Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Behravan, Hamid ^a   Hartikainen, Jaana M ^a   Tengström, Maria ^a,b   Pylkäs, Katri ^c   Winqvist, Robert ^c   Kosma, Veli–Matti ^a,b   Mannermaa, Arto ^a,b  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b KUOPIO UNIVERSITY HOSPITAL   (Finland)

^c OULU UNIVERSITY HOSPITAL   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ESTROGEN RECEPTOR ALPHA; ESTROGEN RECEPTOR ALPHA, HUMAN; PARKIN; UBIQUITIN PROTEIN LIGASE;

BREAST TUMOR; CASE CONTROL STUDY; FEMALE; FINLAND; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; HUMAN GENOME; METABOLISM; NUCLEOTIDE SEQUENCE; PATHOLOGY; PROGNOSIS; PROTEIN ANALYSIS; RISK; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE;

BASE SEQUENCE; BREAST NEOPLASMS; CASE-CONTROL STUDIES; ESTROGEN RECEPTOR ALPHA; FEMALE; FINLAND; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; HUMANS; POLYMORPHISM, SINGLE NUCLEOTIDE; PROGNOSIS; PROTEIN INTERACTION MAPPING; RISK; SUPPORT VECTOR MACHINE; UBIQUITIN-PROTEIN LIGASES;

EID: 85052827942     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-018-31573-5     Document Type: Article

References (49)

