Influence of SH2B3, MTHFD1L, GGCX, and ITGB3 Gene Polymorphisms on theVariability on Warfarin Dosage Requirements and Susceptibility to CVD in the Jordanian Population
1
Department of Applied Biological Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
2
Department of Biotechnology and Genetic Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
3
Department of Hematopathology, King Hussein Medical Center (KHMC), Jordan Royal Medical Services (RMS), Amman 11118, Jordan
4
Department of Anatomy, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia
5
Genomics and Personalized Medicine Unit, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2020, 10(3), 117; https://doi.org/10.3390/jpm10030117
Submission received: 10 August 2020
/
Revised: 4 September 2020
/
Accepted: 7 September 2020
/
Published: 9 September 2020
(This article belongs to the Special Issue Cardiovascular Disease Prevention in the Era of Personalized Medicine)
Abstract
:The purpose of this study was to investigate the effects of the SH2B3, MTHFD1L, GGCX, and ITGB3 gene variants on the efficacy of warfarin treatment and its effects on the risk of cardiovascular disorders in Jordanian patients. The selected genes and their polymorphisms are involved in many Genome-Wide Association Study (GWAS) associated with cardiovascular disease and the variability of warfarin therapy. The current study conducted a genetic association and pharmacogenetics study in (212) Jordanian cardiovascular patients treated with warfarin and (213) healthy controls. DNA extraction and the Mass ARRAY™ system were used to genotype ten selected polymorphisms within four genes (SH2B3, MTHFD1L, GGCX, and ITGB3). This study confirmed a genetic association of MTHFD1L rs6922269 Single Nucleotide Polymorphism (SNP) with warfarin sensitivity during the initial and stabilization phases of treatment. Moreover, this SNP showed significant differences in the initial and maintenance doses of warfarin. This study also found an association between the genetic haplotypes (AGC and GAT) within the SH2B3 gene and responsiveness to warfarin. However, possession of an MTHFD1L rs491552 variant allele was found to affect the outcome measure of the international normalized ratio (INR) during the stabilization phase of warfarin treatment. In contrast, there was no association between all selected SNPs and susceptibility to cardiovascular disorders. This study extends the current understanding of the high variability of the warfarin response, including variability in dose requirements and susceptibility to cardiovascular disease in the Jordanian-Arab population. Other studies on a larger sample and in different ethnic groups could help to better understand the pharmacogenetics of warfarin and its application in personalized medicine.
1. Introduction
The term “pharmacogenetics” was developed in 1959 and defined as “the study of the variability of genome structure in response to drugs due to heredity”; since that time, numerous studies have been conducted to investigate the effects of genetic variants in the drug responses among individuals. However, warfarin is one of the best-known drugs for having a high variability in responses due to individual genetic variants [1,2]. Although it is the most commonly prescribed anticoagulant drug for the treatment of many cardiovascular diseases, including deep vein thrombosis, arterial thromboembolism, and heart valve replacement [3], the adjustment of warfarin doses has been complicated, due to its narrow therapeutic range, which was associated with bleeding or thrombosis in high or low doses, respectively [4]. Thus, regular monitoring of the anticoagulation status should be performed for each patient by measuring the international normalized ratio (INR) [5]. In addition to the environmental factors that include age, body mass index, smoking, gender, vitamin inducers, concomitant medication, and diet, genetic factors and ethnic characteristics affect the pharmacokinetics and pharmacodynamics of warfarin and lead to changes in the efficacy and safety between individuals [6]. Consequently, several studies on the pharmacogenetics of warfarin have been carried out, and it has been shown that the polymorphisms of some genes play an important role in predicting the warfarin dose. However, the most commonly studied genes were the VKORC1 gene (encoding vitamin K epoxide reductase complex subunit 1), which is the target of warfarin, and the CYP2C9 gene (cytochrome P450 superfamily) responsible for S-warfarin metabolism and clearance [7,8,9].
Recent studies have shown that polymorphisms in various genes are responsible for the variability of warfarin response and, therefore, have a significant effect on the warfarin required dose [10,11]. Among them, GGCX (gamma-glutamyl carboxylase), CYP4F2 (cytochrome P450, family 4, subfamily F, polypeptide 2), ApoE (apolipoprotein E), and FVII (blood clotting factor VII) are genes that can help predict a stable therapeutic dose of warfarin [11,12,13,14,15]. Recently, several algorithms have been proposed to predict the appropriate warfarin dose for each patient by combining the CYP2C9 and VKORC1 variants with other clinical and environmental factors [16,17].In fact, there are many complications and challenges in the exchange of personal medicine into clinical settings, especially in the Arab population [18,19]. Therefore, it is necessary to expand studies on candidate genes involved in the pharmacokinetics and pharmacodynamics of warfarin to improve both the efficacy and safety of drugs. Examining several genes that affect warfarin metabolism and are involved in cardiovascular disease (CVD) may help to provide more accurate treatment by reducing the side effects [18,20].
For this reason, four genes (SH2B3, MTHFD1L, GGCX, and ITGB3) were analyzed to determine their influence on CVD risk, warfarin sensitivity, and responsiveness. The SH2B3 gene belongs to the SH2B adaptors family. This gene is located on chromosome 12 (12k24) and encodes the SH2B3 protein, which is responsible for the negative regulation of cytokine signaling [21]. However, it has been shown that genetic variability in this region is associated with multiple symptoms, including type one diabetes, risk of coronary heart disease, and myocardial infarction [22,23].
The methylene-tetrahydrofolate dehydrogenase (NADP + -dependent) 1-like gene (MTHFD1L) is involved in the mitochondrial metabolism of tetrahydrofolate [24]. However, the polymorphism of this gene is closely related to the development of coronary heart disease (CHD), as found in a recent meta-analysis [25]. Moreover, a family study reported that a significant association was found between MTHFD1L polymorphism and the risk of myocardial infarction (MI) [26]. The γ-glutamyl carboxylase (GGCX) gene encodes the main enzyme responsible for carboxylation of the glutamic acid residue in the vitamin K-dependent coagulation factor to obtain its activity. Thus, polymorphisms, including this gene, can affect the coagulation factor cascade, and, therefore, it can lead to variability on an individual’s susceptibility to stroke disease [27]. The ITGB3 gene encodes the beta integrin subunit; this subunit, together with the alpha subunit encoding by ITGA2B, are involved in some of the specific platelet receptors for fibrinogen, a protein that regulates platelet aggregation, thrombosis, and hemostasis [28,29]. However, it was discovered that the ITGB3 gene was a highly polymorphic region, and some of these polymorphisms were associated with cardiovascular diseases [30]. The objective of the present study was to demonstrate the impacts of SH2B3, MTHFD1L, GGCX, and ITGB3 polymorphisms on the risk of CVD and the warfarin sensitivity and responsiveness in Jordanian patients.
2. Materials and Methods
2.1. Study Population
The study population included 425 subjects, of whom 212 were irrelevant warfarin outpatients recruited from the Anticoagulation Clinic at the Queen Alia Heart Institute (QAHI) in Amman, Jordan, and the remaining 213 were healthy noncardiovascular control groups. All participants accompanied the written consent of the Arab-Jordanian population. This study was approved by the Human Research Committees of the Jordan University of Science and Technology in Irbid and the Royal Medical Services of Amman. The enrolled patients met the following inclusion criteria: (1) patients who received warfarin for at least three months, (2) who undergo the anticoagulation clinic regularly, and (3) are older than 18 years. The most important exclusion criteria included: (1) patients without written consent or registered clinical data, (2) the use of CYP2C9 inhibitors or induction drugs, (3) get concomitant medications that are interacting with warfarin, (4) alcohol consumption, and (5) pregnant women.
