Research Article  Open Access
Jinseog Kim, Insuk Sohn, Dennis (Dong Hwan) Kim, SinHo Jung, "SNP Selection in GenomeWide Association Studies via Penalized Support Vector Machine with MAX Test", Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 340678, 8 pages, 2013. https://doi.org/10.1155/2013/340678
SNP Selection in GenomeWide Association Studies via Penalized Support Vector Machine with MAX Test
Abstract
One of main objectives of a genomewide association study (GWAS) is to develop a prediction model for a binary clinical outcome using singlenucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a twostage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity.
1. Introduction
We consider a genomewide association study (GWAS) on a complex disease. One of the popular study objectives of such study is to predict a binary clinical outcome, such as benign versus malignant and response versus no response with respect to a specific regimen, based on singlenucleotide polymorphisms (SNPs) data. A fitted prediction model will be used to predict the diagnostic or prognostic outcomes of future patients. Recently, penalization approaches incorporating logistic model or support vector machines have been actively proposed to fit prediction models with binary outcomes. These are well known to achieve both predictive accuracy and variable selection simultaneously.
By introducing shrinkage priors of the normal exponentialgamma (NEG) distribution family, Hoggart et al. [1] suggested a stochastic search method for penalized logistic regression models with SNPs. Ayers and Cordell [2] showed that the NEG priors have better performance than other competing penalized methods using simulations, while it is very computing intensive to produce the results. Wu et al. [3] considered lassopenalized logistic regression [4] with a large number of SNPs and proposed a cyclic coordinate descent algorithm [5] to implement the computation. Kooperberg et al. [6] removed SNPs that had a HardyWeinberg value smaller than and applied logistic regression models with lasso and Elastic net [7] penalties using a set of SNPs preselected by a crossvalidation procedure. On the other hand, Wei et al. [8] proposed selecting SNPs using EigenStrat algorithm [9] and applying the SVM and logistic regression as predictive models. Abraham et al. [10] showed that the two penalized methods, and Elasticnet SVM, were robust in case/control predictive performance based on simulation studies and real data analyses. These simultaneous analysis methods ignored the genetic models of SNPs [6] or assumed the additive model for all SNPs [6, 8, 10].
The statistical tests such as the Pearson’s chisquared test or the CochranArmitage trend test (CATT) are frequently used to test if an SNP is associated with a binary outcome by assuming a specific genetic model. Oftentimes, however, the true genetic model is unknown. We can improve the testing power if we know the true genetic model of an SNP [11]. Toward this end, the test based on the maximum over the three CATT statistics (MAX test) has been presented by several authors [12, 13]. Kim et al. [14] recently proposed a prediction method for timetoevent traits using SNPs and showed that a prediction model based on the best fitting genetic models of SNPs can improve the prediction efficiency. We extend their approach to the prediction of binary outcomes using SVMs.
In this paper, we propose a prediction method combining the MAX test and penalized SVM to predict binary outcome using SNPs. The proposed method consists of two phase procedures: (i) to select candidate prognostic SNPs and identify their genetic models using MAX test, and (ii) to fit a prediction model using the penalized SVM with appropriate scores for the selected SNPs based on their genetic types. We compare the performance of the proposed method using a different penalized SVM method through simulations and a real GWAS data analysis. Each SVM method is combined with MAX test or the general practice ignoring the genetic types of the SNPs.
To facilitate and enable MAX test, we provide the R package called SNPselect in http://datamining.dongguk.ac.kr/Rlib/SNPselect which uses the penalized SVM R package [15] to implement SVM with SCAD, , and Elastic Net penalties.
2. Methods
2.1. Penalized Support Vector Machine
Suppose that there are subjects. For the subject (), we have an input vector and a class label . The SVM [16, 17] is to find the optimal hyperplane which separates data points into two classes with the largest margin.
Wahba et al. [18] and Hastie et al. [19] found that the optimization problem of the SVM can be represented as a penalized optimization problem: where is called the hinge loss and is a penality function with regularization parameter . The SVM using an norm, , as a penalty function is called the standard SVM or SVM.
The SVM has been successfully applied to classification with highdimensional data such as gene microarrays and SNPs, but it does not select the variables affecting the response class label. For feature selection with SVM, Guyon et al. [20] proposed the SVMREF procedure which combines the recursive feature elimination (RFE) with the SVM. This procedure consists of a twostep procedure using an external gene selection method.
