Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls
Open accessAbstract
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER−) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis.
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XGBoost has proven successful in several diagnostic applications.
“This algorithm has proven successful in several diagnostic applications (Behravan et al., 2018; Torlay et al., 2017; Zheng et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Behravan et al., 2018
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- Authors
- Behravan, H., Hartikainen, J. M., Tengström, M., Pylkäs, K., Winqvist, R., Kosma, V., & Mannermaa, A.
- Affiliations
- University of Eastern Finland (FI)
- Oulu University Hospital (FI)
- Corresponding author
- Behravan, Hamid
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-09-03
- Volume
- 8
- Issue
- 1
- Pages
- 13149-13149
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6120908
- OpenAlex
- W2889381121
- Cited by
- 75
- References
- 48
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 15 · 2022: 16 · 2021: 10 · 2020: 11 · 2019: 4 · 2018: 1
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UEF eRepo (University of Eastern Finland) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Itä-Suomen Yliopisto
- McGill University
- Génome Québec
- Kuopion Yliopistollinen Sairaala
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- breast cancer risk, single nucleotide polymorphisms, gene-gene interaction, epistasis, gradient boosting decision tree, support vector machine, polygenic risk score, estrogen receptor status, breast cancer subtypes, estrogen metabolism, apoptosis, Finnish population
- MeSH terms
- Base Sequence, Breast Neoplasms, Female, Finland, Humans, Prognosis, Risk, Genome, Human, Gene Expression Regulation, Neoplastic, Case-Control Studies, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Protein Interaction Mapping, Ubiquitin-Protein Ligases, Estrogen Receptor alpha, Gene Regulatory Networks, Genome-Wide Association Study, Support Vector Machine
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