ArticleHealthcare informatics research2022
Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network.
Article in Healthcare informatics research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Role of Single Nucleotide Polymorphisms at the Arg399Gln Locus of theInternational journal of molecular sciences · 2025Article
- Strong Cumulative Evidence of Associations of 6 Single Nucleotide Polymorphisms with Ovarian Cancer Risk: An Umbrella Review.Journal of clinical medicine · 2023Review
- SNPs Sets in Codifying Genes for Xenobiotics-Processing Enzymes Are Associated with COPD Secondary to Biomass-Burning Smoke.Current issues in molecular biology · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesGenome-wide association studies (GWAS) are performed to study the associations between genetic variants with respect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certain traits are solely affected by a single mutation. We propose a network analysis method, in which we generate association networks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize that certain phenotypic traits are attributable to mutations in groups of associated SNPs.
methodsWe propose a method based on a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regression to measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer risk score based on the generated SNP networks.
resultsWe demonstrated our method in a dataset from a case-control study of colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. We obtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus on chromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic risk score was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the case and control groups.
conclusionsOur results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.
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