In order to achieve classification accuracy and feature selection simultaneously, variants of SVM have been proposed by replacing the penalty function in (1) with other types of penalty functions, for example, SVM with 1norm [21, 22], adaptive lasso [23], or smoothly clipped and absolute deviation (SCAD) [24, 25] penalties. The SVM with 1norm (or SVM) adapts the lasso (or ) penalty, , originally proposed by Tibshirani [4] as a practical alternative to penalty. Due to the penalty, the SVM automatically selects variables by shrinking the small coefficients of the hyperplane to exactly zero.
One of major drawbacks of the penalty is that it tends to select only one variable when there are many correlated input variables in data. To overcome this limitation of LASSO, Zou and Hastie [7] proposed the Elastic Net penalty by combineing and penalties: The Elastic Net penalty provides variable selection owing to penalty, while finding highly correlated variables, called grouping effect. Wang et al. [26] applied the Elastic Net penalty to SVM classification problems.
Fan and Li [24] proposed the smoothly clipped absolute deviation (SCAD) penalty given as where Here, (>2) and (>0) are tuning parameters. Fan and Li [24] showed that the prediction with SCAD penalty is not sensitive to the tuning parameter and recommended to use .
The SCAD yields the same behavior as for small coefficients , , but assigns a constant penalty for large coefficients. This property reduces the estimation bias. Fan and Li [24] demonstrate more desirable theoretical properties of the SCAD penalty compared to the penalty. Later, Zhang et al. [25] proposed the SVM with the SCAD penalty for feature selection.
2.2. Genetic Models for SNPs
Let AA, AB, and BB be three possible genotypes where B is a risk allele for a given SNP. We denote the number of B alleles in a genotype by ; that is, , or 2 if the genotype is AA, AB, or BB, respectively. For a given SNP, the data from patients are summarized in Table 1.

Let denote the response probability given a genotype . If B is the response allele, the response probability increases as the number of B alleles in the SNP increases; that is, . In this paper, we will consider three popular genetic models satisfying this assumption: (i) recessive model: ;(ii) dominant model: ;(iii) additive model: .
2.3. Trend Test and MAX Test
For testing association between an SNP and a clinical outcome in casecontrol studies, the statistical tests such as the Pearson’s chisquared test or CATT are frequently used when the true genetic model is known. In this case, the CATT is usually more powerful than Pearson’s chisquared test when [12]. For a single SNP, borrowing the notations of Table 1, the CATT statistic can be written as where (, , and ) is a set of scores assigned to genotypes (, , and ) with respect to a specific genotype. The trend test is invariant under a linear transformation with , so that the typical choice of these scores is and , but can take a different value according to a specific genetic model. From the results of Sasieni [27] and Zheng et al. [12, 28], the optimal choices of are and for the recessive, additive, and dominant models, respectively. Let denote the response probability for genotype group . Under the null hypothesis of no association, , approximately follows for large .
When the true genetic model is unknown, the test based on multiple CATTs for different genetic models can lead to substantial reduction in statistical power [11] or inflated type I error rate. To address this issue, the test based on the maximum over the three CATT statistics (MAX test) has been proposed by several authors [12, 13]. Let , , and denote the CATT statistics using the scores for recessive, additive, and dominant models, respectively. Based on the three CATT statistics, the MAX test statistic is defined as The MAX test has robust properties [29] and is more powerful than the Pearson’s chisquared test [12] when the underlying genetic model is unknown.
Even though one can easily calculate the MAX test statistic from (5) and (6), it is not simple to compute its value. One approach of obtaining the value is based on a MonteCarlo simulation. Under , Zheng et al. [12] showed that is asymptotically normal with covariances where denotes the relative frequency of genotype . Thus we can approximate the value of MAX test based on MonteCarlo samples from multivariate normal distribution with estimated variancecovariance matrix which is obtained by replacing in the above covariances with for .
There have been some studies on variants of MAX test for binary clinical outcomes. Zheng et al. [12] developed a robust ranking method, called MAXrank test. Conneely et al. [30] proposed an efficient value computation method that is shown to be more accurate than that using permutations by adjusting for correlated test statistics. Li et al. [31] proposed the Prank test approximating the value for the MAX test with or without covariate adjustment. Li et al. [32] compared the performance of the MAXrank and Prank tests. For more detailed discussions on MAX test, see [11] or [32].
