Single nucleotide polymorphism (SNP) analysis is the study of the most common type of genetic variation found in the DNA of living organisms. This analytical process identifies differences in the genetic code, specifically where a single DNA building block, or nucleotide, has been exchanged for another. These minor variations occur frequently across the genome, acting as biological signposts that contribute to the unique differences between individuals. By examining the patterns of these variations, scientists gain insights into how an organism is wired, which in turn informs everything from physical characteristics to inherited tendencies. The study of these genetic differences has become a primary tool in modern biology for understanding the genetic basis of biological traits.
Understanding Single Nucleotide Polymorphisms
A single nucleotide polymorphism (SNP) is a variation at a single position in a DNA sequence. This change involves the substitution of one of the four nucleotide bases—adenine (A), thymine (T), cytosine (C), or guanine (G)—for another at a specific location in the genome. For a single-base change to be classified as a polymorphism, the less common variant must be present in at least one percent of the population.
This frequency threshold distinguishes a polymorphism from a genetic mutation, which is a rare change found in less than one percent of the population. An individual’s genome contains millions of these inherited SNPs, distributed widely across the DNA. Most SNPs fall into non-coding regions, meaning they do not directly alter a protein. However, they can still influence gene function by affecting how or when a gene is turned on or off. SNPs serve as stable genetic markers that researchers use to track the inheritance of specific traits or conditions.
Technologies Used to Analyze SNPs
The analysis of SNPs is performed using two primary technological approaches: genotyping arrays and DNA sequencing. SNP microarrays are the most common commercial method for analyzing hundreds of thousands of known SNPs simultaneously. This method involves fragmenting a DNA sample and allowing the pieces to bind to microscopic probes fixed onto a solid surface, such as a glass chip.
Each probe is designed to match a specific SNP location and its possible base-pair variations. The binding is detected using fluorescent dyes, with the signal intensity indicating which specific allele is present in the sample. Genotyping arrays are efficient and cost-effective for surveying a predefined set of variants, but they are limited to only the SNPs that have been specifically included on the chip’s design.
Next-Generation Sequencing (NGS) offers a more comprehensive approach by reading the sequence of every nucleotide in a DNA sample. NGS technologies sequence millions of short DNA fragments in parallel for SNP detection. Bioinformatics software then aligns these short reads to a reference human genome, and any single-base difference observed is identified as a potential SNP. While NGS is more expensive and computationally intensive than microarrays, it can discover entirely new SNPs and identify rare variants not included on commercial chips.
Using SNP Analysis for Disease Risk and Treatment
SNP analysis is used to identify genetic factors contributing to complex diseases and predict patient response to medication. Genome-Wide Association Studies (GWAS) utilize high-density SNP genotyping to compare the profiles of people with a specific disease (cases) to healthy individuals (controls). This comparison identifies specific SNPs that occur more frequently in the case group.
The association between an SNP and a disease is quantified using the odds ratio, which indicates the likelihood of a person with that SNP developing the condition. For complex conditions like type 2 diabetes or heart disease, hundreds of different SNPs may each contribute a small amount of risk. Clinicians construct Polygenic Risk Scores (PRS) by aggregating the effects of thousands of associated SNPs. This provides an individual with an estimate of their inherited susceptibility to a condition.
SNP analysis forms the foundation of pharmacogenomics, the study of how an individual’s genetic makeup affects drug response. Variations in genes that encode drug-metabolizing enzymes, such as the Cytochrome P450 (CYP450) family, can dramatically alter how quickly a patient processes a medication. For example, an SNP in the CYP2D6 gene can classify a person as a “poor metabolizer,” meaning the drug is broken down slowly and can accumulate to toxic levels. Conversely, an “ultra-rapid metabolizer” may break down the drug so quickly that it provides no therapeutic benefit. Analyzing these drug-response SNPs allows physicians to adjust dosages for medications like antidepressants or codeine, supporting personalized medicine.
Non-Clinical Applications of SNP Data
SNP analysis is used in non-clinical fields, including genealogy and forensic science. Direct-to-consumer ancestry testing relies on SNP genotyping to estimate an individual’s ethnic and geographic origins by comparing their SNP profile to databases of reference populations worldwide. Individuals share common SNP patterns with others who have common ancestors, allowing these tests to map genetic heritage and identify distant relatives.
In forensic science, SNP profiles are used for both identification and investigative leads. Small panels of informative SNPs can uniquely identify an individual, especially when the DNA sample is degraded. This technology is also leveraged in Forensic Investigative Genetic Genealogy (FIGG) to trace a suspect’s family tree through genetic databases. In agriculture, “Genomic Selection” uses dense SNP maps to estimate the breeding value of livestock or crops, accelerating the selection of organisms with desirable traits like increased yield or disease resistance.