

Emery Winingear^*
*Correspondence: Emery Winingear, Department of Medical Genetics and Genomics, Princeton University, Princeton, NJ 08544, USA, Email:

Introduction

Single-Nucleotide Polymorphisms (SNPs) are variations at a single nucleotide position in the genome that occur in at least 1% of the population. While many SNPs are benign and have no effect on health, others can influence an individual's susceptibility to complex diseases, which are influenced by multiple genetic and environmental factors. Understanding the role of SNPs in disease susceptibility is crucial for advancing personalized medicine and improving disease prevention strategies. SNPs can affect disease susceptibility through various mechanisms. They may alter protein function by changing the amino acid sequence of a protein or affect gene expression by modifying regulatory regions. SNPs in coding regions can lead to non-synonymous changes, which may result in altered protein function or stability. Conversely, SNPs in non-coding regions, such as promoters or enhancers, can influence gene expression levels or the binding affinity of transcription factors.

Moreover, SNPs can affect the structure and function of RNA molecules, including mRNAs and non-coding RNAs. For instance, SNPs in mRNA coding regions can create or disrupt microRNA binding sites, affecting mRNA stability and translation. Additionally, SNPs in non-coding RNA genes can influence the production or function of these regulatory RNAs, impacting gene expression and disease susceptibility.

Description

SNPs and complex disease risk

Single-Nucleotide Polymorphisms (SNPs) have become a central focus in understanding complex diseases due to their ability to influence disease susceptibility through genetic variation. These variations, which involve changes in a single nucleotide at a specific position in the genome, can affect a range of biological processes and contribute to the development of various complex diseases.

The role of SNPs in complex disease risk is multifaceted. SNPs can influence disease susceptibility by affecting gene function, gene expression, and interactions between different genetic and environmental factors. For example, SNPs located in coding regions of genes can lead to amino acid substitutions that alter the structure and function of proteins. These changes can impact enzymatic activity, receptor binding, or protein-protein interactions, thereby influencing disease risk. In some cases, such as with certain types of cancer, SNPs in oncogenes or tumor suppressor genes can affect cellular processes like DNA repair, cell cycle regulation, and apoptosis, contributing to increased cancer susceptibility [1,2].

Beyond coding regions, SNPs in regulatory regions of the genome also play a significant role. Variants in promoter regions or enhancers can affect the binding of transcription factors or the accessibility of chromatin, leading to changes in gene expression. For instance, SNPs in regulatory regions of genes involved in immune responses or inflammation can impact susceptibility to autoimmune diseases or chronic inflammatory conditions. Similarly, SNPs in genes related to metabolism or lipid regulation can influence the risk of cardiovascular diseases and type 2 diabetes.

SNPs can also affect gene-environment interactions. The impact of a SNP on disease risk can be modulated by environmental factors such as diet, lifestyle, or exposure to toxins. For example, certain SNPs associated with cancer risk may only significantly affect disease susceptibility in individuals with specific environmental exposures, such as smoking or high-fat diets.

Genome-Wide Association Studies (GWAS) have been instrumental in identifying SNPs associated with complex diseases by scanning large populations for genetic variants that correlate with disease traits. These studies have uncovered numerous SNPs linked to various conditions, including cardiovascular diseases, diabetes, and neurological disorders. However, the effect sizes of individual SNPs are often small, and their contributions to disease risk are typically combined with other genetic variants and environmental factors [3,4].

Challenges and advances in SNP research

Despite significant progress, several challenges remain in understanding and utilizing SNPs for disease prediction and treatment. One challenge is distinguishing between causative SNPs and those that are merely linked to disease due to their proximity to other genetic variations. Additionally, the influence of SNPs on disease risk is often modulated by interactions with other genetic factors and environmental exposures, complicating the interpretation of their effects.

Advances in genomic technologies, such as high-throughput sequencing and bioinformatics tools, have improved our ability to identify and analyze SNPs associated with complex diseases. Integrating SNP data with other omics data, such as transcriptomics and proteomics, can provide a more comprehensive understanding of disease mechanisms and aid in the development of personalized treatment strategies [5].

Conclusion

Single-nucleotide polymorphisms play a critical role in determining susceptibility to complex diseases. Their influence on gene function and expression provides valuable insights into disease mechanisms and potential therapeutic targets. As genomic research continues to advance, understanding the role of SNPs in complex disease susceptibility will enhance our ability to predict, prevent, and treat a wide range of conditions, paving the way for more personalized and effective healthcare strategies. The identification of SNPs associated with complex diseases has led to advancements in understanding disease mechanisms and has potential implications for personalized medicine. By combining SNP data with other clinical and environmental information, it is possible to develop risk prediction models that can help identify individuals at higher risk for certain diseases. Additionally, understanding the functional implications of disease-associated SNPs can guide the development of targeted therapies and interventions.

Future research should focus on elucidating the functional mechanisms through which SNPs influence disease risk. This includes studying the interactions between SNPs and environmental factors, as well as exploring how SNPs affect gene networks and pathways. Additionally, incorporating SNP information into clinical practice will require developing robust methods for risk prediction and personalized medicine. This involves translating genetic findings into actionable insights for disease prevention, diagnosis, and treatment.

Acknowledgement

None.

Conflict of Interest

Authors declare no conflict of interest.

References

1. Lievens, Antoon, Sara Jacchia, D. Kagkli, Cristian Savini, and Maddalena Querci. "Measuring digital PCR quality: Performance parameters and their optimization." PLoS One11 (2016): e0153317.

   Google Scholar, Crossref, Indexed at

2. Van Heuverswyn, Fran, Maria Karczmarczyk, Heinz Schimmel and Stefanie Trapmann, et al. "Influence of primer & probe chemistry and amplification target on reverse transcription digital PCR quantification of viral RNA." Biomol Detect Quantif9 (2016): 20-28.

   Google Scholar, Crossref, Indexed at

3. Hays, Amanda, Mark Wissel, Kelly Colletti and Russell Soon, et al. "Recommendations for method development and validation of qPCR and dPCR assays in support of cell and gene therapy drug development." AAPS J26 (2024): 24

   Google Scholar, Crossref, Indexed at

4. Miller, Jacob A., ChunHong Huang, Fumiko Yamamoto and Malaya K. Sahoo, et al. "Comparison of real-time PCR and digital PCR for detection of plasma epstein-barr virus dna in nasopharyngeal carcinoma." J Mol Diagn 25 (2023): 490-501.

   Google Scholar, Crossref, Indexed at

5. Schlenker, Franziska, Elena Kipf, Nadine Borst and Tobias Hutzenlaub, et al. "Virtual fluorescence color channels by selective photobleaching in digital PCR applied to the quantification of KRAS point mutations." Anal Chem 93 (2021): 10538-10545

   Google Scholar, Crossref, Indexed at