2.4. Classification via SVM with MAX Test
For patient , let denote the binary clinical outcome taking 1 if responded or if not responded and the encoded data on SNPs, that is, , the number of the risk allele for SNP (). To build a classification model with this data set, we propose a method combining a penalized SVM and the MAX test. Our method consists of twophase procedures: (i) prescreening SNPs and identifying the genetic models for the selected SNPs using the MAX test and (ii) applying the penalized SVM to fit a classification model. Our method can be summarized as follows.(1) Read in the clinical outcomes and SNP data .(2) For SNP (),(a) using the original data, calculate test statistics and their twosided values and MAX test statistic .(b) compute the approximate value of MAX test by MonteCarlo simulation:(i) estimate the variancecovarince matrix ;(ii) generate from for (=100,000, say);(iii) approximate the value for MAX test by where .(3) SNP screening: select () SNPs with for a prespecified value, such as .(4) For SNP , identify the genetic model by the smallest value among , , and .(5) Assign covariate values using the score corresponding to the identified genetic model.(6) Standardize the covariates; that is, where and .(7) Apply the penalized SVM to the response data and the standardized covariates .
3. Results
3.1. Simulation Studies
At first, we generate IID random variables and, for , set Note that have an AR(1) correlation structure with autocorrelation coefficient as in [14]. Correlated SNP data are generated by where denotes the th quantile of the standard normal distribution. The binary clinical outcome of patient is generated using response probability which is related to the covariates by
To consider the cases of uncorrelated or moderately correlated SNPs in our experiment, we set or . We generate encoded SNPs with for and for . SNPs 1 and 2 have recessive models; SNPs 3 and 4 have dominant models, and SNPs 5 and 6 have additive models, the regression coefficients for these six prognostic SNPs are set at . According to the above data generation scheme, we have generated simulation data sets of size 200, and each data set is partitioned into 2/3 training set and 1/3 test set. For a classification model fitting, the SVM with one of the three penalty functions, SCAD (SCADSVM), (SVM), and Elastic Net (EnetSVM), is applied to the SNPs selected using . To choose a final classification model, we use 5fold crossvalidation for selecting the tuning parameters. One of the standard practice in the classification model fitting using SNP data will be assuming an equal genetic model for all SNPs. In order to evaluate the performance of the model fitting methods combined with the MAX test, we also have fitted a classification model by assuming one genetic model for all SNPs.
For each model fitting method, we calculate three performance measures such as the number of the selected SNPs, the number of the selected prognostic SNPs by the penalized SVM, and the misclassification error. Here, the selected SNPs are selected by penalized SVM among SNPs after a prescreening step, and the selected prognostic SNPs are the prognostic ones included in the selected SNPs. The misclassification errors are estimated using test data set; that is, where is an indicator function, denote the predicted response score predicted for the test set, and s are standardized covariates in the test set using the means and standard errors calculated from the training set. In order to assess the variability of the experiments, we replicate the whole process 100 times. Table 2 summarizes the three averaged performance measures from our simulations.

When comparing the number of selected SNPs in Table 2, we observe that EnetSVM tends to select more SNPs but SCADSVM selects lower SNPs except for the case of and dominant model. In view of different genetic models, the proposed method selects more SNPs when applying SVM or EnetSVM. However, the combination of proposed method and SCADSVM selects much less SNPs than other combinations. Comparing the numbers of prognostic SNPs, EnetSVM or SVM performs better than SCADSVM and assuming the proposed method or additive model has good selectivities of the true prognostic SNPs. In results with correlated SNPs (), EnetSVM and SVM with the proposed method result in better selectivities for true prognostic SNPs than those with the additive model. However, the proposed methods can be the worst when SCADSVM is used for uncorrelated SNP data. We also compare the misclassification errors. Even if there are a little differences between EnetSVM and SVM, EnetSVM performs better than other penalized methods. SCADSVM produces the worst misclassification errors for all cases. We also find that the proposed method has the lowest misclassification errors whatever the penalized SVM method used except the case of applying SCADSVM for . Based on the discussions on the simulation results so far, the proposed method combined with EnetSVM or SVM could improve the selectivity for true prognostic SNPs and the ablility of precdiction than other methods using a prefixed genetic model.
3.2. Real Data Analysis Example
Kim et al. [33] performed a GWAS using Affymetic Genomewide Human SNP Arrays 6.0 (San Diego, CA, USA) on 190 patients with chronic myelogenous leukemia (CML). After excluding the SNPs with one missing case and those with the same genotype for all 190 patients, we use 330,353 autosomal SNPs in the further data analysis. The clinical endpoint is the achievement of major molecular response by 18 months to an induction chemotherapy. BCR/ABL transcript levels were measured to determine molecular response to imatinib therapy as described before by Kim et al. [34] and presented using the international scale. Major molecular response (MMR) was defined as <0.1% of the BCR/ABL fusion gene transcript level on an international scale by quantitative PCR. Among the 190 patients, 115 responded.
