Mapping SNPs to Pathways and Diseases

Mapping SNPs to pathways and diseases is a complex but crucial task in genomics research. Here are some of the key methods and tools mentioned in the discussion, along with additional insights:

1. Direct Mapping via Databases

• Biomart/Martview: This tool can map SNP IDs to gene/protein identifiers and further to GO Biological Process terms. It’s a good starting point for linking SNPs to genes and then to pathways.
• KEGG, Reactome, WikiPathways: Once you have gene IDs, these databases can help map genes to specific pathways. Custom visualizations can highlight genes with SNPs.
• DAS (Distributed Annotation System): Useful for retrieving genes/phenotypes associated with specific SNPs, though it primarily focuses on gene regions rather than pathways.

2. Gene Set Enrichment Analysis (GSEA)

• GSEA (Mootha et al.): A method to identify whether a priori defined sets of genes show statistically significant differences between two biological states.
• MAGENTA (Segre et al.): A tool for pathway enrichment analysis from GWAS data.
• VEGAS (Liu et al.): Another method for pathway analysis using GWAS data.
• ALIGATOR (Holmes et al.): Tests Gene Ontology categories for overrepresentation based on SNP p-values.
• GRASS (Lin et al.): A ridge regression method for pathway enrichment analysis from SNP data.

3. Specialized Tools

• GRAIL: Designed for integrating GWAS data to identify relationships between genes and pathways.
• Gene Set Analysis Toolkit V2 (WebGestalt): Allows uploading a list of SNPs and returns pathway information from KEGG or WikiPathways, highlighting genes/proteins.

4. Considerations for Non-Coding SNPs

• Non-Coding Regions: SNPs in non-coding regions can still influence gene expression and pathways. Tools like GRAIL and methods considering regulatory elements are valuable.
• Locus Studies: Examples like the 9p21 locus show that SNPs outside coding regions can still be crucial in disease pathways.

5. Exome Sequencing

• Exome Sequencing: Useful for identifying causative variants in Mendelian disorders. However, GWAS targets common variants, which may not always link directly to disease pathways.

6. Practical Steps

• Step 1: Map SNPs to genes using tools like Biomart.
• Step 2: Use pathway databases (KEGG, Reactome, WikiPathways) to map genes to pathways.
• Step 3: Perform enrichment analysis using GSEA tools to identify significant pathways.
• Step 4: Consider regulatory effects and non-coding SNPs using specialized tools like GRAIL.

7. Challenges

• Complexity: SNPs may influence pathways indirectly, and their effects can be context-dependent (e.g., tissue-specific expression).
• Data Integration: Combining data from multiple sources (GWAS, expression data, pathway databases) is essential for accurate mapping.

8. Additional Resources

• Literature: Reviews on non-coding regions and GWAS studies provide deeper insights.
• Software: Tools like WebGestalt and GRAIL offer user-friendly interfaces for pathway analysis.

Summary

Mapping SNPs to pathways involves multiple steps and tools, from initial gene mapping to pathway enrichment analysis. Given the complexity, especially with non-coding SNPs, a combination of methods and careful consideration of biological context is essential for accurate results. Tools like Biomart, KEGG, GSEA, and GRAIL are invaluable in this process.