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CYP2D6 (C2850T, G1846A, C100T) polymorphisms, haplotypes and MDR analysis in predicting coronary artery disease risk in north-west Indian population: A case-control study
Accepted Manuscript CYP2D6 (C2850T, G1846A, C100T) polymorphisms, haplotypes and MDR analysis in predicting coronary artery disease risk in north-west Indian population: A case-control study
M.A. Bhat, G. Gandhi PII: DOI: Reference:
S0378-1119(18)30369-X doi:10.1016/j.gene.2018.04.008 GENE 42732
To appear in:
Gene
Received date: Revised date: Accepted date:
28 October 2017 23 March 2018 4 April 2018
Please cite this article as: M.A. Bhat, G. Gandhi , CYP2D6 (C2850T, G1846A, C100T) polymorphisms, haplotypes and MDR analysis in predicting coronary artery disease risk in north-west Indian population: A case-control study. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2018.04.008
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ACCEPTED MANUSCRIPT CYP2D6 (C2850T, G1846A, C100T) Polymorphisms, Haplotypes and MDR Analysis in Predicting Coronary Artery Disease Risk in North-West Indian Population: A CaseControl Study
Bhat M. A* and Gandhi. G
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Short title: Association of CYP2D6 Polymorphisms with CAD
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Department of Human Genetics, Guru Nanak Dev University, Amritsar-143005, Punjab, India
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*Corresponding author:
Dr. Mohd. Akbar Bhat Presently at Multidisciplinary Research Unit Government Medical College, Amritsar-143001, Punjab, India Tel: +91-8725905591 E-mail: [email protected]
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ACCEPTED MANUSCRIPT ABSTRACT Aims: The present study was aimed to evaluate the association of C2850T, G1846A and C100T polymorphisms of the CYP2D6 with coronary artery disease (CAD) in North-West Indian population. Methods: In this case-control study, 200 patients with CAD and 200 age-, genderand ethnicity-matched healthy controls were genotyped for C2850T, G1846A and C100T polymorphisms of CYP2D6 using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: Genotype and allele distributions of C2850T and
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G1846A polymorphisms of the CYP2D6 were significantly different between cases and controls
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(p=0.038, p=0.021; p=0.048, p=0.012, respectively) whereas the distribution of genotype and
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allele for C100T polymorphism did not differ significantly (p=0.098, p=0.117, respectively). The 2850T and1846A variants were significantly associated with the increased risk of developing CAD, as observed from the odds ratios for the 2850 T/T and 1846 G/A genotypes (OR: 2.44,
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95% CI: 1.99-4.99, p=0.015 and OR: 1.62, 95% CI: 1.02-2.56, p=0.041, respectively). Moreover, the recessive model in C2850T and the dominant model in G1846A are the best fit inheritance
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models to predict the susceptible gene effects (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031 and OR: 1.70, 95% CI: 1.10-2.62, p=0.016, respectively). On gender stratification, these associations were
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observed only in females in addition to the C/T genotype of C2850T (OR: 2.52, 95% CI: 1.424.38, p=0.001) and C100T (OR: 3.18, 95% CI: 1.52-6.67, p=0.002). Furthermore, it is also
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observed that the CAT (OR: 2. 61, 95% CI: 1.07-6.34, p=0.035) and TAC (OR: 15. 22, 95% CI: 1.97-117.58, p=0.009) are high-risk haplotypes for CAD in the total group, whereas, the TGC
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(OR: 1. 64, 95% CI: 1.02-2.62, p=0.042) and CAT (OR: 4. 21, 95% CI: 1.12-15.59, p=0.035) haplotypes provide gender-specific risk in females. Conclusions: Our results indicate significant
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association of C2850T, G1846A and C100T polymorphisms of CYP2D6 with CAD especially in females of North-West Indian population.
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Key words: SNP-SNP interaction, Haplotype, CYP2D6, Polymorphism
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ACCEPTED MANUSCRIPT Introduction Coronary artery disease (CAD) is a complex heterogeneous disorder and it has become a major public health problem associated with life-style factors in both developed and developing countries like India, influenced by both genetic and environmental determinants (Pranavchand and Readdy, 2013; Liu et al., 2014). The genetic background of CAD is thought to be complex
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involving many genes encoding the activity of enzymes, responsible for metabolism of both
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exogenous and endogenous substances. Genetic polymorphisms in such genes can result in
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decreased or complete loss of enzyme activity which in turn are unable to detoxify these exogenous and endogenously produced substances, thereby increasing the risk for developing
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CAD. Cytochrome P450 2D6 (CYP2D6) enzyme plays an instrumental role in oxidation of xenobiotics including drugs and inactivation of endogenously produced reactive species and
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polymorphisms in CYP2D6 lead to inter-individual differences in enzymatic activity and have been linked to susceptibility to CAD (Teh et al., 2004).
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CYP2D6 is the only functional gene with 9 exons in the CYP2D6 sub-family located on chromosome 22q13.1 (Sheng et al., 2007). It is predominantly expressed in the liver, brain and
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heart where it is involved in the metabolism of exogenous as well as endogenously-produced substances and neurotransmitters responsible for maintaining homeostasis. CYP2D6 is highly
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polymorphic with inter-ethnic variability and more than 135 different allelic variants have been described (www.cypalleles.ki.se/cyp2d6.htm). These polymorphic variants result from point
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mutations, gene rearrangements, deletions or duplications in the entire gene and the action of these allelic variants causes normal, increased, decreased or complete loss of enzyme activity
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(Kitada, 2003). The presence of various combinations (Kurose et al., 2012) of these CYP2D6 alleles in an individual classifies the person as a poor metabolizer (PM), intermediate metabolizer (IM), extensive metabolizer (EM) or ultra-extensive metabolizer (UEM). Interindividual variation in the expression of CYP2D6 may alter susceptibility to disease induced by its substrates. The PM individuals metabolize drugs at slower rate causing accumulation of unmetabolized drugs thereby increasing the risk of drug toxicity and side-effects and will be therefore at higher risk to develop disease (Zanger et al., 2004). It is known that the CYP2D6 enzyme is involved in the metabolism of various drugs, including those for cardiovascular and
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ACCEPTED MANUSCRIPT central nervous system (CNS) conditions besides its implication in some CNS diseases viz. Parkinson’s disease, Lewy body dementia and Alzheimer’s disease (Barbeau et al., 1995; McCann et al., 1997; Tanaka et al., 1998). This raises an important hypothesis that the enzyme may also have a role in the development of CAD. It may act on some endogenous substances that predispose individuals to CAD or alternatively on some environmental substances that may contribute to the development of the disease.
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To the existing knowledge, no attempt has been made regarding the association of C2850T,
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G1846A and C100T polymorphisms of CYP2D6 with the risk of CAD in the North-West Indian
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population. In literature, only one cohort study has come to attention that suggested the role of G1846A and C100T polymorphisms of CYP2D6 with cardiovascular disease in Malaysian population (Teh et al., 2004), no report is available on the association of C2850T polymorphism
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of CYP2D6 with CAD. In view of the increased incidence of CAD and paucity of data available on the association between CYP2D6 genotypes and susceptibility to CAD in North-West Indian
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population, we aimed to investigate the association of C2850T, G1846A and C100T polymorphisms of CYP2D6 gene with CAD in the proposed study population..