We randomly partition the CML data into 126 training samples and 64 test samples and then calculate the predictive performance measures for the methods over 100 random partitions. Table 3 summarizes the number of selected SNPs and the mean misclassification errors with their standard errors in parentheses over 100 random partitions. Similar to the simulation results, SVM and EnetSVM using the MAX test slightly increase the number of selections, but produce lower misclassification errorr. Among the three penalized methods, EnetSVM selects the largest number of SNPs but has the lowest misclassification error regardless of the use of the MAX test. However, SCADSVM selects the lowest SNPs, while it has poor prediction performances for any assumption for genetic models, which is the same observation in the simulation results.

Table 4 shows the list of 51 SNPs selected commonly by three penalized methods from 126 training samples of one of 100 random partitions. TGFBR1 gene (rs420549, located in 3′UTR region) among 51 SNPs, transforming growth factor beta receptor 1, interacts with TGF beta 1 [35, 36] and TGF beta receptor 2 [37, 38] and is located in 9q22. TGF beta is playing an important role of maintaining the growth and differentiation balance of hematopoietic cells [39, 40] and is known to have bidirectional properties of tumor suppressing and promoting function [41]. TGFFOXO signaling pathway is involved in the maintenance of leukemiainitiating cells in CML, contributing to intrinsic resistance of CML LSCs to tyrosine kinase inhibitor [42, 43]. Accordingly, intrinsic trait of receptor affinity on TGF might contribute to different sensitivities to TGF; thus, it is potentially explainable that the response to imatinib therapy is dependent on the TGFBR1 genotype.

4. Conclusions
Although the penalized methods have been considered as successful ones for prediction in GWAS, they are still subject to high misclassification error by ignoring the genetic models of prognostic SNPs. In this paper, we proposed a twophase procedure: (i) carrying out the MAX test for screening out noncandidate SNPs and identifying the genetic models of the selected SNPs at the first stage and then (ii) applying a penalized SVM to the selected SNPs for fitting a classification model at the second stage. We have compared the performances of the proposed method with the conventional methods ignoring the genetic type of prognostic SNPs through simulations and real data example. In the simulations, we observed that EnetSVM and SVM select more SNPs but have higher selectivities for true prognostic SNPs and lower misclassification errors among the three penalized SVM methods. Combining the proposed method which selects candidate SNPs and estimates their genetic models, we observed that the penalized SVMs except for SCADSVM could improve the performances in terms of the selection of the true prognostic SNPs and misclassification errors. Furthermore, the differences of misclassification errors among the three methods with the proposed method become much smaller. Hence, whichever a penalized SVM for model fitting we use, combining it with the MAX test to identify the genetic models of candidate prognostic SNPs could help to improve its performances. We made similar observations from a real data example. Even so, the selection of candidate SNPs could vary according to the choice of a prespecified ; thus, the prescreening by the MAX test could not select a part of true prognostic SNPs. We will consider this point in future work.
Authors’ Contribution
Jinseog Kim and Insuk Sohn contributed equally to this work.
Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (no. 20100023302).
