Predictive accuracy of the breast cancer genetic risk model based on eight common genetic variants: The BACkSIDE study

• Published in: Journal of biotechnology Vol. 299; pp. 1 - 7
• Main Authors: Danková, Zuzana, Žúbor, Pavol, Grendár, Marián, Zelinová, Katarína, Jagelková, Marianna, Stastny, Igor, Kapinová, Andrea, Vargová, Daniela, Kasajová, Petra, Dvorská, Dana, Kalman, Michal, Danko, Ján, Lasabová, Zora
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.06.2019
• Subjects: algorithms; alleles; AUC; Breast cancer; breast neoplasms; fibroblast growth factor receptor 2; genetic factors; genetic variation; genotyping; heterozygosity; homozygosity; inheritance (genetics); melting; mutants; odds ratio; patients; Random Forest algorithm; risk; Risk model; single nucleotide polymorphism; SNP; women; Risk model; Random Forest algorithm; Breast cancer; SNP; AUC; breast cancer; risk model

•Predictive performance of genetic risk model combining age had AUC value 0.728.•Age is more important than common genetic variants, showed Random Forest algorithm.•LSP1 gene polymorphism rs3817198 is specific for ER + breast cancer types. Breast cancer (BC) development is caused by the interaction...