2.2. Data Collection and Follow-Up Time
Blood samples were taken to obtain the INR value and to genotype the selected SNPs. The medical records include the average weekly warfarin dose and INR values, indication for warfarin, bleeding events, and concomitant medication and demographic information (gender, smoking status, age, and body mass index) reported during the patient’s clinical visits through semi-standardized interviews and from patient’s medical records. Both warfarin doses and INR measurements were documented in two stages. The primary stage is at the start of therapy, wherein the warfarin dose is determined dependent on the patient’s clinical factors and the indication of treatment, notwithstanding the genetics factor. Therefore, the INR measurement is unstable and is referred to as the initiation phase of treatment [31,32]. In the second stage, where the INR measurement is stabilized within the therapeutic range for at least three consecutive clinical visits, this phase becomes referred to as the stabilization phase of treatment.
2.3. Outcome Measure
This study was split into two branches: genetic association study (comparison of selected SNPs between patients and controls) and pharmacogenetics study (exhibited the effects of selected SNPs on warfarin therapy during the initial and stabilization phases of treatment). However, the pharmacogenetics study was also divided into two basic categories. The sensitivity to warfarin was the first. Patients in this category were divided according to the warfarin required dose into warfarin-resistant patients (requiring higher doses to achieve a therapeutic INR (>49 mg/week)), normal patients (receiving moderate doses (21–49 mg/week)), and sensitive patients (require a lower dose (<21 mg/week)).The classes of this category were divided according to a study by Gordon et al. (2009) [33]. The second category involved responsiveness to warfarin and was based on Higashi et al. (2002), who divided patients into good responders (INR value within the therapeutic range), poor responders (INR below the target range), and ultra-responders (INR above the target range) [34].
2.4. SNP Selection and Genotyping
Ten SNPs of SH2B3, MTHFD1L, GGCX, and ITGB3 genes were selected from a public database such as the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/SNP/) and the SNP of Applied Biosystems database (http://www.appliedbiosystems). The genes, SNP IDs, and their information locationsare listed in Table S1. The Wizard genomic DNA purification kit (Promega, Madison, WI, USA) was used to extract the DNA from the blood sample. Subsequently, samples that met the quantitative requirements were sent to the Australian Genome Research Center (AGRF) to test the genotype with the Mass ARRAYiPLEX GOLD system, Sequenom, San Diego, CA, USA. The Mass ARRAYTM system protocol and primer information are available upon request.
2.5. Statistical Analysis
To investigate which of the selected SNPs is associated with CVD risk and warfarin sensitivity and responsiveness, multivariate analyses, including a one-way ANOVA, chi-square test, Tukey’s honest significant differences (HSD) post hoc multiple comparison test, and nonparametric correlation tests, were performed. For all analyses, SPSS version 21.0 (SPSS Inc., Chicago, IL, USA) was used.
3. Results
3.1. Patient Characteristics
This study incorporated 212 cardiovascular patients treated with warfarin anticoagulants and 213 healthy controls recruited from Jordan. The genotypes of SH2B3, MTHFD1L, GGCX, and ITGB3 polymorphisms, plus clinical data for the participants, were evaluated to investigate their effects on the risk of cardiovascular disease and warfarin sensitivity and responsiveness during the initial phase treatment. Of these, 139 patients (66%) achieved the stabilization phase; therefore, they were used to assess the genetic effects of the aforementioned polymorphisms on the response and sensitivity to warfarin during the stabilization phase of therapy. Overall, 69.6% of the patients were found to be good metabolizers, 15.2% were extensive metabolizers, and 15.2% poor metabolizers. The demographic and clinical characteristics of the patients in each group were confirmed by a previous study by AL-Eitan et al. (2019) [35].
3.2. Genotyping and Allelic Frequencies
Of the ten selected SNPs, three SNPs were not polymorphic (monomorphic): rs28928872 of the GGCX gene and rs398122372 and rs398122374 of the ITGB3 gene; therefore, only seven SNPs were included in this study. The latter went beyond quality control, with high accuracy and a low discordance rate. The minor allele frequency and Hardy Weinberg equilibrium (HWE) p-values for all participants are summarized in Table S2. The genotypic frequency of the SH2B3 and MTHFD1L genes in patients and controls are exhibited in Table 1.
For all selected SNPs, there was no statistically significant difference between patients and controls (p > 0.05). Whereas significant associations were found between MTHFD1L haplotypes (GGAG) and cardiovascular disease in patients and healthy controls (p = 0.04) (Table S3).
3.3. Association of SH2B3 and MTHFD1L Polymorphisms with Warfarin Sensitivity during the Initiation and Stabilization Phases of Therapy
Patients were characterized based on the dosage needed to reach therapeutic INR into three different groups: sensitive, moderate, and resistant groups. No significant association was observed between the SH2B3 and MTHFD1L haplotypes and the warfarin sensitivity (p > 0.05) (Table S4). Moreover, all examined SNPs showed no significant differences between the warfarin groups and the sensitivity to warfarin during the initial and stabilization phases of therapy with p > 0.05, except for MTHFD1L rs6922269, which showed that 26.2% of the AG carriers were resistant to warfarin and, therefore, required higher doses to achieve the therapeutic INR, while only 5.9% of the wild-type AA was in the initial phase of the treatment with p = 0.03. Additionally, 36.9% of the rs6922269 AG carriers were resistant to warfarin and, therefore, required a higher dose to achieve the therapeutic INR, and only 18.2% of the wild-type AA were resistant in the stabilization phase of the treatment (p = 0.05) (Table 2, Table 3 and Table 4).
Furthermore, there were statistically significant differences between this SNP and the dosages required to reach the INR target during the initial and stabilization phases of treatment (p = 0.02 and p = 0.004, respectively) (Table 4). Post-hoc tests also showed significant correlations between this SNP and the required warfarin dose (p-value ≤ 0.05) (Table S5).
3.4. Association of SH2B3 and MTHFD1L Polymorphisms and Warfarin Responsiveness during Initiation and Stabilization Phases of Therapy
Depending on the response to warfarin, the patients were divided into three groups: poor, good, and ultra-responder. There were no significant differences between the frequencies of the different genotypes in the three groups in the initial phase and stabilization phase of the therapy with p > 0.05 (Table 5 and Table 6). However, a significant association was found with the SH2B3 haplotypes AGC and GAT and the ability to respond to warfarin (p = 0.002) (Table S6). There were no significant differences between the selected SNPs and the INR values measured in the initial phase of therapy in 212 patients receiving warfarin (p > 0.05). However, differences were found between MTHFD1L rs491552 and the INR values measured during the stabilization phase in 139 patients with an overall p-value = 0.02 (Table 7 and Table S7).
3.5. Association between Warfarin Dose and Clinical Data
The correlation between the warfarin dose and clinical features was evaluated in previous studies using multi-regression analyses, and there was no significant association between the warfarin dose and age, body mass index, gender, treatment indication, or comorbidities (p > 0.5) [36].