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Materials and Methods
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Study population
In the present case-control study, 200 patients diagnosed with CAD were enrolled from local
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hospitals (A. P. Heart-Care Hospital and Mata Kaulan Ji Bandi Chod Charitable Hospital, Amritsar) and 200 age-, gender-and ethnicity-matched healthy controls with no present or past
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family history of CAD or any other disease were enrolled from general population of Punjab (a North Indian state). Smoking habits were absent in this study group due to religious restrains
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whereas alcohol consumption was present in both, cases and controls. Family history of CAD was also recorded. Total cholesterol was measured by semi-automated clinical chemistry analyser (Erba Chem-7) using commercially available kits (Erba). The details of sample collection, inclusion and exclusion criteria of cases and controls have been described in a previous paper (Bhat and Gandhi, 2016) and of on-going treatment in Bhat and Gandhi (2017). Patients were diagnosed by the cardiologists as per electrographic (ECG) findings, echocardiography, performance on the treadmill test and serum creatine phosphokinase (CPK) levels. Prescribed medication-combinations initiated on disease-diagnosis (4.03±0.32 y) included
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ACCEPTED MANUSCRIPT Metoprolol (a beta-blocker), Aspirin and Ecosprin (anti-platelet), Ramipril (Ace-inhibitor) and Nitrocontin (nitrate). The study was approved by the Institutional Ethics Committee of Guru Nanak Dev University, Amritsar and the study protocol complies with the guidelines of the Declaration of Helsinki. After obtaining the written informed voluntary consent, demographic and disease-specific information such as age, age-of-onset-of-disease, duration-of-disease, gender, height, weight, body mass index, waist-and hip-circumference and waist-hip-ratio of each participant was documented on a pre-designed questionnaire. Blood pressure of each
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individual was measured after a 5-min rest in the sitting position using a standard mercury
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sphygmomanometer as per the recommendations of American Heart Association (1981). The
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average of three subsequent measurements was used as blood pressure values. Blood samples from all the study participants were withdrawn by a trained phlebotomist and collected into tubes
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containing EDTA as anticoagulant.
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Genotyping of CYP2D6 polymorphisms
Genomic DNA was extracted from blood samples using the salting-out method (Miller et al.,
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1988). The C2850T, G1846A and C100T polymorphisms of the CYP2D6 were analyzed by polymerase chain reaction-restriction fragment length polymorphism technique (Theophilus et
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al., 2006) using primer sequences
obtained from
literature.
All polymerase chain
reactions were in a volume of 15 μl and contained 50 ng target DNA, 1.5 mM MgCl2, 0.2 mM of
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each dNTPs, 10 μM of each primer and 1U Taq DNA polymerase. The reactions were performed in Mastercycler gradient thermal cycler (Eppendorf, Hamburg, Germany) with an initial
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denaturation at 94°C for 5 min followed by 35 cycles of denaturation at 94°C for 45 sec, annealing at 69°C for 45 sec, elongation at 72°C for 45 sec for C2850T polymorphism, 35 cycles
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of denaturation at 94°C for 30 sec, annealing at 65°C for 45 sec, elongation at 72°C for 45 sec for G1846A polymorphism and 35 cycles of denaturation at 94°C for 45 sec, annealing at 65°C for 45 sec, elongation at 72°C for 45 sec for C100T polymorphism and a final extension at 72°C for 10 min. A negative control (PCR mixture without DNA template) was also run with each reaction to check for any contamination. The amplified products of C2850T (1029 bp), G1846A (309 bp) and C100T (345 bp) were observed on 1% agarose gel stained with ethidium bromide and then digested with Hha1, BstN1 and Hph1 restriction enzymes (New England Biolabs, USA), respectively. The digested products were resolved on ethidium bromide stained 2.5% agarose 5
ACCEPTED MANUSCRIPT gel. Genotyping of the C2850T, G1846A and C100T polymorphisms are described in Figures. 1a-c. For genotyping accuracy, samples with known genotypes were used in each batch as positive controls to evaluate the completeness of PCR product digestion. To rule out genotypic error, 10% random samples of known genotypes of each polymorphism with exploratory bidirectional DNA sequencing of few samples was done and the results were 100% concordant. Statistical analysis
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All statistical analysis was performed using the Statistical Package for Social Sciences (SPSS
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version 16.0 for windows) software. Continuous variables have been expressed as mean ±
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standard deviation and categorical variables, as counts and percentages. Differences between the means of continuous variables were evaluated by Student’s t-test. Differences in categorical variables and genotype distribution were tested by Chi-squared test with Yates correction or
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Fisher’s exact test. The association between the C2850T, G1846A and C100T genotypes and CAD risk was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CI)
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using binary logistic regression analysis, adjusting for potential risk factors such as age, gender, BMI, alcohol, hypertension, total cholesterol and family history. The disease risk was also
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evaluated under different genetic models (dominant, co-dominant and recessive) using WebAssotest program (http://www.ekstroem.com). Multifactor dimensionality reduction (MDR)
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analysis was carried out to evaluate gene-gene interaction (Ritchie et al., 2003). Linkage disequilibrium (LD), SNP haplotype analysis and the deviation from Hardy-Weinberg
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equilibrium was done by SNPStats software (Sole et al., 2006). The corrections for multiple comparisons were done by Bonferroni method wherever required and there was no difference in
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statistical significance even after Bonferroni correction (i.e. reducing significance level to p= 0.017). Power calculations were performed using CaTS-Power Calculator (Skol et al., 2006) and
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the present study with a total sample size of 400 participants had a statistical power of 85% at significance level of 0.05 to detect an association with an odds ratio of 1.5. A p-value <0.05 (two-tailed) was considered to be statistically significant. Results The demographic, anthropometric and disease-specific characteristics of the study subjects have been previously described in Bhat and Gandhi (2016). Disease-specific characteristics of patients stratified by genotypes of C2850T, G1846A and C100T polymorphisms are presented in Table 1. Significant mean differences were observed for BMI (p=0.008), being higher in CAD patients 6
ACCEPTED MANUSCRIPT carrying CT+TT genotype and retained only in females (p=0.040) after gender analysis. However, no significant differences were found for disease-specific characteristics for different genotypes in G1846A and C100T. The genotype and allele frequency distribution for C2850T, G1846A and C100T polymorphisms are presented in Table 2. The distribution of genotype and allele frequencies in both cases and controls were in agreement with those predicted by Hardy-Weinberg equilibrium (p>0.05) for all
