- Single nucleotide polymorphism (SNP) detection methods encompass a wide range of molecular, hybridization, sequencing, and emerging technologies designed to identify genetic variations at the single-base level.
- The choice of method depends on the scale of study, whether the SNP is already known or novel, the desired throughput, and available resources. These methods are central to modern genetics, with applications spanning medical diagnostics, pharmacogenomics, evolutionary biology, agriculture, and biodiversity studies.
- Technologies for SNP detection have evolved considerably, ranging from traditional molecular methods to high-throughput platforms. Early approaches included restriction fragment length polymorphism (RFLP) analysis, where restriction enzymes cut DNA at specific sequences, revealing SNPs that alter restriction sites. The allele-specific PCR method allows detection of known SNPs by designing primers that selectively amplify specific variants. DNA sequencing, particularly Sanger sequencing, remains a gold standard for precise identification of SNPs, though its throughput is limited for large-scale studies.
- One of the earliest and most widely used strategies for SNP detection is PCR-based analysis.
- Techniques such as allele-specific PCR (AS-PCR) exploit the principle that a primer with a perfect match at the SNP site will preferentially amplify one allele over another. This method is rapid and inexpensive, making it suitable for targeted detection of known SNPs.
- Another PCR-based method, the TaqMan assay, uses fluorescent probes to distinguish between alleles in real time, providing both specificity and quantification.
- Similarly, PCR-restriction fragment length polymorphism (PCR-RFLP) relies on restriction enzymes that recognize sequence motifs altered by SNPs, with fragment size differences visualized through gel electrophoresis.
- These PCR-based methods are simple and cost-effective but are limited in scalability and are typically used for analyzing a small number of variants.
- For large-scale genotyping, hybridization and microarray-based approaches have proven invaluable. DNA microarrays (or SNP chips) contain thousands to millions of allele-specific probes that hybridize to complementary DNA fragments from samples. Fluorescent signals reveal the genotype at each SNP position, enabling genome-wide association studies (GWAS) that link SNPs to traits or diseases. Although powerful, microarrays are restricted to the detection of known SNPs included on the chip and may miss rare or novel variants. Despite this limitation, they remain cost-effective for population-scale projects and have revolutionized the study of complex diseases and population genetics.
- The most comprehensive and versatile category of SNP detection methods involves DNA sequencing. Sanger sequencing, the traditional gold standard, provides highly accurate identification of SNPs within targeted DNA regions, making it ideal for small-scale validation or clinical confirmation. However, its low throughput and higher cost per base limit its utility for large projects. In contrast, next-generation sequencing (NGS) platforms allow genome-wide detection of millions of SNPs simultaneously. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) offer unparalleled coverage and resolution, making them indispensable for discovering novel SNPs, studying rare variants, and mapping genetic diversity. More recently, nanopore sequencing has emerged, enabling long-read sequencing and real-time SNP detection with portable devices, though error rates remain higher than with short-read technologies.
- Beyond traditional approaches, several emerging technologies are pushing the boundaries of SNP detection. CRISPR-based assays utilize engineered Cas proteins to recognize specific nucleotide sequences, offering rapid, highly specific, and potentially point-of-care detection of SNPs. Similarly, mass spectrometry methods such as MALDI-TOF analyze SNP-associated mass differences in nucleic acids, enabling high-throughput and multiplexed genotyping. These methods, while still developing, promise to make SNP detection faster, cheaper, and more accessible in clinical and field settings.