4. Discussion
This study reports genotype polymorphisms for the SH2B3, MTHFD1L, GGCX, and ITGB3 genes in association with the risk of CVD in Jordanian cardiovascular patients and the relationship between these polymorphisms in addition to non genetic factors for sensitivity and the responses to warfarin during the initial and stabilization phases of treatment. Our data suggest that there is an association between MTHFD1L polymorphism rs6922269 and warfarin sensitivity during the initiation and stabilization phases of treatment, and this indicates that this polymorphism plays an important genetic role for the variations in warfarin dose requirements. Moreover, our data revealed a significant association between MTHFD1L rs491552 polymorphism and the INR outcome measures during the stabilization phase of treatment. However, no correlation was found among all the polymorphisms studied and the risk of developing a cardiovascular disorder.
Our results regarding the genotype and allelic frequencies of the GGCX and ITGB3 genes show that theses polymorphisms are not polymorphic (monomorphic), which means polymorphisms in these two genes are not found in our population; therefore, they were excluded from our analysis. While the minor allele frequencies for rs11065987, rs17696736, and rs3184504 SNPs within the SH2B3 gene in our population are 38%, 38%, and 39%, respectively, which is inconsistent with the frequencies in the European population, with 34%, 42%, and 40–49%, respectively [37,38,39]. For the polymorphisms rs6922269 and rs803422 within the MTHFD1L gene, the minor allele frequency in our population for both is 26%, which is in agreement with the European population with 27% [24]. While the rs491552 SNP is high in our population, with 46%, the rs803455 is low, with only 9%. Although several previous studies have shown a significant association between rs11065987, rs17696736, and rs3184504 SNPs within the SH2B3 gene and susceptibility to cardiovascular diseases, including blood pressure, ischemic stroke, myocardial infarction, and coronary artery disease, our results failed to find this correlation in our population (p > 0.05) (Table 1) [23,40,41]. For MTHFD1L SNPs, our results showed no association between these SNPs and CVD risk (p > 0.05) (Table 1),while rs6922269 was observed to be significantly associated in the previous study with the risk of CVD, and that was first reported by a meta-analysis of two large genome-wide association studies [26]. Inaccordance with our results, other studies showed no significant association between this polymorphism and the risk of CVD [25,42].
To our knowledge, no studies have investigated the correlation between SH2B3 and MTHFD1L polymorphisms and the effectiveness of warfarin treatments. In this study, we examined the relationship between these polymorphisms and the sensitivity and responsiveness of warfarin therapy in the initial and stabilization phases of therapy in Jordanian cardiovascular patients. The results of this pharmacogenetics study suggest that, in both treatment phases, there was no significant association of SH2B3 polymorphisms with the sensitivity and responsiveness of warfarin (p > 0.05) (Table 2, Table 3, Table 4, Table 5 and Table 6), except for a significant relationship between SH2B3 haplotypes and the response to warfarin (p = 0.002) (Table S6). Meanwhile, our results showed that (rs6922269) was associated with warfarin sensitivity during the initiation phase of the treatment.
We found that patients with the variant allele A are at an increased risk of warfarin resistance. With 26.2% of the patients with AG, they were resistant to warfarin, while only 10.4% of the homozygous of the wild-type allele GG were resistant (overall, p = 0.003) (Table 2). Consequently, our results showed that patients with AG required 43.91 mg/week, while homozygous GG patients required only 34.49 mg/week (p = 0.02) (Table 4). Besides, this SNP tends towards statistical significance in relation with sensitivity to warfarin during the stabilization phase of the treatment, in which it was found that 36.8% of patients with AG were resistant to warfarin, and only 14.1% of GG was resistant (overall, p = 0.05) (Table 3). In accordance, our results showed that patient carriers for AG required 44.52 mg/week, while patients homozygous for GG required 34.28 mg/week only (p = 0.00) (Table 4).
Regarding warfarin responsiveness, our results did not show a significant association between the selected SNPs and the ability to respond to warfarin during the initiation phase. Although the rs491552 MTHFD1L SNP did not show a statistically significant value, it was close to being significant (p = 0.07) (Table 5). The patients carrying the variant allele G showed a high risk of being ultra-responders (12.1%), so the INR value over the target INR (their average INR values were 2.61), while 0.0% of the wild-type allele AA carriers were ultra-responders, and their INR values were 2.3 (p = 0.03) (Table 7).
5. Conclusions
Our data show the lack of a significant association between the SH2B3 and MTHFD1L SNPs and CVD risk in the Jordanian population. On the other hand, the MTHFD1L rs6922269 SNP showed a significant association with sensitivity and response to warfarin during the initial and the stabilization phases of the treatment. However, there was a significant relationship between this SNP, and the variability of warfarin required doses during both therapy phases. A significant association was also found between the MTHFD1L rs491552 SNP and the ability to respond to warfarin and the INR outcome values. To verify our results, more research is needed on larger sample sizes and on different ethnic populations. Individualized warfarin therapy should be done to maintain a safe and effective anticoagulation treatment in patients with cardiovascular disease. Thus, further pharmacogenetics studies are needed to evaluate the effects of other clinical and genetic factors and to facilitate the prevention and treatment of cardiovascular diseases.
Supplementary Materials
The supplemental materials for this project are available online at https://www.mdpi.com/2075-4426/10/3/117/s1. Table S1: Genes and SNPs Characteristics. Table S2: List of SNPs, their minor allele frequencies, and HWE p-values. Table S3: The distributions of SH2B3 and MTHFD1L haplotypes and 211 Cardiovascular patients in compare to 213 healthy controls. Table S4: The distributions of SH2B3 and MTHFD1L haplotypes among 212 warfarin sensitive patients. Table S5: Post Hoc Tests for theAssociation of SH2B3 and MTHFD1L SNPs with variability on warfarin required doses. Table S6: The distributions of SH2B3 and MTHFD1L haplotypes among 212 warfarin responsiveness patients. Table S7: Post Hoc Tests for theAssociation of SH2B3 and MTHFD1L SNPs with INR Treatment Outcome.
Author Contributions
L.N.A.-E. designed the study. L.N.A.-E., A.Y.A., and R.H.K. were responsible for the clinical data and blood samples collection. L.N.A.-E., A.Y.A., and M.A.A. analyzed and interpreted the data. L.N.A.-E. and A.Y.A. prepared the manuscript. All authors helped in reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Deanship of Research at Jordan University of Science and Technology under grant number 203/2014.
