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http://dx.doi.org/10.9708/jksci/2012.17.11.141

A Classifier for the association study between SNPs and quantitative traits  

Uhmn, Saangyong (Dept. of Computer Science, Hallym University)
Lee, Kwang Mo (Dept. of Computer Science, Hallym University)
Abstract
The advance of technologies for human genome makes it possible that the analysis of association between genetic variants and diseases and the application of the results to predict risk or susceptibility to them. Many of those studies carried out in case-control study. For quantitative traits, statistical analysis methods are applied to find single nucleotide polymorphisms (SNP) relevant to the diseases and consider them one by one. In this study, we presented methods to select informative single nucleotide polymorphisms and predict risk for quantitative traits and compared their performance. We adopted two SNP selection methods: one considering single SNP only and the other of all possible pairs of SNPs.
Keywords
SNP; quantitative trait; decision tree;
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1 Human Genome Project. http://www.ornl.gov/sci/techresources/HumanGenome/home.shtml
2 Y. S. Cho et al., "A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits," Nature Genetics, vol. 41, no. 5, pp. 527-534, May 2009.   DOI   ScienceOn
3 S. Ripatti et al., "A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses," Lancet, vol.376, no. 9750, pp. 1393- 1400, Oct. 2010.   DOI   ScienceOn
4 A. C. Heath et al., "A quantitative-trait genome-wide association study of alcoholismrisk in the community: Findings and implications," Biological Psychiatry, vol. 70, no. 6, pp. 513-518, Sep. 2011.   DOI   ScienceOn
5 H. D. Daetwyler, B. Villanueva, and J. A. Woolliams, "Accuracy of predicting the genetic risk of disease using a genome-wide approach," PLoS One, vol. 3, no. 10, p. e3395, Oct. 2008   DOI   ScienceOn
6 S. Waaijenborg and A. H. Zwinderman, "Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data," Algorithms formolecular biology :AMB, vol. 5, p. 17, 2010.
7 N. P. Paynter, D. I. Chasman, J. E. Buring,D. Shiffman, N. R. Cook, and P. M. Ridker, "Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3," Annals of internal medicine, vol. 150, no. 2, pp. 65-72, Jan. 2009.   DOI   ScienceOn
8 J. Batsis and F. Lopez-Jimenez, "Cardiovascular risk assessment - From individual risk prediction to estimation of global risk and change in risk in the population," BMC medicine, vol. 8, no. 1, p. 29, 2010.   DOI   ScienceOn
9 Z.Wei, K.Wang,H. Qu,H. Zhang, J. Bradfield, C. Kim, E. Frackleton, C. Hou, J. Glessner, and R. Chiavacci, "From disease association to risk assessment: an optimistic viewfromgenome-wide association studies on Type 1 Diabetes," PLoS genetics, vol. 5, no. 10, p.e1000678, 2009.   DOI   ScienceOn
10 C. Kooperberg, M. LeBlanc, and V. Obenchain, "Risk prediction using genome-wide association studies," Genetic Epidemiology, vol. 34,no. 7, pp. 643-652, Sep. 2010.   DOI   ScienceOn
11 P. Kraft and D. J. Hunter, "Genetic risk prediction-are we there yet?" New England Journal of Medicine, vol. 360, no. 17, pp. 1701-1703, Apr. 2009.   DOI   ScienceOn
12 T. A.Manolio et al., "Finding themissing heritability of complex diseases," Nature, vol. 461, no. 7265, pp. 747- 753, Oct. 2009.   DOI   ScienceOn
13 H. Siu, Y. Zhu, L. Jin, andM. Xiong, "Implication of next-generation sequencing on association studies," BMC genomics, vol. 12, no. 1, p. 322, Jun. 2011.   DOI
14 K. Kahrizi et al., "Next generation sequencing in a family with autosomal recessive Kahrizi syndrome (OMIM612713) reveals a homozygous frameshift mutation in SRD5A3," European journal of human genetics : EJHG, vol. 19, no. 1, pp. 115-117, 2011.   DOI   ScienceOn
15 P. M. Ridker, J. E. Buring, N. Rifai, and N. R. Cook, "Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women The Reynolds Risk Score," JAMA, vol. 297, no. 6, pp. 611-619, Feb. 2007.   DOI   ScienceOn
16 S. Uhmn, D.-H. Kim, Y.-W. Ko, S. Cho, J. Cheong, and J. Kim, "A study on application of single nucleotide polymorphism and machine learning techniques to diagnosis of chronic hepatitis," Expert Systems, vol. 26, no. 1, pp. 60-69, Feb. 2009.   DOI   ScienceOn
17 Online Mendelian Inheritance in Man. http://www.ncbi.nlm.nih.gov/omim.
18 S. J. Pocock, V.McCormack, F.Gueyffier, F. Boutitie, R. H. Fagard, and J.-P. Boissel, "A score for predicting risk of death fromcardiovascular disease in adults with raised blood pressure, based on individual patient data fromrandomized controlled trials," BMJ, vol. 323, no. 7304, pp. 75-81, Jul. 2001.   DOI   ScienceOn
19 A Catalog of Genome-Wide Association Studies. http://www.genome.gov/26525384
20 The Jackson Laboratory, http://www.jax.org.
21 C. F. Deschepper, J. L. Olson, M. Otis, and N. Gallo-Payet, "Characterization of blood pressure and morphological traits in cardiovascular-related organs in 13 different inbredmouse strains," Journal of applied physiology (Bethesda,Md. : 1985), vol. 97, no. 1, pp. 369-376, Jul. 2004.   DOI   ScienceOn
22 P. Pudil and J. Novovicova, "Floating searchmethods in feature selection," Pattern recognition letters, 1994.
23 M.H. Cho et al., "Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation," Respiratory Research, 11:30, March 2010.   DOI   ScienceOn
24 Y Guan and M Stephens, "Bayesian variable selection regression for genome-wide association studies and other large-scale problems," Ann. Appl. Stat. Volume 5, Number 3, pp.1780-1815, 2011.   DOI