Geodesic
Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with Bayesian networks
Journal of computational and engineering mathematics, Tome 2 (2015) no. 4, pp. 11-26.

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The genome-wide analysis of genetic associations with lipid metabolism indicators was carried out using the technology of Bayesian networks (BN). It was performed to diagnose polygenic hypercholesterolemia on the basis of genetic data of the Russian population of patients. The data of 1,200 patients was analyzed. 196725 SNPs as well as clinical data, lipid profile indicators — different types of cholesterol — were obtained for each of them. The genome-wide association analysis (GWAS) and the statistical method of Pearson's chi-squared test were used for the initial selection of the most significant parameters. Two of the patient states related to a lipid metabolism were studied. These states are the level of LDL-C (low density lipoprotein) and the level of HDL-C (high density lipoprotein). The Bayesian networks having the simplest topology — naive — were used to predict the level of lipoprotein. The construction of ROC-curves and the calculation of the area under these curves (AUC) were used to assess a quality (reliability) of the prediction. AUC value increased from 0,5 for the initial BN to 0,9 after selecting of significant parameters using the GWAS method or the Pearson one. A further increase in AUC to 0,99 and decrease in the number of prognostic parameters to 150 was performed using Bayesian network optimization with respect to the number of parameters-nodes. Here the optimized function was value of AUC. The ambiguity of obtaining prognostic parameters at various ways of initial reducing the number of network nodes using the methods of GWAS and Pirson is shown. Low values of AUC were obtained for an independent control group of patients, despite very good results on the quality of the predictions, which were obtained on the training set. Further application of the proposed methodology is possible after the substantial reduction of the number of SNPs on the base of the analysis of the respective molecular mechanisms.
Keywords: GWAS; LDL-C; HDL-C; SNP; Bayesian networks. 
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A. V. Sulimov; A. N. Meshkov; I. A. Savkin; E. V. Katkova; D. K. Kutov; Z. B. Hasanova; N. V. Konovalova; V. V. Kukharchuk; V. B. Sulimov. Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with Bayesian networks. Journal of computational and engineering mathematics, Tome 2 (2015) no. 4, pp. 11-26. http://geodesic.mathdoc.fr/item/JCEM_2015_2_4_a1/

[1] V. L. Roger, A. S. Go, D. M. Loyd-Jones, et al., “Heart Disease and Stroke Statistics - 2012 Update: A Report from the American Heart Association”, Circulation, 125:1 (2012), e2–e220 | DOI

[2] W. B. Kannel, T. R. Dawber, A. Kagan, N. Revotskie, J. 3rd Stokes, “Factors of Risk in the Development of Coronary Heart Disease”, Six Year Follow-up Experience. The Framingham Study. Ann Intern Med, 55:1 (1961), 33–50 | DOI

[3] A. Keys, “Coronary Heart Disease in Seven Countries: 1. The Study Program and Objectives”, Circulation, 41:4S (1970), 1-1-1-8

[4] F. Gianfagna, D. Cugino, I. Santimone, L. Iacoviello, “From Candidate Gene to Genome-Wide Association Studies in Cardiovascular Disease”, Thromb Res, 129:3 (2012), 320–324 | DOI

[5] Y. Y. Teo, X. Sim, “Patterns of Linkage Disequilibrium in Different Populations: Implications and Opportunities for Lipid-Associated Loci Identified from Genome-Wide Association Studies”, Curr Opin Lipidol, 21:2 (2010), 104–115 | DOI

[6] W. Zhou, D. C. Christiani, “East Meets West: Ethnic Differences in Epidemiology and Clinical Behaviors of Lung Cancer Between East Asians and Caucasians”, Chinese Journal of Cancer, 30:5 (2011), 287–292 | DOI

[7] S. G. Urazalin, V. N. Titov, T. N. Vlasic, T. V. Balahonova, Yu. A. Karpov, V. In. Kukharchuk, S. A. Fighters, “Relationship concentrations of lipoprotein-associated secretory phospholipase A2 with markers of subclinical atherosclerotic lesions of the arterial wall in patients with low and intermediate risk scale SCORE”, Therapeutic Archives, 83:9 (2011), 29–35