Conflicts of Interest
None of the authors has any conflicts of interest to disclose. We confirm that we have read the journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
References
- Vogel, F. Moderne Probleme der Humangenetik. Ergebnisse der Inneren Medizin und Kinderheilkunde; Springer: Berlin/Heidelberg, Germany, 1959; pp. 52–125. [Google Scholar]
- Mroziewicz, M.; Tyndale, R.F. Pharmacogenetics: A tool for identifying genetic factors in drug dependence and response to treatment. Addict. Sci. Clin. Pract. 2010, 5, 17. [Google Scholar] [PubMed]
- Kim, Y.; Smith, A.; Wu, A.H. C3435T polymorphism of MDR1 gene with warfarin resistance. Clin. Chim. Acta 2013, 425, 34–36. [Google Scholar] [CrossRef] [PubMed]
- Eriksson, N.; Wadelius, M. Prediction of warfarin dose: Why, when and how? Pharmacogenomics 2012, 13, 429–440. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dentali, F.; Donadini, M.P.; Clark, N.; Crowther, M.A.; Garcia, D.; Hylek, E. Brand name versus generic warfarin: A systematic review of the literature. Pharmacother. J. Hum. Pharmacol. Drug Ther. 2011, 31, 386–393. [Google Scholar] [CrossRef]
- Eichelbaum, M.; Ingelman-sundberg, M.; Evans, W.E. Pharmacogenomics and Individualized drug theraby. Annu. Rev. Med. 2006, 57, 119–137. [Google Scholar] [CrossRef]
- D’Andrea, G.; D’Ambrosio, R.L.; Di Perna, P.; Chetta, M.; Santacroce, R.; Brancaccio, V.; Margaglione, M.A. Polymorphism in the VKORC1 gene is associated with an interindividual variability in the dose-anticoagulant effect of warfarin. Blood 2005, 105, 645–649. [Google Scholar] [CrossRef]
- Muszkat, M.; Blotnik, S.; Elami, A.; Krasilnikov, I.; Caraco, Y. Warfarin metabolism and anticoagulant effect: A prospective, observational study of the impact of CYP2C9 genetic polymorphism in the presence of drug-disease and drug-drug interactions. Clin. Ther. 2007, 29, 427–437. [Google Scholar] [CrossRef]
- Al-Eitan, L.N.; Almasri, A.Y.; Al-Habahbeh, S.O. Effects of coagulation factor VII polymorphisms on warfarin sensitivity and responsiveness in Jordanian cardiovascular patients during the initiation and maintenance phases of warfarin therapy. Pharmacogenom. Personal. Med. 2019, 12, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Pautas, E.; Moreau, C.; Gouin-Thibault, I.; Golmard, J.L.; Mahe, I.; Legendre, C.; Beaune, P. Genetic factors (VKORC1, CYP2C9, EPHX1, and CYP4F2) are predictor variables for warfarin response in very elderly, frail inpatients. Clin. Pharmacol. Ther. 2010, 87, 57–64. [Google Scholar] [CrossRef]
- Kumar, D.K.; Shewade, D.G.; Loriot, M.A.; Beaune, P.; Balachander, J.; Chandran, B.S.; Adithan, C. Effect of CYP2C9, VKORC1, CYP4F2 and GGCX genetic variants on warfarin maintenance dose and explicating a new pharmacogenetic algorithm in South Indian population. Eur. J. Clin. Pharmacol. 2014, 70, 47–56. [Google Scholar] [CrossRef]
- Caldwell, M.D.; Awad, T.; Johnson, J.A.; Gage, B.F.; Falkowski, M.; Gardina, P.; King, C.R. CYP4F2 genetic variant alters required warfarin dose. Blood 2008, 111, 4106–4112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lal, S.; Sandanaraj, E.; Jada, S.R.; Kong, M.C.; Lee, L.H.; Goh, B.C.; Chowbay, B. Influence of APOE genotypes and VKORC1 haplotypes on warfarin dose requirements in Asian patients. Br. J. Clin. Pharmacol. 2008, 65, 260–264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Viitanen, L.; Pihlajamäki, J.; Miettinen, R.; Kärkkäinen, P.; Vauhkonen, I.; Halonen, P.; Laakso, M. Apolipoprotein E gene promoter (–219G/ T) polymorphism is associated with premature coronary heart disease. J. Mol. Med. 2001, 79, 732–737. [Google Scholar] [CrossRef] [PubMed]
- Al-Eitan, L.N.; Almasri, A.Y.; Al-habahbeh, S.O. Impact of a variable number tandem repeat in the CYP2C9 promoter on warfarin sensitivity and responsiveness in Jordanians with cardiovascular disease. Pharm. Pers. Med. 2019, 12, 15–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Borgiani, P.; Ciccacci, C.; Forte, V.; Romano, S.; Federici, G.; Novelli, G. Allelic variants in the CYP2C9 and VKORC1 loci and interindividual variability in the anticoagulant dose effect of warfarin in Italians. Pharmacogenomics 2007, 8, 1545–1550. [Google Scholar] [CrossRef] [PubMed]
- Hamberg, A.K.; Dahl, M.L.; Barban, M.; Scordo, M.G.; Wadelius, M.; Pengo, V.; Jonsson, E.N. A PK-PD model for predicting the impact of age, CYP2C9, and VKORC1 genotype on individualization of warfarin therapy. Clin. Pharmacol. Ther. 2007, 81, 529–538. [Google Scholar] [CrossRef] [PubMed]
- AL-Eitan, L.N.; Tarkhan, A.H. Practical Challenges and Translational Issues in Pharmacogenomics and Personalized Medicine from 2010 Onwards. Curr. Pharm. Pers. Med. Former. Curr. Pharm. 2016, 14, 7–17. [Google Scholar] [CrossRef]
- Al-eitan, L.N.; Haddad, Y.A. Emergence of Pharmacogenomics in Academic Medicine and Public Health in Jordan: History, Present State and Prospects. Curr. Pharm. Pers. Med. Former. Curr. Pharm. 2014, 12, 167–175. [Google Scholar] [CrossRef]
- Xie, H.G.; Frueh, F.W. Pharmacogenomics steps toward personalized medicine. Per. Med. 2005, 2, 325–337. [Google Scholar] [CrossRef]
- Ji, Y.; Song, Y.; Wang, Q.; Xu, P.; Zhao, Z.; Li, X.; Chen, C. Sex-specific association of SH2B3 and SMARCA4 polymorphisms with coronary artery disease susceptibility. Oncotarget 2017, 8, 59397–59407. [Google Scholar] [CrossRef] [Green Version]
- Todd, J.A.; Walker, N.M.; Cooper, J.D.; Smyth, D.J.; Downes, K.; Plagnol, V.; Lowe, C.E. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes The Wellcome Trust Case Control Consortium. Nat. Genet. 2007, 39, 857–864. [Google Scholar] [CrossRef] [PubMed]
- Hong, L.; Jiang, Y.F.; Chen, M.; Zhang, N.N.; Yang, H.J.; Rui, Q.; Zhou, Y.F. Role of SH2B3 R262W gene polymorphism and risk of coronary heart disease A PRISMA-compliant meta-analysis. Medicine 2018, 97, e13436. [Google Scholar] [CrossRef] [PubMed]
- Palmer, B.R.; Slow, S.; Ellis, K.L.; Pilbrow, A.P.; Skelton, L.; Frampton, C.M.; Whalley, G.A. Genetic polymorphism rs6922269 in the MTHFD1L gene is associated with survival and baseline active vitamin B12 levels in post-acute coronary syndromes patients. PLoS ONE 2014, 9, e89029. [Google Scholar] [CrossRef] [PubMed]