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the three SNPs. In C2850T polymorphism, the genotype and allele distribution between cases
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and controls was statistically significant in the total group and in females (p=0.038, p=0.021;
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p=0.001, p=0.001, respectively) but non-significant in males (p=0.104, p=0.629, respectively). Binary logistic regression analysis (Table 3) revealed that the T/T genotype was significantly associated with an almost 2.5-fold increased risk for CAD (OR: 2.44, 95% CI: 1.99-4.99,
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p=0.015). However after adjustment for risk factors of age, gender, BMI, alcohol, total cholesterol, hypertension and family history, the T/T genotype showed increased odds ratio,
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although not statistically significant (OR: 4.46, 95% CI: 0.82-24.37, p=0.085) while the C/T genotype independently conferred ~2.5-fold risk for CAD (OR: 2.47, 95% CI: 1.05-5.77,
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p=0.037). In addition, the CT genotype in co-dominant model (OR: 1.47, 95% CI: 1.08-2.01, p=0.013) and the TT genotype (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031) in recessive model have
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also shown significant association with increased risk of developing CAD (Table 2). On stratification for gender, the C/T (OR: 2. 52, 95% CI: 1.42-4.38, p=0.001) and T/T (OR: 3.2,
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95% CI: 1.36-7.59, p=0.008) genotypes showed 2.5-and 3.2-fold increased risk, respectively for CAD in females but not in males. On adjustment (Table 3), the C/T genotype independently
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conferred a five-fold risk for CAD (OR: 5. 10, 95% CI: 1.50-17.28, p=0.009) while the T/T genotype had an increased odds ratio, though not reaching statistical significance (OR: 6. 16,
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95% CI: 0.90-42.16, p=0.064). Genetic models (Table 2) have revealed that the minor T allele in the dominant model and CT genotype in the co-dominant model exhibit increased risk for CAD in females (OR: 2. 65, 95% CI: 1.56-4.48, p=0.001; OR: 2. 01, 95% CI: 1.35-2.99, p=0.001, respectively). For the G1846A polymorphism (Table 3), the genotype (p=0.048) and allele (p=0.012) frequencies were significantly different between cases and controls in total group but with only allelic difference in females (p=0.023) which was non-significant for both frequencies in males (p=0.533, p=0.117, respectively). The G/A genotype conferred 1.6-fold increased risk for CAD 7
ACCEPTED MANUSCRIPT (OR: 1.62, 95% CI: 1.02-2.56, p=0.041) which was however lost after adjustment (OR: 0.67, 95% CI: 0.27-1.65, p=0.381). The minor A allele in the dominant model (OR: 1.70, 95% CI: 1.10-2.62, p=0.016) and the AG genotype (OR: 1.55, 95% CI: 1.09-2.20, p=0.014) in the codominant model (Table 2) also conferred increased risk for CAD. On gender stratification, the G/A genotype (Table 3) conferred 1.9-fold increased risk for CAD in females (OR: 1.94, 95% CI: 1.07-3.52, p=0.030) which on adjustment was lost (OR: 0.38, 95% CI: 0.12-1.24, p=0.381). Among the genetic models, the minor A in the dominant model (OR: 1.95, 95% CI: 1.12-3.40,
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showed significant association with CAD in females (Table 2).
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p=0.017) and G/A genotype in the co-dominant model (OR: 1.65, 95% CI: 1.06-2.58, p=0.024)
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In the case of C100T polymorphism, the genotype (p=0.098) and allele (p=0.117) frequencies did not differ significantly between cases and controls. However on stratification by gender, a
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significant difference was observed for the genotype (p=0.003) and allele (p=0.001) frequency distribution only in females and not in males (p=0.493, p=0.117, respectively). The C/T
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genotype (Table 3) and dominant model (Table 2) increased 3.2-fold risk for CAD (OR: 3.18, 95% CI: 1.52-6.67, p=0.002) in females, which however was not retained after adjustment (OR:
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2.78, 95% CI: 0.69-11.18, p=0.150).
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The Multifactor Dimensionality Reduction (MDR) analysis was used to analyze the possible interactions between the C2850T, G1846A and C100T SNPs for CAD (Table 4). The 2-loci
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interaction model of C2850T and G1846A emerged as the best predictor of CAD potential based on the cross validation consistency (CVC) of 10/10, with the highest testing balance accuracy of
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56% and p<0.001. The entropy interaction graphical model (Figure 2a) revealed that C2850T and G1846A have significant synergistic interaction (0.34%) sharing the positive information
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gain with respect to CAD, whereas separately, there was contribution of both, the C2850T (1.19%) and G1846A (1.1%) towards CAD susceptibility (Table 5). The one-locus model of C2850T was in fact the best predictor of CAD potential, both in males (Figure 2b) with TBA 58% (CVC=10/10, p=0.043) and in females (Figure 2c) with TBA 62% (10/10, p<0.001), although prediction was higher for females. The selected SNPs of CYP2D6 were neither in LD in the total group nor after gender stratification even though they are located in the same gene (Figure 3a-3c). There was nonetheless significant association of global haplotypes with CAD in the total group (p=0.002) as well as in the females (p=0.001). In the total group, the observed haplotypes, CAT (OR: 2.61, 8
ACCEPTED MANUSCRIPT 95% CI: 1.07-6.34, p=0.035) and TAC (OR: 15.22, 95% CI: 1.97-117.58, p=0.009) conferred 2.6-and 15.2-fold increased risk for CAD. Furthermore after stratification, in females (Table 5), the TGC (OR: 1.64, 95% CI: 1.02-2.62, p=0.042) and CAT (OR: 4.21, 95% CI: 1.12-15.59, p=0.035) haplotypes showed 1.6-and 4.2-fold increased risk for CAD, respectively. Discussion In the present study, the association of C2850T, G1846A and C100T polymorphisms of the
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CYP2D6 with CAD in North-West Indian population was investigated. In literature, numerous
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reports have documented the role of CYP2D6 polymorphisms in predisposing to several kinds of
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tumors (Sobti et al., 2005; Surekha et al., 2010; Levkovich et al., 2011; Shukla et al., 2012) and central nervous system disorders Parkinson’s or Alzheimer’s diseases (Smith et al., 1992; Lu et
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al., 2014) in different ethnic groups. A single report has come to attention on the role of CYP2D6 polymorphisms in cardiovascular diseases but not from the North-West Indian population. This
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therefore is the first study of its kind investigating the association of C2850T, G1846A and C100T polymorphisms of CYP2D6 in North-West Indian CAD patients. In the present study, the
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frequency of homozygous T/T genotype of C2850T was found to be significantly higher in CAD patients compared to controls (13.5% vs. 7.0%). The T/T genotype conferred ~2.5-fold increased
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risk for CAD development, which is also supported by the recessive model. When stratified by gender, it was observed in female patients with the C/T genotype, there was a 2.5-fold and with
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the T/T genotype, there was 3.2-fold increased risk for developing CAD, suggestive of an association of the dominant and co-dominant models. This observation as a first study therefore
reported.
