SNP-based pathway enrichment analysis for genome-wide association studies

Area of Science:

• Genetics
• Bioinformatics
• Statistical genetics

Background:

• Genome-wide association studies (GWAS) identify genetic variations for complex diseases but explain only a small fraction of genetic risks.
• Pathway analysis offers a strategy to combine evidence from multiple single nucleotide polymorphisms (SNPs) within genes and pathways.
• Current methods often focus on single SNPs per gene, potentially overlooking joint genetic effects and overemphasizing genes with more SNPs.

Purpose of the Study:

• To develop and evaluate a novel SNP-based pathway enrichment method for analyzing genome-wide association studies (GWAS) data.
• To address the limitations of traditional GWAS by integrating information from multiple SNPs within biological pathways.
• To identify statistically significant and replicable pathways associated with complex diseases.

Main Methods:

• A two-step SNP-based pathway enrichment method was developed.
• Step 1: Identifies representative SNPs per gene using an adaptive truncated product statistic and re-selects SNPs based on average gene representation.
• Step 2: Ranks SNPs by association significance and uses a weighted Kolmogorov-Smirnov test to assess pathway enrichment.

Main Results:

• The method was applied to schizophrenia GWAS datasets from European-American (EA) and African-American (AA) populations.
• In EA data, 22 pathways met nominal P-value ≤ 0.001 and FDR < 5% thresholds.
• In AA data, 11 pathways were identified, with 8 overlapping with those found in the EA sample, demonstrating replicability.

Conclusions:

• The proposed SNP-based pathway enrichment method provides a valuable alternative for GWAS data analysis.
• The method successfully identifies statistically significant pathways associated with schizophrenia.
• The findings highlight the method's ability to detect pathways that are replicable across genetically distinct populations.