- Angelakopoulou, A.; Shah, T.; Sofat, R.; Shah, S.; Berry, D.J.; Cooper, J.; Maniatis, N. Comparative analysis of genome-wide association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration. Eur. Heart J. 2012, 33, 393–407. [Google Scholar] [CrossRef] [Green Version]
- Samani, N.J.; Erdmann, J.; Hall, A.S.; Hengstenberg, C.; Mangino, M.; Mayer, B.; Barrett, J.H. UKPMC Funders Group Author Manuscript Genomewide Association Analysis of Coronary Artery Disease. N. Engl. J. Med. 2007, 357, 443–453. [Google Scholar] [CrossRef] [Green Version]
- Kamali, X.; Wulasihan, M.; Yang, Y.C.; Lu, W.H.; Liu, Z.Q.; He, P.Y. Association of GGCX gene polymorphism with warfarin dose in atrial fibrillation population in Xinjiang. Lipids Health Dis. 2013, 12, 149. [Google Scholar] [CrossRef] [Green Version]
- Weiss, L.A.; Veenstra-VanderWeele, J.; Newman, D.L.; Kim, S.J.; Dytch, H.; McPeek, M.S.; Abney, M. Genome-wide association study identifies ITGB3 as a QTL for whole blood serotonin. Eur. J. Hum. Genet. 2004, 12, 949–954. [Google Scholar] [CrossRef] [Green Version]
- Li, M.P.; Xiong, Y.; Xu, A.; Zhou, J.P.; Tang, J.; Zhang, Z.L.; Chen, X.P. Association of platelet ITGA2B and ITGB3 polymorphisms with ex vivo antiplatelet effect of ticagrelor in healthy Chinese male subjects. Int. J. Hematol. 2014, 99, 263–271. [Google Scholar] [CrossRef]
- Khatami, M.; Heidari, M.M.; Soheilyfar, S. Common rs5918 (PlA1/A2) polymorphism in the ITGB3 gene and risk of coronary artery disease. Arch. Med. Sci. Atheroscler. 2016, 1, 9–15. [Google Scholar] [CrossRef]
- Gage, B.F.; Lesko, L.J. Pharmacogenetics of warfarin: Regulatory, scientific, and clinical issues. J. Thromb. Thrombolysis 2008, 25, 45–51. [Google Scholar] [CrossRef]
- Md Arif, K.; Rahman, M.A. A Review of Warfarin Dosing and Monitoring. Faridpur. Med. Coll. J. 2018, 13, 40–43. [Google Scholar] [CrossRef]
- Klein, T.; Altman, R.; Eriksson, N. Europe PMC Funders Group Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data. N. Engl. J. Med. 2009, 360, 753–764. [Google Scholar] [PubMed]
- Higashi, M.K.; Veenstra, D.L.; Kondo, L.M.; Wittkowsky, A.K.; Srinouanprachanh, S.L.; Farin, F.M.; Rettie, A.E. Association Between CYP2C9 Genetic variants and anticoagulation-related outcomes during warfarin therapy. JAMA 2002, 287, 1690–1698. [Google Scholar] [CrossRef] [PubMed]
- AL-Eitan, L.N.; Almasri, A.Y.; Khasawneh, R.H. Effects of CYP2C9 and VKORC1 polymorphisms on warfarin sensitivity and responsiveness during the stabilization phase of therapy. Saudi Pharm. J. 2019, 27, 484–490. [Google Scholar] [CrossRef]
- AL-Eitan, L.; Almasri, A.; Khasawneh, R. Impact of CYP2C9 and VKORC1 Polymorphisms on Warfarin Sensitivity and Responsiveness in Jordanian Cardiovascular Patients during the Initiation Therapy. Genes 2018, 9, 578. [Google Scholar] [CrossRef] [Green Version]
- Soranzo, N.; Spector, T.D.; Mangino, M.; Kühnel, B.; Rendon, A.; Teumer, A.; Salo, P. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat. Genet. 2009, 41, 1182–1190. [Google Scholar] [CrossRef] [Green Version]
- Douroudis, K.; Kisand, K.; Nemvalts, V.; Rajasalu, T.; Uibo, R. Allelic variants in the PHTF1-PTPN22, C12orf30 and CD226 regions as candidate susceptibility factors for the type 1 diabetes in the Estonian population. BMC Med. Genet. 2010, 11, 11. [Google Scholar] [CrossRef] [Green Version]
- Zhernakova, A.; Elbers, C.C.; Ferwerda, B.; Romanos, J.; Trynka, G.; Dubois, P.C.; Bardella, M.T. Evolutionary and Functional Analysis of Celiac Risk Loci Reveals SH2B3 as a Protective Factor against Bacterial Infection. Am. J. Hum. Genet. 2010, 86, 970–977. [Google Scholar] [CrossRef] [Green Version]
- Dichgans, M.; Malik, R.; König, I.R.; Rosand, J.; Clarke, R.; Gretarsdottir, S.; Thorleifsson, G.; Mitchell, B.D.; Assimes, T.L.; Levi, C.; et al. Shared genetic susceptibility to ischemic stroke and coronary artery disease: A genome-wide analysis of common variants. Stroke 2014, 45, 24–36. [Google Scholar] [CrossRef] [Green Version]
- Ding, K.; Kullo, I.J. Geographic differences in allele frequencies of susceptibility SNPs for cardiovascular disease. BMC Med. Genet. 2011, 12, 55. [Google Scholar] [CrossRef] [Green Version]
- Muendlein, A.; Saely, C.H.; Rhomberg, S.; Sonderegger, G.; Loacker, S.; Rein, P.; Drexel, H. Evaluation of the association of genetic variants on the chromosomal loci 9p21.3, 6q25.1, and 2q36.3 with an giographically characterized coronary artery disease. Atherosclerosis 2009, 205, 174–180. [Google Scholar] [CrossRef] [PubMed]
Table 1.
The distributions of SH2B3 and MTHFD1L SNPs in 212 cardiovascular patients and 213 healthy controls.
Table 1.
The distributions of SH2B3 and MTHFD1L SNPs in 212 cardiovascular patients and 213 healthy controls.
| Gene | SNP ID | Model | Patients % | Controls % | p-Value * |
|---|---|---|---|---|---|
| SH2B3 | AA/GA/GG | 39.6/42.9/17.4 | 42.2/41.3/16.4 | 0.86 | |
| rs11065987 | AA/(GA + GG) | 39.6/60.4 | 42.2/57.8 | 0.58 | |
| (AA + GA)/GG | 82.5/17.4 | 83.6/16.4 | 0.78 | ||
| AA/AG/GG | 39.5/42.4/18.1 | 42.5/42.5/15.1 | 0.67 | ||
| rs17696736 | AA/(AG + GG) | 39.5/60.5 | 42.5/57.5 | 0.54 | |
| (AA + AG)/GG | 81.9/18.1 | 84.9/15.1 | 0.41 | ||
| CC/TC/TT | 38.7/42/19.3 | 41.8/42.2/16 | 0.62 | ||
| rs3184504 | CC/(TC + TT) | 38.7/61.3 | 41.8/58.2 | 0.51 | |
| (CC + TC)/TT | 80.7/19.3 | 84/16 | 0.36 | ||
| MTHFD1L | GG/CG/CC | 28.4/49.5/22.1 | 33.5/41.3/25.2 | 0.24 | |
| rs491552 | GG/(CG + CC) | 28.4/71.6 | 33.5/66.5 | 0.27 | |
| (GG + CG)/CC | 77.9/22.1 | 84.8/25.2 | 0.45 | ||
| GG/AG/AA | 54.2/37.7/8 | 55.9/37.6/6.6 | 0.84 | ||
| rs6922269 | GG/(AG + AA) | 54.2/45.8 | 55.9/44.1 | 0.74 | |
| (GG + AG)/AA | 92/8 | 93.4/6.6 | 0.57 | ||
| AA/GA/GG | 53.3/35.4/11.3 | 59.6/34.3/6.1 | 0.12 | ||
| rs803422 | AA/(GA + GG) | 53.3/46.7 | 59.6/40.4 | 0.19 | |
| (AA + GA)/GG | 88.7/11.3 | 93.9/6.1 | 0.06 | ||
| AA/GA/GG | 86.7/12.8/0.5 | 79.8/18.3/1.9 | 0.1 | ||
| rs803455 | AA/(GA + GG) | 86.7/13.3 | 79.8/20.2 | 0.06 | |
| (AA + GA)/GG | 99.5/0.5 | 98.1/1.9 | 0.17 |
* Chi–square test with p < 0.05 is considered significant.
Table 2.
