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Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network.
Bharuno Mahesworo1,2, Arif Budiarto2,3, Alam Ahmad Hidayat2
1Department of Statistics, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
Healthcare Informatics Research
|August 19, 2022
Summary
This study introduces a network analysis method to identify groups of single-nucleotide polymorphisms (SNPs) associated with cancer risk. The approach successfully identified SNP interactions linked to colorectal cancer, enabling the computation of a polygenic risk score.
Area of Science:
- Genetics
- Bioinformatics
- Cancer Research
Background:
- Genome-wide association studies (GWAS) typically assume single mutations influence traits.
- This assumption limits understanding of complex genetic contributions to diseases like cancer.
Purpose of the Study:
- To develop and validate a network analysis method for identifying SNP-SNP interactions.
- To assess the utility of this method in differentiating cancer cases from controls.
- To compute a polygenic risk score based on identified SNP networks.
Main Methods:
- A network analysis framework was applied to GWAS data for colorectal cancer.
- Logistic regression was used to assess the significance of SNP pairs.
- A cancer risk score was calculated from generated SNP networks.
Main Results:
- The method identified a cluster of five single-nucleotide polymorphisms (SNPs) associated with colorectal cancer risk.
- A significant connection was found between SNPs on chromosomes 12 and 1.
- A polygenic risk score derived from these SNPs showed significant differences between cancer cases and controls.
Conclusions:
- The proposed network analysis method is effective for understanding SNP-SNP interactions.
- This approach can be used to compute risk scores for various cancers.
- The findings highlight the importance of considering combined SNP effects in genetic studies.
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