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exhibits an association of C2850T polymorphism with CAD as no studies on this SNP have been
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For G1846A, the frequency of heterozygous G/A genotype was observed to be significantly higher in CAD patients (29.0%) than in controls (21.0%). The G/A genotype provided 1.6-fold increased risk for developing CAD in the total group and 1.9-fold increased risk for CAD female patients, as also evidenced by the dominant and co-dominant models. Conversely, no significant association of C100T polymorphism with CAD was observed in the total group while there was a 3.2-fold increased risk in CAD females for the C/T genotype, indication of dominant mode of inheritance. For this SNP also, case-control studies are lacking in literature except for the work of Teh et al. (2004) in a Malaysian cohort reporting the higher frequency of both G1846A and 9
ACCEPTED MANUSCRIPT C100T SNPs in cardiovascular disease patients, suggested the role of CYP2D6 genotypes in cardiovascular disease. The potential explanations of these discrepancies in different populations might be attributed through differences in ethnic background, variations in the population structure, diverse genetic and environmental backgrounds, varied sample sizes and different study designs. SNP-SNP interaction was examined by MDR analysis because of the limitation of logistic
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regression analysis for modeling multifactor interactions. The MDR analysis showed that the
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C2850T alone does not play any significant role in increasing disease risk with the C2850T and
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G1846A interaction conferring increased risk for CAD. The observations on MDR and logistic regression analysis of the C2850T and G1846A polymorphisms exhibit both, there association and interaction for increased disease-risk. As haplotype analysis is more informative for
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investigating the genetic influence on disease manifestation compared to the effect of individual genotypes, haplotypes were constructed and analyzed for the possible association with CAD. The
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haplotype analysis inferred that CAT and TAC haplotypes have overall risk for CAD patients and the TGC and CAT haplotypes confer gender-specific risk in women for developing CAD in
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the studied North Indian group.
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Therefore the present study after association and haplotype analysis and SNP-SNP interaction, has revealed both, an individual and/or cumulative effect of C2850T and G1846A
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polymorphisms of CYP2D6 on the development of CAD. Stratification by gender further revealed the association of C100T and of C2850T and G1846A with disease risk and show cases
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that gender-stratification can provide more insights for disease association in male and female patients and the present findings are indicative that genetic polymorphisms of CYP2D6 may
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exert a greater influence on CAD in females than in males. Indeed, epidemiological data have shown that the risk of CAD differs among males and females because of hormonal differences. Coban et al. (2015) also observed gender differences while studying the relationship between (CYP19A1) polymorphism and hypertension in a Turkish population. As a first study of its kind, the results of the present study can be used as a baseline data for association of CYP2D6 polymorphisms with CAD. This can enable the construction of a panel of candidate genes for identifying the susceptible genotypes responsible for CAD at an earlier stage, so that the preventive measures and life-style modifications can be initiated to prevent disease progression.
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ACCEPTED MANUSCRIPT A number of limitations in the present study include the smaller sample size wherein a larger sample size could have provided more insights for CAD risk. Moreover analysis of other SNPs of CYP2D6 and functional studies are needed for elucidation of CYP2D6 in CAD. The lack of similar studies in literature especially from this region of India has not allowed for comparisons. Nevertheless the study has relevance as a first study to report the association of CYP2D6 polymorphisms with CAD. In conclusion, the present results showed that C2850T, G1846A and C100T polymorphisms of
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CYP2D6 are strong pre-disposing genetic variants for CAD, predominantly in women of North-
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West Indian Punjabi population. Extrapolation of other conclusions from the present study is
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limited and requires elucidation in large ethnic-specific and case-control studies to understand the role of CYP2D6 gene polymorphisms in CAD susceptibility.
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Acknowledgments
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We are thankful to Dr. Ajinder Pal Singh (A. P. Heart-Care Hospital) and Dr. Sudhir Abrol (Mata Kaulan Ji Bandi Chod Charitable Hospital) Amritsar for providing contact with CAD patients and their clinical information. We would also like to thank our study participants for
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providing their valuable information and blood samples.
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Funding/Support: This work was supported from the UGC DRS-SAP grant. Mohd. Akbar Bhat is grateful to Guru Nanak Dev University for providing the University with Potential for
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Excellence Fellowship for his Ph.D. programme.
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References
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Conflict of interest: The authors declare that there is no conflict of interest.
American Heart Association (1981). Report of subcommittee of post graduate education committee recommendation for human blood pressure determination of sphygmomanometer. Circulation 64: 510A-599B. Barbeau, A., Cloutier, T., Roy, M., Plasse, L., Paris, S. and Poirier, J. (1985). Ecogenetics of Parkinson’s disease 4-hydroxylation of debrisoquine. Lancet 2:1213-1216. Bhat M.A. and Gandhi G. (2016). Association of GSTT1 and GSTM1 gene polymorphisms with coronary artery disease in North Indian Punjabi population: a casecontrol study. Postgraduate Medical Journal 92:701-706.
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Bhat, M.A. and Gandhi, G. (2017). Assessment of DNA Damage in Leukocytes of Patients with Coronary Artery Disease by Comet Assay. International Heart Journal. 58: 1-4. Coban, N., Onat, A., Guclu-Geyik, F., Can, G. And Erginel-Unaltuna, N. (2015). Sexand obesity-specific association of aromatase (CYP19A1) gene variant with apolipoprotein B and hypertension. Archives of Medical Research 46: 564-571. Kitada, M. (2003). Genetic polymorphism of cytochrome P450 enzymes in Asian population: focus on CYP2D6. International Journal of Clinical Pharmacology 23: 3135. Kurose, K., Sugiyama, E. and Saito, Y. (2012). Population differences in major fuctional polymorphisms of Pharmacokinetics/ pharmacodynamics-related genes in Eastern Asians and Europeans: Implications in the clinical trials for novel drug development. Journal of Drug Metabolism and Pharmacokinetics 27: 9-54. Levkovich, N. N., Gorovenko, N. G. and Myasoedov, D. V. (2011). Association of polymorphic G1934a variant (Allele *4) Of Cyp2d6 Gene with increased risk of breast cancer development in Ukrainian women. Experimental Oncology 33: 136-139. Liu, D., Jiang, Z., Dai, L., Zhang, X., Yan, C. and Han, Y. (2014). Association between the-786 TfiC1 polymorphism in the promoter region of endothelial nitric oxide synthase (eNOS) and risk of coronary artery disease: a system at review and meta-analysis. Gene 545: 175-183. Lu, Y., Qin, X., Li, S., Zhang , X., He, Y., Peng ,Q., Deng, Y., Wang, J., Xie, L., Li, T. and Zeng, Z. (2014). Quantitative assessment of CYP2D6 polymorphisms and risk of Alzheimer's disease: a meta-analysis. Journal of the Neurological Sciences 343: 15-22. McCann, S.J., Pond, S.M., James, K.M. and Le Couteur, D. G. (1997). The association between polymorphismsin the cytochrome P-450 2D6 gene and Parkinson’s disease: A case-control study and meta-analysis. Journal of Neurological Sciences 153:50-53. Miller, S.A., Dykes, D.D. and Polesky, H.F. (1988). A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acid Research 16: 1215. Pranavchand, R. and Reddy, B.M. (2013). Current status of understanding of the genetic etiology of coronary heart disease. Journal of Postgraduate Medicine 59: 30-41. Ritcchie, M. D., Hahn, L. W. and Moore, J. H. (2003). Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogenecity. Genetic Epidemiology 24: 150-157. Sheng, H. H., Zeng, A. P., Zhu, W. X., Zhu, R. F., Li, H. M., Zhu, Z. D., Qin, Y., Jin, W., Liu, Y., Du, Y. L., Sun, J. and Xiao, H. S. (2007). Allelic distributions of CYP2D6 gene copy number variation in the Eastern Han Chinese population. Acta Pharmacologica Sinica 28: 279-286. Shukla, P., Gupta, D., Pant, M. C. and Parmar, D. (2012). CYP2D6 polymorphism: A predictor of susceptibility and response to chemoradiotherapy in head and neck cancer. Journal of Cancer Research and Therapeutics 8: 40-45.