Association of SH2B3 and MTHFD1L SNPs with warfarin sensitivity during the initiation phase of therapy of 212 cardiovascular patients treated with warfarin.
Table 2.
Association of SH2B3 and MTHFD1L SNPs with warfarin sensitivity during the initiation phase of therapy of 212 cardiovascular patients treated with warfarin.
| Gene | SNP ID | Genotype | Sensitive a | Moderate b | Resistance c | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | rs11065987 | AA | (14/84) 16.7% | (57/84) 67.9% | (13/84) 15.5% | 0.99 |
| p-value * | 0.87 | 0.97 | 0.99 | |||
| GA | (13/91) 14.3% | (63/91) 69.2% | (15/91) 16.5% | |||
| p-value * | 0.96 | 1 | 0.99 | |||
| GG | (5/37) 13.5% | (26/37) 70.3% | (6/37) 16.2% | |||
| p-value * | 0.96 | 0.98 | 1 | |||
| rs17696736 | AA | (13/83) 15.7% | (57/83) 68.7% | (13/83) 15.7% | 0.99 | |
| p-value* | 0.99 | 1 | 1 | |||
| AG | (14/89) 15.7% | (61/89) 68.5% | (14/89) 15.7% | |||
| p-value * | 0.99 | 0.99 | 1 | |||
| GG | (5/38) 13.2% | (27/38) 71.1% | (6/38) 15.8% | |||
| p-value * | 0.92 | 0.96 | 1 | |||
| rs3184504 | CC | (14/82) 17.1% | (56/82) 68.3% | (12/82) 14.6% | 0.94 | |
| p-value * | 0.81 | 0.99 | 0.90 | |||
| TC | (12/89) 13.5% | (61/89) 68.5% | (16/89) 18% | |||
| p-value * | 0.86 | 1 | 0.81 | |||
| TT | (6/41) 14.6% | (29/41) 70.7% | (6/41) 14.6% | |||
| p-value * | 1 | 0.96 | 0.97 | |||
| MTHFD1L | rs491552 | CC | (6/45) 13.3% | (34/45) 75.6% | (5/45) 11.1% | 0.79 |
| p-value * | 0.98 | 0.66 | 0.63 | |||
| CG | (13/101) 12.9% | (71/101) 70.3% | (17/101) 16.8% | |||
| p-value * | 0.86 | 1 | 0.90 | |||
| GG | (10/58) 17.2% | (38/58) 65.5% | (10/58) 17.2% | |||
| p-value* | 0.74 | 0.67 | 0.93 | |||
| rs6922269 | AA | (2/17) 11.8% | (14/17) 82.4% | (1/17) 5.9% | 0.03 | |
| p-value * | 0.92 | 0.46 | 0.49 | |||
| AG | (11/80) 13.8% | (48/80) 60% | (21/80) 26.2% | |||
| p-value * | 0.91 | 0.09 | 0.01 | |||
| GG | (19/115) 16.5% | (84/115) 73% | (12/115) 10.4% | |||
| p-value * | 0.82 | 0.36 | 0.05 | |||
| rs803422 | AA | (2/24) 8.3% | (8/24) 75% | (4/24) 16.7% | 0.58 | |
| p-value * | 0.62 | 0.79 | 1 | |||
| GA | (15/75) 20% | (50/75) 66.7% | (10/75) 13.3% | |||
| p-value * | 0.34 | 0.88 | 0.73 | |||
| GG | (15/113) 13.3% | (78/113) 69% | (20/113) 17.7% | |||
| p-value * | 0.73 | 1 | 0.78 | |||
| rs803455 | AA | (0/1) 0% | (1/1) 100% | (0/1) 0% | 0.49 | |
| p-value * | 0.91 | 0.80 | 0.91 | |||
| GA | (7/27) 25.9% | (17/27) 63% | (3/27) 11.1% | |||
| p-value * | 0.25 | 0.76 | 0.79 | |||
| GG | (25/183) 13.7% | (128/183) 69.9% | (30/183) 16.4% | |||
| p-value * | 0.30 | 0.84 | 0.74 |
* Chi-square test with p-value < 0.05 is considered significant. a Warfarin-sensitive group (required minimum warfarin dose < 21 mg/week). b Warfarin-moderate response group (required average warfarin dose between 21 and 49 mg/week). c Warfarin-resistance group (required high warfarin dose > 49 mg/week).
Table 3.
Association of SH2B3 and MTHFD1L SNPs with warfarin sensitivity during the stabilization phase of therapy of 139 cardiovascular patients treated with warfarin.
Table 3.
Association of SH2B3 and MTHFD1L SNPs with warfarin sensitivity during the stabilization phase of therapy of 139 cardiovascular patients treated with warfarin.
| Gene | SNP ID | Genotype | Sensitive a | Moderate b | Resistance c | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | rs11065987 | AA | (5/52) 9.6% | (36/52) 69.2% | (11/52) 21.2% | 0.41 |
| p-value * | 0.46 | 0.38 | 0.86 | |||
| GA | (9/63) 14.3% | (37/63) 58.7% | (17/63) 27% | |||
| p-value * | 1 | 0.79 | 0.72 | |||
| GG | (6/24) 25% | (13/24) 54.2% | (5/24) 20.8% | |||
| p-value * | 0.27 | 0.69 | 0.93 | |||
| rs17696736 | AA | (4/51) 7.8% | (36/51) 70.6% | (11/51) 21.6% | 0.31 | |
| p-value * | 0.23 | 0.23 | 0.87 | |||
| AG | (10/61) 16.4% | (35/61) 57.4% | (16/61) 26.2% | |||
| p-value * | 0.87 | 0.70 | 0.87 | |||
| GG | (6/25) 24% | (13/25) 52% | (6/25) 24% | |||
| p-value * | 0.34 | 0.57 | 1 | |||
| rs3184504 | CC | (5/51) 9.8% | (36/51) 70.6% | (10/51) 19.6% | 0.35 | |
| p-value * | 0.50 | 0.27 | 0.68 | |||
| TC | (9/61) 14.8% | (34/61) 55.7% | (18/61) 29.5% | |||
| p-value * | 1 | 0.42 | 0.37 | |||
| TT | (6/27) 22.2% | (16/27) 59.3% | (5/27) 18.5% | |||
| p-value * | 0.30 | 0.95 | 0.78 | |||
| MTHFD1L | rs491552 | CC | (2/25) 8% | (15/25) 60% | (8/25) 32% | 0.71 |
| p-value * | 0.89 | 0.73 | 0.62 | |||
| CG | (10/70) 14.3% | (46/70) 65.7% | (14/70) 20% | |||
| p-value * | 0.98 | 0.82 | 0.45 | |||
| GG | (5/36) 13.9% | (21/36) 58.3% | (10/36) 27.8% | |||
| p-value * | 0.96 | 0.62 | 0.86 | |||
| rs6922269 | AA | (2/11) 18.2% | (7/11) 63.6% | (2/11) 18.2% | 0.05 | |
| p-value * | 0.93 | 0.99 | 0.90 | |||
| AG | (7/57) 12.3% | (29/57) 50.9% | (21/57) 36.8% | |||
| p-value * | 0.84 | 0.08 | 0.01 | |||
| GG | (11/71) 15.5% | (50/71) 70.4% | (10/71) 14.1% | |||
| p-value * | 0.93 | 0.11 | 0.02 | |||
| rs803422 | AA | (2/14) 14.3% | (7/14) 50% | (5/14) 35.7% | 0.64 | |
| p-value * | 1 | 0.63 | 0.54 | |||
| GA | (9/52) 17.3% | (30/52) 57.7% | (13/52) 25% | |||
| p-value * | 0.75 | 0.74 | 0.97 | |||
| GG | (9/73) 12.3% | (49/73) 67.1% | (15/73) 20.5% | |||
| p-value * | 0.77 | 0.41 | 0.65 | |||
| rs803455 | GA | (4/20) 20% | (12/20) 60% | (4/20) 20% | 0.74 | |
| p-value * | 0.75 | 0.98 | 0.94 | |||
| GG | (16/118) 13.6% | (74/118) 62.7% | (28/118) 23.7% | |||
| p-value * | 0.75 | 0.98 | 0.94 |
* Chi-square test with p-value < 0.05 is considered significant. a Warfarin-sensitive group (required minimum warfarin dose < 21 mg/week). b Warfarin-moderate response group (required average warfarin dose between 21 and 49 mg/week). c Warfarin-resistance group (required high warfarin dose > 49 mg/week).
