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Published on: November 3, 2023
INTERSNP: genome-wide interaction analysis guided by a priori information
Christine Herold1, Michael Steffens, Felix F Brockschmidt
1Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de
Bioinformatics (Oxford, England)
|October 20, 2009
Summary
Genome-wide association studies (GWAS) explain some disease heritability, but interactions between genetic variants remain key. We developed INTERSNP, a novel approach for computationally feasible genome-wide interaction analysis (GWIA) to uncover these complex genetic relationships.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) identify genomic regions linked to complex diseases, yet a significant portion of heritability remains unexplained.
- Genetic variant interactions are a leading hypothesis for the 'missing heritability' phenomenon.
- Performing exhaustive genome-wide interaction analysis (GWIA) is computationally prohibitive due to the vast number of possible combinations.
Purpose of the Study:
- To develop a computationally feasible GWIA approach for identifying complex disease-associated genetic interactions.
- To integrate statistical, genetic, and biological information for targeted SNP selection in interaction analyses.
- To introduce the INTERSNP software package for conducting advanced GWIA strategies.
Main Methods:
- Developed a GWIA strategy that prioritizes SNP combinations based on a priori information, including statistical association, genomic location, and biological relevance (SNP function, pathways).
- Implemented logistic regression and log-linear models within the INTERSNP software for joint multi-SNP analysis.
- Integrated automatic SNP annotation and KEGG pathway information, along with Monte Carlo simulations for assessing genome-wide significance.
Main Results:
- Demonstrated the feasibility of various GWIA strategies using INTERSNP on a GWAS dataset.
- Successfully applied targeted analyses, such as examining pairs of non-synonymous SNPs or combinations of three SNPs within common pathways.
- Identified promising results, suggesting the utility of the INTERSNP approach for uncovering complex genetic interactions.
Conclusions:
- The INTERSNP approach offers a computationally tractable solution for genome-wide interaction analysis, addressing the limitations of exhaustive pairwise or triple-SNP analyses.
- By integrating diverse biological and statistical information, INTERSNP enables focused and meaningful GWIA strategies.
- This method facilitates the exploration of complex genetic architectures underlying diseases, potentially explaining previously missing heritability.
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