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Skol, A. D., Scott, L. J., Abecasis, G. R. and Boehnke, M. (2006). Joint analysis is more efficient than replication-based analysis for two stage genome wide association studies. Nature Genetics 38: 209-213. Smith, C. A., Gough, A. C., Leigh, P. N., Summers, B. A., Harding, A. E., Maranganore, D. M., Sturman, S. G., Schapira, A. H, Williams, A. C., Spurr, N.K. and Wolf, C.R. (1992). Debrisoquine hydroxylase gene polymorphism and susceptibility to Parkinson’s disease. Lancet 339: 1375-1377. Sobti, R. C., Al-Badrana, A. I., Sharma, S., Sharmab, S. K., Krishanc, A. and Mohand, H. (2005). Genetic polymorphisms of CYP2D6, GSTM1 and GSTT1 genes and bladder cancer risk in North India. Cancer Genetics and Cytogenetics 156: 68-73. Sole, X., Guino, E., Valls, J., Iniesta, R. and Moreno, V. (2006). SNPStats: a web tool for the analysis of association studies. Bioinformatics 22: 1928-1929. Surekha, D., Sailaja, K., Nageswara, R. D., Padma, T., Raghunadharao, D. and Vishnupriya, V. (2010). CYP2D6*4 polymorphisms and breast cancer risk. Biology and Medicine 2: 49-55. Tanaka, S., Chen, X., Xia, Y., Kang, D. E., Matoh, N., Sundsmo, M., Thomas, R.G., Katzman, R., Thal, L.J., Trojanowski, J.Q., Saitoh, T., Ueda, K. and Masliah, E. (1998). Association of CYP2D microsatellite polymorphism with Lewy body variant of Alzheimer’s disease. Neurology 50:1556-1562. Teh, L. K., Zilfalil, B. A., Marina, I., Rosemi, B. S. and Ismail, R. (2004). Genetic polymorphism of CYP2D6 in patients with cardiovascular disease-a cohort study. Journal of Clinical Pharmacy and Therapeutics 29: 559-564. Theophilus, N.A., Chanfrasekaran, A., Sam, S.S., Gerard, N. and Rajagopal, K. (2006). CYP2D6 genetic polymorphism in South Indian populations. Biological and Pharmaceutical Bulletin 29: 1655-1658. Zanger, U.M., Raimundo, S. and Eichelbaum, M. (2004). Cytochrome P450 2D6: overview and update on pharmacology, genetics, and biochemistry. NaunynSchmiedeberg's Archives of Pharmacology 369: 23-37.
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ACCEPTED MANUSCRIPT CAPTIONS TO THE FIGURES Figure 1a: Restriction band pattern of CYP2D6*2 (C2850T). Lane M: 100bp molecular weight marker; lanes 1, 4, 6, 8, 9: a homozygous wild type CC genotype; lanes 2, 5, 7, 10, 11: a heterozygous CT genotype; lane 3: homozygous mutant TT genotype; lane 12: undigested control
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Figure 1b: Restriction band pattern of CYP2D6*4 (G1846A). Lane M: 100bp molecular weight marker; lanes 3, 4, 7: a homozygous wild type GG genotype; lanes 1, 6: a heterozygous GA genotype; lanes 2, 5: a homozygous mutant (AA) genotype; lane 8: undigested control
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Figure 1c: Restriction band pattern of CYP2D6*10 (C100T). Lane M: 100bp molecular weight marker; lanes 1, 3, 4, 6, 8, 9: a homozygous wild type CC genotype; lanes 2, 5: a heterozygous CT genotype; lane 10: undigested control
M
AN
Figure 2: Interaction entropy model for C2850T, G1846A and C100T polymorphisms of CYP2D6 and CAD risk in total group (a), males (b) and females (c). This graphical model describes the percent entropy explained by each SNP. Positive percent entropy indicates synergy whereas the negative percent indicates redundancy. Orange line indicated positive interaction between CYP2 and CYP4, green and blue color indicated weak and no interactions.
AC
CE
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Figure 3: Pair-wise linkage disequilibrium plot of CYP2D6 (C2850T, G1846A and C100T) SNPs in the total study group (a), males (b) and females (c). The hatch marks contain D´values, multiplied by100 to indicate the strength of LD between SNPs.
14
ACCEPTED MANUSCRIPT Table1.Comparison of demographic and disease-specific characteristics according to the C2850T, G1846A and C100T polymorphisms of CYP2D6 Variables
G1846A G/A+A/A, (n=48) 59±1.2
0.902
C100T C/C, (n=155) 59±0.9
C100T C/T, (n=45) 60±1.4
pvalue
pvalue
55±1.2
55±1.0
0.939
55±1.0
55±1.2
0.863
55±0.9
56±1.5
0.662
5±0.6 26.2±0.5 1.0±0.0
4±0.4 28.2±0.5 1.0±0.0
0.156 0.008 0.928
4±0.4 27.3±0.4 1.0±0.01
4±0.5 27.7±0.7 1.0±0.0
0.397 0.631 0.914
4±0.4 27.6±0.4 1.0±0.1
5±0.6 27.2±0.8 1.0±0.1
0.477 0.635 0.553
SBP (mmHg)
125.9±1.8
126.5±1.32
0.781
126.8±1.3
125.4±1.8
0.540
126.3±1.2
126.3±2.1
0.996
DBP (mmHg) MABP Pulse pressure Total cholesterol (mg/dl)
79.9±0.97 95.2±1.1 46.0±1.4
81.3±0.76 96.4±0.9 45.2±1.0
0.255 0.421 0.628
81.0±0.8 96.3±0.9 45.7±1.0
80.4±1.0 95.4±1.2 45.0±1.3
0.627 0.550 0.654
80.7±0.7 95.9±0.8 45.5±0.9
80.0±1.3 96.1±1.4 45.3±1.7
0.846 0.908 0.889
279.2±25.4
277.2±25.5
0.600
279.5±24.6
272.9±27.5
0.145
278.8±26.7
274.9±20.2
0.366
C/C, (n=34) 59±8.8
C/T+T/T, (n=39) 61±12.0
pvalue 0.444
G/G, (n=57) 60±10.1
G/A+A/A, (n=16) 60±12.6
pvalue 0.975
C/C, (n=60) 60±10.7
C/T, (n=13) 63±10.1
pvalue 0.359
55±9.2
59±12.0
0.228