References
 C. J. Hoggart, J. C. Whittaker, M. De Iorio, and D. J. Balding, “Simultaneous analysis of all SNPs in genomewide and resequencing association studies,” PLoS Genetics, vol. 4, no. 7, Article ID e1000130, 2008. View at: Publisher Site  Google Scholar
 K. L. Ayers and H. J. Cordell, “SNP Selection in genomewide and candidate gene studies via penalized logistic regression,” Genetic Epidemiology, vol. 34, no. 8, pp. 879–891, 2010. View at: Publisher Site  Google Scholar
 T. T. Wu, Y. F. Chen, T. Hastie, E. Sobel, and K. Lange, “Genomewide association analysis by lasso penalized logistic regression,” Bioinformatics, vol. 25, no. 6, pp. 714–721, 2009. View at: Publisher Site  Google Scholar
 R. Tibshirani, “Regression shrinkage and selection via the lasso: a retrospective,” Journal of the Royal Statistical Society B, vol. 73, no. 3, pp. 273–282, 2011. View at: Publisher Site  Google Scholar
 J. Friedman, T. Hastie, H. Hofling, and R. Tibshirani, “Pathwise coordinate optimization,” Annals of Applied Statistics, vol. 1, no. 2, pp. 302–332, 2007. View at: Google Scholar
 C. Kooperberg, M. LeBlanc, and V. Obenchain, “Risk prediction using genomewide association studies,” Genetic Epidemiology, vol. 34, no. 7, pp. 643–652, 2010. View at: Publisher Site  Google Scholar
 H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society B, vol. 67, no. 2, pp. 301–320, 2005. View at: Publisher Site  Google Scholar
 Z. Wei, K. Wang, H.Q. Qu et al., “From disease association to risk assessment: an optimistic view from genomewide association studies on type 1 diabetes,” PLoS Genetics, vol. 5, no. 10, Article ID e1000678, 2009. View at: Publisher Site  Google Scholar
 A. L. Price, N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick, and D. Reich, “Principal components analysis corrects for stratification in genomewide association studies,” Nature Genetics, vol. 38, no. 8, pp. 904–909, 2006. View at: Publisher Site  Google Scholar
 G. Abraham, A. Kowalczyk, J. Zobel, and M. Inouye, “Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease,” Genetic Epidemiology, vol. 37, no. 2, pp. 184–195, 2013. View at: Publisher Site  Google Scholar
 B. Freidlin, G. Zheng, Z. Li, and J. L. Gastwirth, “Trend tests for casecontrol studies of genetic markers: power, sample size and robustness,” Human Heredity, vol. 53, no. 3, pp. 146–152, 2002. View at: Google Scholar
 G. Zheng, B. Freidlin, and J. L. Gastwirth, “Comparison of robust tests for genetic association using casecontrol studies,” in IMS Lecture NotesMonograph Series 2nd Lehmann Symposium—Optimality, vol. 49, pp. 253–265, 2006. View at: Google Scholar
 R. Sladek, G. Rocheleau, J. Rung et al., “A genomewide association study identifies novel risk loci for type 2 diabetes,” Nature, vol. 445, no. 7130, pp. 881–885, 2007. View at: Publisher Site  Google Scholar
 J. Kim, I. Sohn, D. Son, D. H. Kim, T. Ahn, and S. Jung, “Prediction of a timetoevent trait using genome wide SNP data,” BMC Bioinformatics, vol. 14, p. 58, 2013. View at: Publisher Site  Google Scholar
 N. Becker, W. Werft, G. Toedt, P. Lichter, and A. Benner, “PenalizedSVM: a Rpackage for feature selection SVM classification,” Bioinformatics, vol. 25, no. 13, pp. 1711–1712, 2009. View at: Publisher Site  Google Scholar
 V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1996.
 B. Scholkopf and A. Smola, Learning With Kernels—Support Vector Machines, Regularization, Optimization and Beyond, MIT Press, 2002.
 G. Wahba, Y. Lin, and H. Zhang, “Gacv for support vector machines,” in Advances in Large Margin Classiers, A. J. Smola, P. L. Bartlett, B. Scholkopf, and D. Schuurmans, Eds., pp. 297–211, 2000. View at: Google Scholar
 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference and Prediction, Springer, New York, NY, USA, 2001.
