Study Notes: SNPs - Key Concepts and Classifications

SNP stands for Single Nucleotide Polymorphism. In the slides, SNPs are also described as pronounced either as "snips" or, per another slide, "snip".
Definition: A SNP is a single-nucleotide substitution of one base for another that occurs in more than one percent of the general population.
Not every single-nucleotide change is a SNP.
Distribution in the genome: SNPs occur throughout the human genome, about one in every 300 nucleotide base pairs.
This translates to roughly $10^7$ SNPs within the $3\times 10^9$ -base-pair human genome.
At a SNP location, there can be up to four versions corresponding to the four nucleotides: A, C, G, and T.

SNPs can serve as predictive markers that inform medical decisions across various areas:
- Diseases
- Effectiveness of different drugs
- Adverse reactions to specific drugs
Pharmacogenetic approach: uses SNP information to personalize medical care, potentially saving time, money, and discomfort by enabling accurate diagnoses and matching patients with appropriate medicines.

Genomic approaches (the big picture):
- Large-scale projects involve hundreds of scientists from many institutions.
- Goal: identify and catalog all SNPs in the 3-billion-base-pair human genome.
- Methods rely on comparing the genomes of many individuals and require substantial computer-powered data analysis.
- Results are sorted and cataloged in databases that are available to anyone over the Internet, including the public.
Functional approaches (focus on specific processes):
- Target particular diseases or drug responses.
- Many genes control biological processes involved in diseases and drug responses.
- Scientists select genes known to be involved in a process and examine them in people with and without the response/disease.
- By comparing DNA sequences, researchers identify SNPs that correlate with a function or response.
SNP Quick Reference (slide label):
- Appears as a reference section; content specifics not detailed in the transcript.

True, SNPs and disease-causing mutations are both single-nucleotide changes, but they are not the same.
Key distinctions:
- To be classified as a SNP, the change must be present in at least one percent of the general population: $\ge 0.01$ .
- Most disease-causing mutations occur within a gene's coding or regulatory regions and affect the protein encoded by the gene.
- SNPs are not necessarily located within genes, and they do not always affect protein function.

Linked SNPs (also called indicative SNPs):
- Do not reside within genes.
- Do not affect protein function.
- Nevertheless, they correlate with a particular drug response or with the risk of developing a certain disease.
Causative SNPs:
- Affect the way a protein functions.
- Correlate with a disease or influence a person’s response to medication.
- Come in two forms:
- Coding SNPs: located within the coding region of a gene; change the amino acid sequence of the gene’s protein product.
- Non-coding SNPs: located within regulatory sequences of a gene; change the timing, location, or level of gene expression.

Coding SNPs:
- Located in the coding region of a gene.
- Change the amino acid sequence of the protein (could affect protein function).
Non-coding SNPs:
- Located in regulatory regions (non-coding regions) of a gene.
- Alter when, where, or how much a gene is expressed, rather than changing the amino acid sequence.

Genome-wide perspective:
- The human genome contains about $3\times 10^9$ base pairs.
- SNPs occur roughly every $rac{1}{300}$ base pairs, i.e., about one SNP per 300 base pairs, on average.
- This results in an estimated $10^7$ SNPs across the genome.
Practical implications:
- SNPs provide a framework for understanding genetic variation among individuals.
- They enable researchers to link genetic variation to disease risk, drug response, and adverse drug reactions.
- The predictive power of SNPs supports the broader goal of personalized medicine.

Genetic variation underpins diversity in disease risk and drug response across populations.
Use of SNPs aligns with the principle of tailoring medical care to individual genetic profiles, enhancing effectiveness and reducing harm.
Large-scale genomic projects showcase the importance of data-sharing and openly accessible databases for scientific progress.
Functional approaches illustrate how focusing on specific diseases or drug responses helps identify clinically relevant SNPs that might be missed in a purely genome-wide search.

Ethical considerations include privacy and consent for genomic data sharing and potential misuse of genetic information.
Practical implications involve the integration of SNP-based insights into clinical workflows, including decision-making, cost, and accessibility for patients.

SNPs are single-nucleotide substitutions common in the population (>= 1%), distributed throughout the genome, with an average occurrence of roughly one per 300 base pairs, equating to about 10 million SNPs in the human genome.
Not all SNPs affect protein function; some are simply indicative markers that correlate with disease risk or drug response, while others causally affect protein function via coding or non-coding changes.
Two main approaches identify SNPs: Genomic (broad cataloging across the genome) and Functional (focused on specific diseases or drug responses).
Distinctions between SNPs and disease-causing mutations underscore that commonality (>= 1%) excludes typical disease-causing mutations, which are often rarer and more likely to impact coding or regulatory regions.
Understanding SNPs supports personalized medicine by informing disease risk assessment, drug choice, and potential adverse reactions, while also raising important ethical and practical considerations for clinical implementation.