57±10.5
57±12.4
0.903
57±10.5
58±12.4
0.615
4±5.1 26.1±4.7 1.0±0.1 124.3±17.1
3±3.9 27.9±4.5 1.0±0.1 127.2±16.8
0.278 0.089 0.156 0.474
3±4.6 27.6±4.4 1.0±0.1 127.1±17.6
3±4.3 26..0±5.3 1.0±0.1 121.4±13.4
0.716 0.153 0.172 0.235
3±4.4 27.4±4.8 1.0±0.1 126.4±18.0
4±4.9 25.7±4.0 1.0±0.1 123.2±10.2
0.322 0.249 0.625 0.533
IP
CR
US
AN
0.397
DBP (mmHg)
78.3±7.7
80.9±9.3
0.201
80.5±9.1
76.8±6.1
0.135
80.0±9.1
78.3±5.8
0.535
MABP
93.6±10.0
96.3±10.6
0.270
96.0±10.9
91.7±7.0
0.138
95.5±11.1
93.3±5.6
0.493
Pulse pressure
46.1±12.9
46.3±13.5
0.934
46.6±13.3
44.6±12.5
0.583
46.5±13.6
44.9±10.7
0.693
Total cholesterol (mg/dl)
279.4±21.2
279.7±26.4
M
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR SBP (mmHg)
T
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR
0.956
282.5±25.0
269.8±17.4
0.067
281.0±25.3
272.5±15.6
0.247
C/C, (n=37) 60±9.7
C/T+T/T, (n=90) 58±10.9
pvalue 0.229
G/G, (n=95) 58±11.0
G/A+A/A, (n=32) 59±9.4
pvalue 0.815
C/C, (n=95) 58±11.2
C/T, (n=32) 59±8.7
pvalue 0.602
53±10.3
0.521
54±10.9
55±8.7
0.638
54±10.9
54±8.9
0.650
5±4.8 26.5±3.8 1.0±0.1 127.3±13.7 81.4±8.7 96.7±9.4 45.9±10.6
4±4.5 28.2±5.6 1.0±0.1 126.2±14.1 81.4±8.3 96.4±9.5 44.8±10.1
0.157 0.040 0.095 0.694 0.996 0.848 0.587
5±4.8 27.4±4.7 1.0±0.1 127.8±13.3 81.3±8.2 95.5±9.1 45.5±9.9
4±4.1 28.8±6.3 1.0±0.1 122.6±15.2 79.8±8.6 94.4±10.0 43.8±11.0
0.430 0.180 0.228 0.067 0.153 0.234 0.412
5±4.8 27.7±4.9 1.0±0.1 126.2±13.4 81.2±8.1 96.2±9.1 45.0±9.6
5±4.1 27.8±6.0 1.0±0.1 127.5±15.7 82.1±9.5 97.3±10.6 45.4±11.8
0.996 0.960 0.928 0.632 0.607 0.588 0.818
279.0±29.0
276.1±25.2
0.577
277.8±24.3
274.5±31.5
0.589
277.3±27.6
275.8±21.9
0.780
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR SBP (mmHg) DBP (mmHg) MABP Pulse pressure Total cholesterol (mg/dl)
55±10.8
AC
CE
Female
0.488
G1846A G/G, (n=152) 59±1.0
pvalue
ED
Male
C2850T C/T+T/T, (n=129) 59±1.0
PT
Total
C2850T C/C, (n=71) 60±1.1
BMI: body mass index; WHR: waist-to-hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; MABP: mean arterial blood pressure
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ACCEPTED MANUSCRIPT Table 2. Genotype, allele distributions and genetic models of C2850T, G1846A and C100T polymorphisms in patients and controls Patients (n=200/73/127)
Controls (n=200/85/115)
C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele
72 (36.0) 101 (50.5) 27 (13.5) 245 (61.0) 155 (39.0) 34 (46.6) 32 (43.8) 7 (9.6) 100 (68.5) 46 (31.5) 38 (29.9) 69 (54.3) 20 (15.8) 145 (57.1) 109 (42.9)
91 (45.5) 95 (47.5) 14 (7.0) 27 (69.0) 123 (31.0) 30 (35.3) 51 (60.0) 4 (4.7) 111 (65.3) 59 (34.7) 61 (53.0) 44 (38.3) 10 (8.7) 166 (72.2) 64 (27.8)
G/G G/A A/A G allele A allele G/G G/A A/A G allele A allele G/G G/A A/A G allele A allele
129 (64.5) 58 (29.0) 13 (6.5) 316 (79.0) 84 (21.0) 51 (64.5) 18 (29.0) 4 (6.5) 120 (82.2) 26 (17.8) 78 (61.4) 40 (31.5) 9 (7.1) 196 (77.2) 58 (22.8)
151 (75.5) 42 (21.0) 7 (3.5) 344 (86.0) 56 (14.0) 64 (75.5) 19 (21.0) 2 (3.50) 147 (86.5) 23 (13.5) 87 (75.7) 23 (20.0) 5 (4.3) 197 (85.7) 33 (14.3)
C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele
155 (77.5) 45 (22.5) 0 355 (89.0) 45 (11.0) 60 (82.2) 13 (17.8) 0 133 (91.1) 13 (8.9) 95 (74.8) 32 (25.2) 0 190 (89.0) 32 (11.0)
Genotypes
χ2
p-value
Dominant model
Co-dominant model
Recessive model
(CC vs. CT/TT) OR (95% CI) 1.48 (0.99-2.22) p-value=0.053
(CT vs.CC/TT) OR (95% CI) 1.47 (1.08-2.01) p-value=0.013
(TT vs.CC/CT) OR (95% CI) 2.07 (1.05-4.08) p-value=0.031
OR (95% CI) 0.63 (0.33-1.19) p-value=0.150
OR (95% CI) 0.84 (0.50-1.41) p-value=0.505
OR (95% CI) 2.15 (0.60-7.65) p-value=0.229
OR (95% CI) 2.01 (1.35-2.99) p-value=0.001
OR (95% CI) 1.96 (0.88-4.39) p-value=0.093
(GG vs.GA/AA) OR (95% CI) 1.70 (1.10-2.62) p-value=0.016
(GAvs.GG/AA) OR (95% CI) 1.55 (1.09-2.20) p-value=0.014
(AAvs.GG/AA) OR (95% CI) 1.92 (0.75-4.91) p-value=0.166
OR (95% CI) 1.31 (0.65-2.65) p-value=0.445
OR (95% CI) 1.34 (0.75-2.41) p-value=0.320
OR (95% CI) 2.41 (0.43-13.54) p-value=0.304
OR (95% CI) 1.95 (1.12-3.40) p-value=0.017
OR (95% CI) 1.65 (1.06-2.58) p-value=0.024
OR (95% CI) 1.68 (0.55-5.16) p-value=0.358
Female
0.021
4.53
0.104
0.232
0.629
13.65
0.001
11.32
0.001
IP
5.30
OR (95% CI) 2.65 (1.56-4.48) p-value=0.001
CR
Male
0.038
US
Total
6.52
T
C2850T
G1846A
Total
Male
Female
6.31
0.012
M
AN
0.048
1.26
0.533
0.79
0.373
5.64
0.060
5.15
0.023
ED
PT
Female
CE
Male
AC
Total
6.09
169 (84.5) 31 (15.5) 0 369 (92.0) 31 (8.0) 65 (76.5) 20 (23.5) 0 150 (88.2) 20 (11.8) 104 (90.4) 11 (9.6) 0 219 (95.2) 11 (4.8)
C100T 2.74
0.098
2.47
0.117
0.47
0.493
2.47
0.117
9.05
0.003
11.08
0.001
OR: odds ratio; CI: confidence interval 16
(CC vs. CT) OR (95% CI) 1.58 (0.96-2.63) p-value=0.076 OR (95% CI) 0.70 (0.32-1.54) p-value=0.379
OR (95% CI) 3.18 (1.52-6.67) p-value=0.002
ACCEPTED MANUSCRIPT Table 3. Crude and adjusted odds ratios for C2850T, G1846A and C100T polymorphisms in patients and controls Patients (n=200/73/127)