 I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at: Publisher Site  Google Scholar
 P. Bradley and O. Mangasarian, “Feature selection via concave minimization and support vector machines,” in Morgan Kaufmann (ICML '98), 1998. View at: Google Scholar
 J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, “1norm support vector machines,” in Neural Information Processing Systems 16. Massachusettes, MIT Press, 2003. View at: Google Scholar
 H. Zou, “An improved 1norm SVM for simultaneous classification and variable selection,” in Proceedings of the Eleventh International Conference on Articial Intelligence and Statistics, vol. 2, pp. 675–681, 2007. View at: Google Scholar
 J. Fan and R. Li, “Variable selection via nonconcave penalized likelihood and its oracle properties,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1348–1360, 2001. View at: Google Scholar
 H. H. Zhang, J. Ahn, X. Lin, and C. Park, “Gene selection using support vector machines with nonconvex penalty,” Bioinformatics, vol. 22, no. 1, pp. 88–95, 2006. View at: Publisher Site  Google Scholar
 L. Wang, J. Zhu, and H. Zou, “The doubly regularized support vector machine,” Statistica Sinica, vol. 16, no. 2, pp. 589–615, 2006. View at: Google Scholar
 P. D. Sasieni, “From genotypes to genes: doubling the sample size,” Biometrics, vol. 53, no. 4, pp. 1253–1261, 1997. View at: Publisher Site  Google Scholar
 G. Zheng, B. Freidlin, Z. Li, and J. L. Gastwirth, “Choice of scores in trend tests for casecontrol studies of candidategene associations,” Biometrical Journal, vol. 45, no. 3, pp. 335–348, 2003. View at: Publisher Site  Google Scholar
 J. L. Gastwirth, “The use of maximin efficiency robust tests in combining contingency tables and survival analysis,” Journal of the American Statistical Association, vol. 80, no. 390, pp. 380–384, 1985. View at: Publisher Site  Google Scholar
 K. N. Conneely and M. Boehnke, “So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests,” American Journal of Human Genetics, vol. 81, no. 6, pp. 1158–1168, 2007. View at: Publisher Site  Google Scholar
 Q. Li, K. Yu, Z. Li, and G. Zheng, “MAXrank: a simple and robust genomewide scan for casecontrol association studies,” Human Genetics, vol. 123, no. 6, pp. 617–623, 2008. View at: Publisher Site  Google Scholar
 Q. Li, G. Zheng, Z. Li, and K. Yu, “Efficient approximation of Pvalue of the maximum of correlated tests, with applications to genomewide association studies,” Annals of Human Genetics, vol. 72, no. 3, pp. 397–406, 2008. View at: Publisher Site  Google Scholar
 D. Kim et al., “Genomewide genotypebased prognostic stratification of treatment outcomes following Imatinib therapy in chronic myeloid leukemia in chronic phase,” In submission, 2013. View at: Google Scholar
 D. H. Kim, J. H. Kong, J. Y. Byeun et al., “The IFNG (IFNγ) genotype predicts cytogenetic and molecular response to imatinib therapy in chronic myeloid leukemia,” Clinical Cancer Research, vol. 16, no. 21, pp. 5339–5350, 2010. View at: Publisher Site  Google Scholar
 R. Ebner, R.H. Chen, S. Lawler, T. Zioncheck, and R. Derynck, “Determination of type I receptor specificity by the type II receptors for TGFβ or activin,” Science, vol. 262, no. 5135, pp. 900–902, 1993. View at: Google Scholar
 S. P. Oh, T. Seki, K. A. Goss et al., “Activin receptorlike kinase 1 modulates transforming growth factorβ1 signaling in the regulation of angiogenesis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 6, pp. 2626–2631, 2000. View at: Publisher Site  Google Scholar
 B. Razani, X. L. Zhang, M. Bitzer et al., “Caveolin1 regulates transforming growth factor (TGF)β/SMAD signaling through an interaction with the TGFβ type I receptor,” The Journal of Biological Chemistry, vol. 276, no. 9, pp. 6727–6738, 2001. View at: Publisher Site  Google Scholar
 M. Kawabata, A. Chytil, and H. L. Moses, “Cloning of a novel type II serine/threonine kinase receptor through interaction with the type I transforming growth factorβ receptor,” The Journal of Biological Chemistry, vol. 270, no. 10, pp. 5625–5630, 1995. View at: Publisher Site  Google Scholar
 S.J. Kim and J. Lettirio, “Transforming growth factorβ signaling in normal and malignant hematopoiesis,” Leukemia, vol. 17, no. 9, pp. 1731–1737, 2003. View at: Publisher Site  Google Scholar
 N. O. Fortunel, J. A. Hatzfeld, M.N. Monier, and A. Hatzfeld, “Control of hematopoietic stem/progenitor cell fate by transforming growth factorβ,” Oncology Research, vol. 13, no. 6–10, pp. 445–453, 2002. View at: Google Scholar
 B. Bierie and H. L. Moses, “Tumour microenvironment—TGFΒ: the molecular Jekyll and Hyde of cancer,” Nature Reviews Cancer, vol. 6, no. 7, pp. 506–520, 2006. View at: Publisher Site  Google Scholar
 K. Naka, T. Hoshii, T. Muraguchi et al., “TGFΒFOXO signalling maintains leukaemiainitiating cells in chronic myeloid leukaemia,” Nature, vol. 463, no. 7281, pp. 676–680, 2010. View at: Publisher Site  Google Scholar
 K. Miyazono, “Tumour promoting functions of TGFΒ in CMLinitiating cells,” The Journal of Biochemistry, vol. 152, no. 5, pp. 383–385, 2012. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2013 Jinseog Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.