Introduction:
The Single Nucleotide Polymorphism Database (dbSNP), maintained by the National Center for Biotechnology Information (NCBI), has become an essential genomic resource cataloging over 1 billion variants, largely through collaboration with projects such as 1000 Genomes, gnomAD, TopMed, and ALFA.  With over 90% of dbSNP’s variants classified as rare, the database provides unparalleled insight into allele frequencies across diverse populations, supporting the identification of genetic variants that may be associated with disease susceptibility and cancer risk. 

 

Methods:
This presentation reviews key data attributes within dbSNP’s Reference SNP (RefSNP) entries, focusing on genomic placement, allele frequency, clinical relevance, and variant functional impact using tools like the Variation Viewer and RefSNP interface. Allele frequency data sourced from the Allele Frequency Aggregator (ALFA) offers a broad spectrum of population-specific frequencies that can highlight potential disease-associated alleles. Clinical annotations from ClinVar and literature references augment these datasets, allowing for comprehensive screening of candidate variants based on disease relevance. 

Results:
 dbSNP’s integration of data from large-scale genomic projects enables robust variant screening, revealing numerous rare alleles with potential clinical significance. Approximately 1 billion dbSNP variants are annotated with population-level allele frequency data, of which more than 90% are rare (minor allele frequency < 0.01). This unique focus on rare variants, coupled with functional and clinical annotations, allows for the identification of both known pathogenic variants and novel mutations, particularly valuable in cancer and complex disease research. 

Conclusion:
As a centralized, comprehensive resource, dbSNP facilitates genome-wide screening of rare and novel variants, offering researchers valuable tools to explore genetic contributions to disease and individual health. The database’s extensive allele frequency data, supported by clinical and functional annotations, enables a systematic approach to variant discovery and interpretation, promoting advances in precision medicine and population genetics.