[8] S. G. Urazalin, A. N. Rogoza, T. V. Balahonova, “The value of markers of preclinical lesions wall of the carotid artery to determine the cardiovascular risk scale Recommendations ESH / ESC (2003, 2007)”, Cardiovascular therapy and prevention, 10:7 (2011), 74–80

[9] D. A. Jdanov, A. D. Deev, D. Jasilionis, S. A. Shalnova, M. A. Shkolnikova, V. M. Shkolnikov, “Recalibration of the SCORE Risk Chart for the Russian Population”, European Journal of Epidemiology, 29:9 (2014), 621–628 | DOI

[10] B. F. Voight, H. M. Kang, J. Ding, C. D. Palmer, C. Sidore, et al, “The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits”, PLoS Genet, 8:8 (2012), e1002793 | DOI

[11] S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. de Bakker, M. J. Daly, P. C. Sham, “PLINK: a Toolset for Whole-Genome Association and Population-Based Linkage Analysis”, American Journal of Human Genetics, 81:3 (2007), 559–575 | DOI

[12] W. Jian, S. Sanjay, “A Powerful Hybrid Approach to Select Top Single-Nucleotide Polymorphisms for Genome-Wide Association Study”, BMC Genetics, 12:3 (2011), 3–9

[13] R. J. Klein, C. Zeiss, E. Y. Chew, J.-Y. Tsai,R. S. Sackler, C. Haynes , Henning A.K., SanGiovanni J.P., Mane S.M., Mayne S.T., et al, “Complement Factor H Polymorphism in Age-Related Macular Degeneration”, Science, 308:5720 (2005), 385–389 | DOI

[14] P. J. Lucas, L. C. van der Gaag, A. Abu-Hanna, “Bayesian Networks in Biomedicine and Health-Care”, Artif Intell Med, 30:3 (2004), 201–214 | DOI

[15] O. Gevaert, F. D. Smet, D. Timmerman, Y. Moreau, B. D. Moor, “Predicting the Prognosis of Breast Cancer by Integrating Clinical and Microarray Data with Bayesian Networks”, Bioinformatics, 22:14 (2006), e184–e190 | DOI

[16] A. V. Sulimov, D. N. Vtyurina, A. N. Romanov, E. D. Maslennikov, V. B. Sulimov, I. N. Kurochkin et al., “Expert Systems of Personalized Medicine: the Use of Bayesian Networks to Predict the State of Patients”, Post-Genomic Research and Technology, 2011, 641-702

[17] D. P. Genis, A. V. Sulimov, N. I. Moiseev, Oh. G. Ovsy, L. Z. Velsher, E. Yu. Rybalkina, I. I. Selezneva, I. A. Savkin, “Search of approaches to predict breast cancer outcomes using Bayesian networks”, Oncology. Journal of them. PA Herzen, 2014, no. 9, 37–46

[18] E. D. Maslennikov, A. V. Sulimov, I. A. Savkin, M. A. Evdokimova, D. A. Zateyshchikov, V. V. Nosikov, V. B. Sulimov, “An Intuitive Risk Factors Search Algorithm: Usage of the Bayesian Network Technique in Personalized Medicine”, Journal of Applied Statistics, 42:1 (2014), 71–87 | DOI | MR

[19] F. V. Jensen, T. D. Nielsen, Bayesian Networks and Decision Graphs, Springer Verlag, New York, 2007, 441 pp. | MR | Zbl

[20] T. Fawcett, “ROC Graphs: Notes and Practical Considerations for Researchers”, ReCALL, 31:HPL-2003-4 (2004), 1–38

[21] N. A. Obuchowski, “ROC Analysis”, American Journal of Roentgenology, 184:2 (2005), 364–372 | DOI

[22] GenomeStudio Software, } {\tt http://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html

[23] B. L. Van der Waerden, Mathematische statistik, Springer-Verlag, Berlin–Gottingen–Heidelberg, 1960, 84 pp. | MR

[24] L. Jay Devore, N. Kenneth, Berk Modern Mathematical Statistics with Applications, Springer-Verlag, New York, 2012, 200 pp. | MR

[25] D. P. Gens, V. B. Sulimov, N. I. Moiseev, A. V. Sulimov, O. G. Ovsy, method for predicting outcome of breast cancer, Patent of Russian Federation 06.2014 No 2563437, stated 26.06.2014, publ. 20.09.2015