1. Mavaddat, N. et al. Prediction of breast cancer risk based on profiling with common genetic variants. Journal of the National Cancer Institute 107, djv036 (2015)
2. Giacomini, K. M. et al. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nature Reviews Drug Discovery 16, 70 (2017)
3. Kar, S. P. et al. Genome-wide meta-analyses of breast, ovarian, and prostate cancer association studies identify multiple new susceptibility loci shared by at least two cancer types. Cancer Discovery 6, 1052–1067 (2016)
4. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature Genetics 45, 1452–1458 (2013)
5. Hendricks, A. E., Dupuis, J., Logue, M. W., Myers, R. H. & Lunetta, K. L. Correction for multiple testing in a gene region. European Journal of Human Genetics 22, 414–418 (2014)
6. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289–300 (1995)
7. Manolio, T. A. Genomewide association studies and assessment of the risk of disease. New England Journal of Medicine 363, 166–176 (2010)
8. Rakitsch, B., Lippert, C., Stegle, O. & Borgwardt, K. A lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29, 206–214 (2013)
9. Kuchenbaecker, K. B. et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. Journal of the National Cancer Institute 109, djw302 (2017)
10. Lee, S., Kong, S. & Xing, E. P. A network-driven approach for genome-wide association mapping. Bioinformatics 32, i164–i173 (2016)
11. Gorfine, M. et al. Heritability estimation using a regularized regression approach (herra): Applicable to continuous, dichotomous or age-at-onset outcome. Plos One 12, 1–19 (2017)
12. Wu, J., Devlin, B., Ringquist, S., Trucco, M. & Roeder, K. Screen and clean: a tool for identifying interactions in genome-wide association studies. Genetic Epidemiology 34, 275–285 (2010)
13. Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological) 58, 267–288 (1996)
14. Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970)
15. Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E. & Lange, K. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25, 714–721 (2009)
16. Malo, N., Libiger, O. & Schork, N. J. Accommodating Linkage Disequilibrium in genetic-association analyses via ridge regression. The American Journal of Human Genetics 82, 375–385 (2008)
17. Moore, J. H. & Ritchie, M. D. The challenges of whole-genome approaches to common diseases. JAMA 291, 1642–1643 (2004)
18. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. of KDD, 785–794 (2016)
19. Zheng, H., Yuan, J. & Chen, L. Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10, 1168 (2017)
20. Ren, X., Guo, H., Li, S., Wang, S. & Li, J. A novel image classification method with CNN-XGBoost model. In Proc. of IWDW, 378–390 (2017)
21. Zou, J. & Wang, E. eTumorType, An algorithm of discriminating cancer types for circulating tumor cells or cell-free DNAs in blood. Genomics, Proteomics & Bioinformatics 15, 130–140 (2017)
22. Hartikainen, J. M. et al. Refinement of the 22q12-q13 breast cancer-associated region: Evidence of TMPRSS6 as a candidate gene in an Eastern Finnish population. Clinical Cancer Research 12, 1454–1462 (2006)
23. Mantere, T. et al. Case-control analysis of truncating mutations in DNA damage response genes connects TEX15 and FANCD2 with hereditary breast cancer susceptibility. Scientific Reports 7, 681 (2017)
24. Siddhartha, P. K. et al. Genome-wide meta-analyses of breast, ovarian, and prostate cancer association studies identify multiple new susceptibility loci shared by at least two cancer types. Cancer Discovery 6, 1052–1067 (2016)
25. Michailidou, K. et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nature Genetics 47, 373–380 (2015)
26. Cortes, C. & Vapnik, V. Support-vector networks. Machine Learning 20, 273–297 (1995)
27. Mittag, F., Römer, M. & Zell, A. Influence of feature encoding and choice of classifier on disease risk prediction in genome-wide association studies. Plos One 10, 1–18 (2015)
28. Davis, J. & Goadrich, M. The relationship between precision-recall and ROC curves. In Proc. of ICML, 233–240 (2006)
29. Salton, G. & McGill, M. J. Introduction to modern information retrieval (McGraw-Hill, Inc., 1986)
30. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017)
31. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Research 46, D754–D761 (2018)
32. Bean, D. M. et al. esyN: Network building, sharing and publishing. Plos One 9, 1–5 (2014)
33. Cytoscape.js website. http://cytoscape.github.io/cytoscape.js/, (Accessed July 15, 2014)
34. Contrino, S. et al. modMine: flexible access to modENCODE data. Nucleic Acids Research 40, D1082–D1088 (2012)
35. jquery website. http://jquery.com/, (Accessed July 15, 2014)
36. Angularjs website. https://angularjs.org/, (Accessed July 15, 2014)
37. Underscore.js website. http://underscorejs.org/, (Accessed July 15, 2014)
38. Friedman, J. H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29, 1189–1232 (2001)
39. Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24, 417–441 (1933)
40. Kerminen, S. et al. Fine-scale genetic structure in Finland. G3: Genes, Genomes, Genetics 7, 3459–3468 (2017)
41. Gruvberger, S. et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Research 61, 5979–5984 (2001)
42. Gong, Y. et al. Pan-cancer analysis links PARK2 to BCL-XL-dependent control of apoptosis. Neoplasia 19, 75–83 (2017)
43. Gao, S. et al. Identification and construction of combinatory cancer hallmark-based gene signature sets to predict recurrence and chemotherapy benefit in stage II colorectal cancer. JAMA Oncology 2, 37–45 (2016)
44. Li, J. et al. Identification of high-quality cancer prognostic markers and metastasis network modules. Nature Communications 1 (2010)
45. Wang, E. et al. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. Seminars in Cancer Biology 30, 4–12 (2015)
46. Lane, A., Segura-Cabrera, A. & Komurov, K. A comparative survey of functional footprints of EGFR pathway mutations in human cancers. Oncogene 33, 5078–5089 (2014)
47. Liang, Y. et al. The EGFR/miR-338-3p/EYA2 axis controls breast tumor growth and lung metastasis. Cell Death & Disease 8, e2928 (2017)
48. Si, W. et al. A miR-20a/MAPK1/c-Myc regulatory feedback loop regulates breast carcinogenesis and chemoresistance. Cell Death And Differentiation 25, 406–420 (2017)
49. Bhaskara, S. et al. HDAC3 is essential for the maintenance of chromatin structure and genome stability. Cancer cell 18, 436–447 (2010)