Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network
Objectives Genome-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 prop...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2022-07-01
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| Series: | Healthcare Informatics Research |
| Subjects: | |
| Online Access: | http://www.e-hir.org/upload/pdf/hir-2022-28-3-247.pdf |
| _version_ | 1828295914109796352 |
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| author | Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Bens Pardamean |
| author_facet | Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Bens Pardamean |
| author_sort | Bharuno Mahesworo |
| collection | DOAJ |
| description | Objectives Genome-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. Methods We 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. Results We 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. Conclusions Our results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer. |
| first_indexed | 2024-04-13T12:02:54Z |
| format | Article |
| id | doaj.art-aa31eef4b9f34c6da4cfe3db8f2f0ef7 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-13T12:02:54Z |
| publishDate | 2022-07-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-aa31eef4b9f34c6da4cfe3db8f2f0ef72022-12-22T02:47:44ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2022-07-0128324725510.4258/hir.2022.28.3.2471127Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism NetworkBharuno Mahesworo0Arif Budiarto1Alam Ahmad Hidayat2Bens Pardamean3 Department of Statistics, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, IndonesiaObjectives Genome-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. Methods We 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. Results We 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. Conclusions Our results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.http://www.e-hir.org/upload/pdf/hir-2022-28-3-247.pdfdata analysisgeneticsrisk factorscolorectal neoplasmsmultifactorial inheritance |
| spellingShingle | Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Bens Pardamean Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network Healthcare Informatics Research data analysis genetics risk factors colorectal neoplasms multifactorial inheritance |
| title | Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network |
| title_full | Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network |
| title_fullStr | Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network |
| title_full_unstemmed | Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network |
| title_short | Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network |
| title_sort | cancer risk score prediction based on a single nucleotide polymorphism network |
| topic | data analysis genetics risk factors colorectal neoplasms multifactorial inheritance |
| url | http://www.e-hir.org/upload/pdf/hir-2022-28-3-247.pdf |
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