Table 4.
Association of SH2B3 and MTHFD1L SNPs with the variability on warfarin required doses.
| Gene | SNP ID | Genotype | Initiation Dose | Overall p-Value * | Maintenance Dose | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | AA | 35.66 (12.87) | 37.31 (14.39) | |||
| rs11065987 | GA | 37.32 (14.74) | 0.1 | 40.00 (19.24) | 0.69 | |
| GG | 45.32 (46.11) | 37.48 (21.18) | ||||
| AA | 36.03 (12.70) | 37.90 (14.11) | ||||
| rs17696736 | AG | 36.74 (14.78) | 0.09 | 38.42 (17.37) | 0.98 | |
| GG | 45.36 (45.48) | 38.00 (20.90) | ||||
| CC | 35.70 (12.74) | 36.61 (13.60) | ||||
| rs3184504 | TC | 37.92 (14.86) | 0.17 | 41.07 (19.83) | 0.35 | |
| TT | 43.74 (44.11) | 36.57 (20.12) | ||||
| MTHFD1L | CC | 41.17 (40.85) | 39.06 (14.54) | |||
| rs491552 | CG | 38.13 (15.48) | 0.62 | 39.76 (19.65) | 0.88 | |
| GG | 36.65 (14.47) | 37.84 (17.42) | ||||
| AA | 34.64 (09.87) | 35.25 (13.99) | ||||
| rs6922269 | AG | 43.91 (33.84) | 0.02 | 44.52 (22.01) | 0.004 | |
| GG | 34.49 (11.81) | 34.28 (12.84) | ||||
| AA | 38.53 (13.39) | 40.86 (18.38) | ||||
| rs803422 | GA | 36.58 (15.18) | 0.79 | 38.63 (19.85) | 0.87 | |
| GG | 38.94 (28.49) | 38.07 (16.45) | ||||
| AA | 34.80 (……) | |||||
| rs803455 | GA | 33.11 (14.99) | 0.49 | 35.22 (15.28) | 0.39 | |
| GG | 38.60 (24.01) | 38.85 (18.11) |
* One-way ANOVA test with p-value < 0.05 is considered significant; mean standard deviation in roundbrackets.
Table 5.
Association of SH2B3 and MTHFD1L SNPs with warfarin responsiveness during the initiation phase of therapy of 212 cardiovascular patients treated with warfarin.
Table 5.
Association of SH2B3 and MTHFD1L SNPs with warfarin responsiveness during the initiation phase of therapy of 212 cardiovascular patients treated with warfarin.
| Gene | SNP ID | Genotype | Poor a | Good b | Ultra c | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | rs11065987 | AA | (17/84) 20.2% | (62/84) 73.8% | (5/84) 6% | 0.95 |
| p-value * | 0.86 | 0.77 | 0.92 | |||
| GA | (15/91) 16.5% | (72/91) 79.1% | (4/91) 4.4% | |||
| p-value * | 0.82 | 0.72 | 0.90 | |||
| GG | (7/37) 18.9% | (28/37) 75.7% | (2/37) 5.4% | |||
| p-value * | 1 | 1 | 1 | |||
| rs17696736 | AA | (17/83) 20.5% | (62/83) 74.7% | (4/83) 4.8% | 0.98 | |
| p-value * | 0.85 | 0.92 | 0.98 | |||
| AG | (15/89) 16.9% | (69/89) 77.5% | (5/89) 5.6% | |||
| p-value * | 0.85 | 0.93 | 0.98 | |||
| GG | (7/38) 18.4% | (29/38) 76.3% | (2/38) 5.3% | |||
| p-value * | 1 | 1 | 1 | |||
| rs3184504 | CC | (17/82) 20.7% | (60/82) 73.2% | (5/82) 6.1% | 0.89 | |
| p -value * | 0.78 | 0.69 | 0.98 | |||
| TC | (14/89) 15.7% | (71/89) 79.8% | (4/89) 4.5% | |||
| p-value * | 0.68 | 0.62 | 0.99 | |||
| TT | (8/41) 19.5% | (31/41) 75.6% | (2/41) 4.9% | |||
| p-value * | 0.90 | 0.93 | 1 | |||
| MTHFD1L | rs491552 | CC | (11/45) 24.4% | (34/45) 75.6% | (0/45) 0% | 0.07 |
| p-value * | 0.59 | 1 | 0.19 | |||
| CG | (18/101) 17.8% | (79/101) 78.2% | (4/101) 4% | |||
| p-value * | 0.90 | 0.67 | 0.67 | |||
| GG | (10/58) 17.2% | (41/58) 70.7% | (7/58) 12.1% | |||
| p-value * | 0.91 | 0.6 | 0.03 | |||
| rs6922269 | AA | (4/17) 23.5% | (11/17) 64.7% | (2/17) 11.8% | 0.56 | |
| p-value * | 0.85 | 0.49 | 0.44 | |||
| AG | (15/80) 18.8% | (60/80) 75% | (5/80) 6.3% | |||
| p-value * | 1 | 0.93 | 0.87 | |||
| GG | (20/115) 17.4% | (91/115) 79.1% | (4/115) 3.5% | |||
| p-value * | 0.92 | 0.6 | 0.47 | |||
| rs803422 | AA | (4/24) 16.7% | (20/24) 83.3% | (0/24) 0% | 0.55 | |
| p-value * | 0.98 | 0.70 | 0.48 | |||
| GA | (11/75) 14.7% | (60/75) 80% | (4/75) 5.3% | |||
| p-value * | 0.58 | 0.66 | 1 | |||
| GG | (24/113) 21.2% | (82/113) 72.6% | (7/113) 6.2% | |||
| p-value * | 0.73 | 1 | 0.78 | |||
| rs803455 | AA | (0/1) 0% | (1/1) 100% | (0/1) 0% | 0.64 | |
| p-value * | 0.89 | 0.86 | 0.97 | |||
| GA | (4/27) 14.8% | (23/27) 85.2% | (0/27) 0% | |||
| p-value * | 0.87 | 0.51 | 0.43 | |||
| GG | (35/183) 19.1% | (137/183) 74.9% | (11/183) 6% | |||
| p-value * | 0.83 | 0.45 | 0.41 |
* Chi-square test with p-value < 0.05 is considered significant. a Poor responders (international normalized ratio (INR) value below target). b Good responders who have an INR in the target range (therapeutic range). c Ultra-responders (INR over target).
Table 6.