Genotypes
Controls (n=200/85/115)
Association test Odds ratio (95% CI)
p-value
Association test *Odds ratio (95% CI)
p-value
91 (45.5)
Reference
Total
C/T
101 (50.5)
95 (47.5)
1.34 (0.89-2.04)
0.165
2.47 (1.05-5.77)
0.037
27 (13.5) 34 (46.6) 32 (43.8)
14 (7.0) 30 (35.3) 51 (60.0)
2.44 (1.19-4.99) Reference 0.55 (0.29-1.07)
0.015
4.46 (0.82-24.37)
0.085
Male
T/T C/C C/T
0.079
0.946
T/T
7 (9.6)
4 (4.7)
1.54 (0.41-5.79)
IP
1.04 (0.29-3.66)
0.520
3.34 (0.02-636.13)
0.652
C/C
38 (29.9)
61 (53.0)
Reference
C/T
69 (54.3)
44 (38.3)
2.52 (1.45-4.38)
0.001
5.10 (1.50-17.28)
0.009
T/T
20 (15.7)
10 (8.7)
3.21 (1.36-7.59)
0.008
6.16 (0.90-42.16)
0.064
G1846A Reference 1.62 (1.02-2.56)
0.041
Reference 0.67 (0.27-1.65)
0.381
0.109
1.04 (0.12-8.81)
0.971
0.648 0.299
1.63 (0.36-7.32) 2.01 (0.03-146.58)
0.522 0.750
0.030 0.229
0.38 (0.12-1.24) 0.77 (0.07-9.01)
0.108 0.838
Total
Male
Female
7 (3.5) 64 (75.5) 19 (21.0) 2 (3.5) 87 (75.7) 23 (20.0) 5 (4.3)
C/C
155 (77.5)
C/T T/T C/C C/T T/T C/C C/T T/T
45 (22.5) 0 60 (82.2) 13 (17.8) 0 95 (74.8) 32 (25.2) 0
AN
13 (6.5) 51 (64.5) 18 (29.0) 4 (6.5) 78 (61.4) 40 (31.5) 9 (7.1)
2.17 (0.84-5.61) Reference 1.19 (0.57-2.49) 2.51 (0.44-14.25) Reference 1.94 (1.07-3.52) 2.01 (0.64-6.24)
M
A/A G/G G/A A/A G/G G/A A/A
ED
Female
151 (75.5) 42 (21.0)
PT
Male
129 (64.5) 58 (29.0)
C100T
169 (84.5)
Reference
31 (15.5) 0 65 (76.5) 20 (23.5) 0 104 (90.4) 11 (9.6) 0
1.58 (0.95-2.63) Reference 0.70 (0.32-1.54)
CE
Total
G/G G/A
AC
Female
Reference
T
72 (36.0)
US
C/C
CR
C2850T
Reference 3.18 (1.52-6.67)
Reference 0.076 -
1.95 (0.72-5.31) -
0.188
0.379
1.29 (0.27-6.11)
0.751
0.002
2.78 (0.69-11.18)
0.150
CI: confidence interval. *Adjusted for age, gender, body mass index, alcohol, total cholesterol, hypertension and family history
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ACCEPTED MANUSCRIPT Table 4. Multifactor dimensionality reduction method for C2850T, G1846A and C100T variants and CAD risk TBA
Total
CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.557 0.578 0.589
0.515 0.56 0.525
7/10 10/10 10/10
0.016 0.000 0.000
Male
CYP2D6*2 (C2850T) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.581 0.590 0.604
0.581 0.561 0.528
10/10 8/10 10/10
0.043 0.027 0.012
Female
CYP2D6*2 (C2850T) CYP2D6*2 (C2850T), CYP2D6*10 (C100T) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.616 0.629 0.652
0.616 0.547 0.606
10/10 7/10 10/10
0.000 0.000 0.000
T
TrBA
IP
SNP interaction
CVC
p-value
CR
TrBA: training balance accuracy, TBA: testing balance accuracy, CVC: cross validation consistency, p-values obtained after 10,000
AC
CE
PT
ED
M
AN
US
permutations.
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ACCEPTED MANUSCRIPT Table 5. Haplotype analysis of C2850T, G1846A and C100T polymorphisms and CAD risk
M
AC
CE
PT
ED
OR: odds ratio; CI: confidence interval.
T
OR (95% CI) 1.34 (0.92-1.95) 0.99 (0.57-1.71) 2.61 (1.07-6.34) 1.19 (0.36-3.92) 15.22 (1.97-117.58) 1.89 (0.41-8.67)
US
CR
IP
0.93 (0.50-1.74) 1.17 (0.43-3.23) 1.15 (0.34-3.92) 0.88 (0.20-3.81) 1.57 (0.15-16.0)
AN
Haplotype C2850T G1846A C100T Frequency 1 T G C 0.294 2 C A C 0.101 3 C A T 0.040 4 C G T 0.029 Total 5 T A C 0.027 6 T G T 0.019 Global haplotype association 1 T G C 0.294 2 C A C 0.092 3 C A T 0.044 Male 4 C G T 0.042 5 T A C 0.020 Global haplotype association 1 T G C 0.293 2 C A C 0.105 Female 3 C A T 0.035 4 C G T 0.024 Global haplotype association
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1.64 (1.02-2.62) 0.96 (0.49-1.91) 4.21 (1.12-15.89) 1.71 (0.40-7.32)
p-value 0.130 0.970 0.035 0.770 0.009 0.410 0.002 0.280 0.760 0.820 0.860 0.710 0.720 0.042 0.920 0.035 0.470 0.001
ACCEPTED MANUSCRIPT Abbreviations
AC
CE
PT
ED
M
AN
US
CR
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µl: Microlitre A: Adenine BMI: Body mass index bp: Basepair C: Cytosine CAD: Coronary artery disease CI: Confidence intervals CNS: Central nervous system CVC: Cross validation consistency CYP2D6: Cytochrome P450 2D6 EDTA: Ethylenediaminetetra acetic acid EM: Extensive metabolizer G: Guanine IM: Intermediate metabolizer LD: Linkage disequilibrium MDR: Multifactor dimensionality reduction min: Minute mM: Millimolar OR: Odds ratio p: Significance value PCR-RFLP: Polymerase chain reaction-restriction fragment length polymorphism PM: Poor metabolizer SNP: Single nucleotide polymorphism SPSS: Statistical Package for Social Sciences T: Thiamine UEM: Ultra-extensive metabolizer
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Highlights
“200 cases and 200 controls from North India”
The 2850T and1846A variants were significantly associated with the increased risk of
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developing CAD, as observed from the odds ratios for the 2850 T/T and 1846 G/A
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genotypes (OR: 2.44, 95% CI: 1.99-4.99, p=0.015 and OR: 1.62, 95% CI: 1.02-2.56, p=0.041, respectively).
