SNP-based Pathway Enrichment Analysis for GWAS
Author Information
Author(s): Weng Lingjie, Macciardi Fabio, Subramanian Aravind, Guffanti Guia, Potkin Steven G, Yu Zhaoxia, Xie Xiaohui
Primary Institution: University of California, Irvine
Hypothesis
Can a SNP-based pathway enrichment method improve the identification of significant pathways in genome-wide association studies (GWAS)?
Conclusion
The SNP-based pathway enrichment method can identify statistically significant pathways in schizophrenia GWAS studies, with some pathways replicating across genetically distinct samples.
Supporting Evidence
- The method identified 22 significant pathways in the European-American sample and 11 in the African-American sample.
- Eight pathways were found to be significant in both samples, indicating robustness.
- The method was implemented in a user-friendly software package called SNP Set Enrichment Analysis (SSEA).
Takeaway
This study created a new way to look at genes and their effects on diseases by grouping them into pathways, which helps find important patterns in genetic data.
Methodology
The method involves selecting representative SNPs for each gene and performing pathway enrichment analysis using a weighted Kolmogorov-Smirnov test.
Potential Biases
The method may favor larger genes or pathways due to the number of SNPs, potentially introducing bias.
Limitations
The method assumes independence of P-values, which may not hold due to linkage disequilibrium among SNPs.
Participant Demographics
The study included genetically distinct populations: European-American and African-American.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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