Association of SH2B3 and MTHFD1L SNPs with warfarin responsiveness during the stabilization phase of therapy of 139 cardiovascular patients treated with warfarin.
Table 6.
Association of SH2B3 and MTHFD1L SNPs with warfarin responsiveness during the stabilization phase of therapy of 139 cardiovascular patients treated with warfarin.
| Gene | SNP ID | Genotype | Poor a | Good b | Ultra c | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | rs11065987 | AA | (4/52) 7.7% | (44/52) 84.6% | (4/52) 7.7% | 0.56 |
| p-value * | 0.90 | 0.40 | 0.32 | |||
| GA | (4/63) 6.3% | (58/63) 92.1% | (1/63) 1.6% | |||
| p-value * | 1 | 0.61 | 0.35 | |||
| GG | (1/24) 4.2% | (22/24) 91.7% | (1/24) 4.2% | |||
| p-value ** | 0.88 | 0.91 | 1 | |||
| rs17696736 | AA | (4/51) 7.8% | (44/51) 86.3% | (3/51) 5.9% | 0.93 | |
| p-value * | 0.90 | 0.73 | 0.80 | |||
| AG | (4/61) 6.6% | (55/61) 90.2% | (2/61) 3.3% | |||
| p-value * | 1 | 0.93 | 0.85 | |||
| GG | (1/25) 4% | (23/25) 92% | (1/25) 4% | |||
| p-value * | 0.85 | 0.87 | 1 | |||
| rs3184504 | CC | (4/51) 7.8% | (43/51) 84.3% | (4/51) 7.8% | 0.53 | |
| p-value * | 0.88 | 0.37 | 0.30 | |||
| TC | (4/61) 6.6% | (56/61) 91.8% | (1/61) 1.6% | |||
| p-value * | 1 | 0.68 | 0.39 | |||
| TT | (1/27) 3.7% | (25/27) 92.6% | (1/27) 3.7% | |||
| p-value * | 0.81 | 0.82 | 0.99 | |||
| MTHFD1L | rs491552 | CC | (2/25) 8% | (23/25) 92% | (0/25) 0% | 0.26 |
| p-value * | 0.97 | 0.84 | 0.48 | |||
| CG | (5/70) 7.1% | (63/70) 90% | (2/70) 2.9% | |||
| p-value * | 0.99 | 0.86 | 0.60 | |||
| GG | (2/36) 5.6% | (30/36) 83.3% | (4/36) 11.1% | |||
| p-value * | 0.94 | 0.51 | 0.09 | |||
| rs6922269 | AA | (3/11) 27.3% | (6/11) 54.5% | (2/11) 18.2% | 0.30 | |
| p-value * | 0.01 | <0.0001 | 0.06 | |||
| AG | (4/57) 7% | (51/57) 89.5% | (2/57) 3.5% | |||
| p-value * | 0.98 | 1 | 0.93 | |||
| GG | (2/71) 2.8% | (67/71) 94.4% | (2/71) 2.8% | |||
| p-value * | 0.2 | 0.13 | 0.67 | |||
| rs803422 | AA | (0/14) 0% | (14/14) 100% | (0/14) 0% | 0.18 | |
| p-value * | 0.58 | 0.39 | 0.70 | |||
| GA | (1/52) 1.9% | (49/52) 94.2% | (2/52) 3.8% | |||
| p-value * | 0.24 | 0.34 | 0.98 | |||
| GG | (8/73) 11% | (61/73) 83.6% | (4/73) 5.4% | |||
| p-value * | 0.08 | 0.08 | 0.78 | |||
| rs803455 | GA | (0/20) 0% | (20/20) 100% | (0/20) 0% | 0.24 | |
| p-value * | 0.44 | 0.24 | 0.59 | |||
| GG | (9/118) 7.6% | (103/118) 87.3% | (6/118) 5.1% | |||
| P-value* | 0.44 | 0.24 | 0.59 |
* Chi-square test with p-value < 0.05 is considered significant. a Poor responders (INR value below target). b Good responders who have an INR value in the target range (therapeutic range). c Ultra-responders (INR over target).
Table 7.
Association of SH2B3 and MTHFD1L SNPs with the INR treatment outcomes.
| Gene | SNP ID | Genotype | Initiation INR | Overall p-Value * | Maintenance INR | Overall p-Value * |
|---|---|---|---|---|---|---|
| SH2B3 | AA | 2.41 (0.75) | 2.68 (0.43) | |||
| rs11065987 | GA | 2.51 (0.79) | 0.70 | 2.68 (0.37) | 0.80 | |
| GG | 2.45 (0.79) | 2.74 (0.35) | ||||
| AA | 2.39 (0.75) | 2.69 (0.42) | ||||
| rs17696736 | AG | 2.53 (0.80) | 0.53 | 2.68 (0.38) | 0.86 | |
| GG | 2.44 (0.78) | 2.73 (0.34) | ||||
| CC | 2.41 (0.76) | 2.68 (0.43) | ||||
| rs3184504 | TC | 2.50 (0.77) | 0.72 | 2.68 (0.38) | 0.75 | |
| TT | 2.46 (0.81) | 2.74 (0.33) | ||||
| MTHFD1L | CC | 2.33 (0.75) | 2.67 (0.39) | |||
| rs491552 | CG | 2.40 (0.69) | 0.15 | 2.61 (0.37) | 0.02 | |
| GG | 2.61 (0.93) | 2.83 (0.40) | ||||
| AA | 2.30 (0.67) | 2.66 (0.49) | ||||
| rs6922269 | AG | 2.55 (0.82) | 0.73 | 2.66 (0.42) | 0.65 | |
| GG | 2.42 (0.76) | 2.72 (0.35) | ||||
| AA | 2.46 (0.97) | 2.64 (0.37) | ||||
| rs803422 | GA | 2.47 (0.50) | 0.62 | 2.73 (0.41) | 0.68 | |
| GG | 1.70 (……) | 2.68 (0.38) | ||||
| AA | 1.90 (……) | |||||
| rs803455 | GA | 2.48 (0.66) | 0.76 | 2.85 (0.31) | 0.05 | |
| GG | 2.46 (0.79) | 2.67 (0.40) |
* One-way ANOVA test with p-value < 0.05 is considered significant; mean standard deviation in round brackets.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
AL-Eitan, L.N.; Almasri, A.Y.; Khasawneh, R.H.; Alghamdi, M.A. Influence of SH2B3, MTHFD1L, GGCX, and ITGB3 Gene Polymorphisms on theVariability on Warfarin Dosage Requirements and Susceptibility to CVD in the Jordanian Population. J. Pers. Med. 2020, 10, 117. https://doi.org/10.3390/jpm10030117
AMA Style
AL-Eitan LN, Almasri AY, Khasawneh RH, Alghamdi MA. Influence of SH2B3, MTHFD1L, GGCX, and ITGB3 Gene Polymorphisms on theVariability on Warfarin Dosage Requirements and Susceptibility to CVD in the Jordanian Population. Journal of Personalized Medicine. 2020; 10(3):117. https://doi.org/10.3390/jpm10030117
Chicago/Turabian StyleAL-Eitan, Laith N., Ayah Y. Almasri, Rame H. Khasawneh, and Mansour A. Alghamdi. 2020. "Influence of SH2B3, MTHFD1L, GGCX, and ITGB3 Gene Polymorphisms on theVariability on Warfarin Dosage Requirements and Susceptibility to CVD in the Jordanian Population" Journal of Personalized Medicine 10, no. 3: 117. https://doi.org/10.3390/jpm10030117
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
