Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network
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초록
Objectives: Genome-wide association studies (GWAS) are performed to study the associations between genetic variants withrespect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certaintraits are solely affected by a single mutation. We propose a network analysis method, in which we generate associationnetworks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize thatcertain phenotypic traits are attributable to mutations in groups of associated SNPs. Methods: We propose a method basedon a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regressionto measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer riskscore based on the generated SNP networks. Results: We demonstrated our method in a dataset from a case-control studyof colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. Weobtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus onchromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic riskscore was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the caseand control groups. Conclusions: Our results demonstrate the applicability of our method to understand SNP-SNP interactionsand compute risk scores for various types of cancer.
Abstract
Objectives: Genome-wide association studies (GWAS) are performed to study the associations between genetic variants withrespect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certaintraits are solely affected by a single mutation. We propose a network analysis method, in which we generate associationnetworks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize thatcertain phenotypic traits are attributable to mutations in groups of associated SNPs. Methods: We propose a method basedon a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regressionto measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer riskscore based on the generated SNP networks. Results: We demonstrated our method in a dataset from a case-control studyof colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. Weobtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus onchromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic riskscore was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the caseand control groups. Conclusions: Our results demonstrate the applicability of our method to understand SNP-SNP interactionsand compute risk scores for various types of cancer.
참고문헌 (30)
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