Step-by-Step Manual to Find Disease-Associated SNPs

This guide provides the latest tools, databases, and tips to identify disease-associated SNPs, including associated PMIDs, case/control numbers, and population studies.

Step 1: Define Your Research Goal

• Objective: Identify SNPs associated with a specific disease (e.g., diabetes, Alzheimer’s).
• Required Information:
  • Disease name or trait.
  • Gene(s) of interest (optional).
  • Chromosomal region (optional).
  • Population or study details (optional).

Step 2: Use the NHGRI-EBI GWAS Catalog

• Purpose: Find validated SNP-disease associations from published GWAS studies.
• Website: NHGRI-EBI GWAS Catalog
• Steps:
  1. Go to the GWAS Catalog website.
  2. Use the search bar to enter your disease/trait (e.g., “Type 2 Diabetes”).
  3. Filter results by:
     • P-value threshold (e.g., < 1 x 10⁻⁵).
     • Population (e.g., European, Asian).
     • Study size (cases/controls).
  4. Review the results:
     • SNP ID (e.g., rs7903146).
     • Associated gene(s).
     • PMID (PubMed ID) for the study.
     • Odds ratio, confidence intervals, and p-values.
  5. Export results as a CSV/TSV file for further analysis.

Step 3: Cross-Reference with dbSNP

• Purpose: Retrieve detailed SNP information, including functional annotations and population frequencies.
• Website: dbSNP
• Steps:
  1. Enter the SNP ID (e.g., rs7903146) in the search bar.
  2. Review the SNP summary:
     • Genomic location (chromosome, position).
     • Functional consequences (e.g., missense, intronic).
     • Allele frequencies in different populations.
  3. Check the “Clinical Significance” section for disease associations.
  4. Use the “PubMed” link to find related studies.
  5. Export data using the “Send to” option (e.g., file, clipboard).

Step 4: Explore SNPedia for Curated SNP-Disease Associations

• Purpose: Access manually curated SNP-disease associations and additional annotations.
• Website: SNPedia
• Steps:
  1. Search for your disease (e.g., “Type 2 Diabetes”) or SNP ID (e.g., rs7903146).
  2. Review the SNP page for:
     • Disease associations.
     • Genotype risks (e.g., TT, CT, CC).
     • Links to external databases (e.g., ClinVar, OMIM).
  3. Use Promethease for personalized SNP analysis:
     • Upload your raw genetic data (e.g., 23andMe, AncestryDNA).
     • Generate a report with disease-associated SNPs.
     • Export the report as a CSV/TSV file.

Step 5: Use OMIM for Gene-Disease Relationships

• Purpose: Identify genes associated with your disease and their SNPs.
• Website: OMIM
• Steps:
  1. Search for your disease (e.g., “Alzheimer’s Disease”).
  2. Review the gene entries (e.g., APP, PSEN1, PSEN2).
  3. Check the “Allelic Variants” section for SNPs and their clinical significance.
  4. Use the “PubMed” links to find related studies.
  5. Export gene and SNP data for further analysis.

Step 6: Analyze Genomic Context with UCSC Genome Browser

• Purpose: Visualize SNP locations, nearby genes, and functional annotations.
• Website: UCSC Genome Browser
• Steps:
  1. Enter your SNP ID or genomic coordinates (e.g., chr10:114758349-114758349).
  2. Add tracks for:
     • dbSNP variants.
     • GWAS Catalog associations.
     • Gene annotations (e.g., RefSeq, Ensembl).
  3. Use the “Table Browser” to extract SNP data:
     • Select the “snpXXX” table (e.g., snp153 for the latest version).
     • Filter by chromosome, position, or gene.
     • Export results as a BED or text file.

Step 7: Use ClinVar for Clinical Significance

• Purpose: Find SNPs with clinical significance and disease associations.
• Website: ClinVar
• Steps:
  1. Search for your disease or SNP ID.
  2. Review the clinical significance (e.g., pathogenic, benign).
  3. Check the associated conditions and supporting evidence (e.g., PMIDs).
  4. Export data for further analysis.

Step 8: Perform Advanced Queries with BioMart

• Purpose: Extract SNP-disease associations from Ensembl.
• Website: Ensembl BioMart
• Steps:
  1. Select the “Ensembl Genes” dataset.
  2. Choose your species (e.g., human).
  3. Apply filters:
     • Chromosome/region.
     • Gene name(s).
     • SNP consequences (e.g., missense, synonymous).
  4. Add attributes:
     • SNP IDs.
     • Associated phenotypes.
     • External references (e.g., dbSNP, ClinVar).
  5. Export results as a CSV/TSV file.

Step 9: Validate Findings with Literature

• Purpose: Confirm SNP-disease associations using published studies.
• Tools:
  • PubMed: Search for PMIDs from GWAS Catalog or ClinVar.
  • Google Scholar: Look for additional studies.
  • Zotero/Mendeley: Organize and annotate references.

Step 10: Automate with APIs and Scripts

• Purpose: Streamline data retrieval and analysis.
• Tools:
  • NCBI E-Utilities: Programmatically access dbSNP, ClinVar, and PubMed.
  • UCSC Table Browser API: Automate genomic data extraction.
  • Python/R Scripts: Use libraries like Biopython or biomaRt for data integration.

Tips for Success

1. Combine Multiple Databases: Cross-reference results from GWAS Catalog, dbSNP, and ClinVar for robust findings.
2. Check Population Specificity: Ensure SNP associations are relevant to your target population.
3. Use Latest Data: Always use the most recent versions of databases (e.g., dbSNP build 155, GWAS Catalog updates).
4. Leverage Visualization Tools: Use tools like UCSC Genome Browser or IGV for genomic context.
5. Stay Updated: Follow updates from NHGRI, EBI, and SNPedia for new associations and tools.

By following this manual, you can systematically identify and validate disease-associated SNPs, ensuring accurate and comprehensive results for your research.