The recessive model in C2850T and the dominant model in G1846A are the best fit
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inheritance models to predict the susceptible gene effects (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031 and OR: 1.70, 95% CI: 1.10-2.62, p=0.016, respectively). Multifactor dimensionality reduction (MDR) analysis showed that the C2850T and
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G1846A polymorphisms formed a significant model in predicting the CAD risk. Haplotypes CAT and TAC conferred 2.6-and 15.2-fold increased risk for CAD,
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respectively.
A gender-specific association between CYP2D6 polymorphisms and CAD was also observed.
The C/T and T/T genotypes of C2850T, G/A genotype of G1846A and C/T genotype of
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C100T showed significant association with CAD in females (OR: 2.52, 95% CI: 1.424.38, p=0.001; OR: 3.2, 95% CI: 1.36-7.59, p=0.008; OR: 1.94, 95% CI: 1.07-3.52, p=0.030; OR: 3.18, 95% CI: 1.52-6.67, p=0.002, respectively). Haplotypes TGC and CAT conferred 1.6-and 4.2-fold increased risk for CAD in females,
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respectively.
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Figure 1
Figure 2
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M.A. Bhat, G. Gandhi PII: DOI: Reference:
S0378-1119(18)30369-X doi:10.1016/j.gene.2018.04.008 GENE 42732
To appear in:
Gene
Received date: Revised date: Accepted date:
28 October 2017 23 March 2018 4 April 2018
Please cite this article as: M.A. Bhat, G. Gandhi , CYP2D6 (C2850T, G1846A, C100T) polymorphisms, haplotypes and MDR analysis in predicting coronary artery disease risk in north-west Indian population: A case-control study. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2018.04.008
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ACCEPTED MANUSCRIPT CYP2D6 (C2850T, G1846A, C100T) Polymorphisms, Haplotypes and MDR Analysis in Predicting Coronary Artery Disease Risk in North-West Indian Population: A CaseControl Study
Bhat M. A* and Gandhi. G
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Short title: Association of CYP2D6 Polymorphisms with CAD
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Department of Human Genetics, Guru Nanak Dev University, Amritsar-143005, Punjab, India
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*Corresponding author:
Dr. Mohd. Akbar Bhat Presently at Multidisciplinary Research Unit Government Medical College, Amritsar-143001, Punjab, India Tel: +91-8725905591 E-mail: [email protected]
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ACCEPTED MANUSCRIPT ABSTRACT Aims: The present study was aimed to evaluate the association of C2850T, G1846A and C100T polymorphisms of the CYP2D6 with coronary artery disease (CAD) in North-West Indian population. Methods: In this case-control study, 200 patients with CAD and 200 age-, genderand ethnicity-matched healthy controls were genotyped for C2850T, G1846A and C100T polymorphisms of CYP2D6 using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: Genotype and allele distributions of C2850T and
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G1846A polymorphisms of the CYP2D6 were significantly different between cases and controls
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(p=0.038, p=0.021; p=0.048, p=0.012, respectively) whereas the distribution of genotype and
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allele for C100T polymorphism did not differ significantly (p=0.098, p=0.117, respectively). The 2850T and1846A variants were significantly associated with the increased risk of developing CAD, as observed from the odds ratios for the 2850 T/T and 1846 G/A genotypes (OR: 2.44,
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95% CI: 1.99-4.99, p=0.015 and OR: 1.62, 95% CI: 1.02-2.56, p=0.041, respectively). Moreover, the recessive model in C2850T and the dominant model in G1846A are the best fit inheritance
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models to predict the susceptible gene effects (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031 and OR: 1.70, 95% CI: 1.10-2.62, p=0.016, respectively). On gender stratification, these associations were
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observed only in females in addition to the C/T genotype of C2850T (OR: 2.52, 95% CI: 1.424.38, p=0.001) and C100T (OR: 3.18, 95% CI: 1.52-6.67, p=0.002). Furthermore, it is also
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observed that the CAT (OR: 2. 61, 95% CI: 1.07-6.34, p=0.035) and TAC (OR: 15. 22, 95% CI: 1.97-117.58, p=0.009) are high-risk haplotypes for CAD in the total group, whereas, the TGC
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(OR: 1. 64, 95% CI: 1.02-2.62, p=0.042) and CAT (OR: 4. 21, 95% CI: 1.12-15.59, p=0.035) haplotypes provide gender-specific risk in females. Conclusions: Our results indicate significant
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association of C2850T, G1846A and C100T polymorphisms of CYP2D6 with CAD especially in females of North-West Indian population.
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Key words: SNP-SNP interaction, Haplotype, CYP2D6, Polymorphism
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ACCEPTED MANUSCRIPT Introduction Coronary artery disease (CAD) is a complex heterogeneous disorder and it has become a major public health problem associated with life-style factors in both developed and developing countries like India, influenced by both genetic and environmental determinants (Pranavchand and Readdy, 2013; Liu et al., 2014). The genetic background of CAD is thought to be complex
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involving many genes encoding the activity of enzymes, responsible for metabolism of both
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exogenous and endogenous substances. Genetic polymorphisms in such genes can result in
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decreased or complete loss of enzyme activity which in turn are unable to detoxify these exogenous and endogenously produced substances, thereby increasing the risk for developing
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CAD. Cytochrome P450 2D6 (CYP2D6) enzyme plays an instrumental role in oxidation of xenobiotics including drugs and inactivation of endogenously produced reactive species and
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polymorphisms in CYP2D6 lead to inter-individual differences in enzymatic activity and have been linked to susceptibility to CAD (Teh et al., 2004).
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CYP2D6 is the only functional gene with 9 exons in the CYP2D6 sub-family located on chromosome 22q13.1 (Sheng et al., 2007). It is predominantly expressed in the liver, brain and
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heart where it is involved in the metabolism of exogenous as well as endogenously-produced substances and neurotransmitters responsible for maintaining homeostasis. CYP2D6 is highly
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polymorphic with inter-ethnic variability and more than 135 different allelic variants have been described (www.cypalleles.ki.se/cyp2d6.htm). These polymorphic variants result from point
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mutations, gene rearrangements, deletions or duplications in the entire gene and the action of these allelic variants causes normal, increased, decreased or complete loss of enzyme activity
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(Kitada, 2003). The presence of various combinations (Kurose et al., 2012) of these CYP2D6 alleles in an individual classifies the person as a poor metabolizer (PM), intermediate metabolizer (IM), extensive metabolizer (EM) or ultra-extensive metabolizer (UEM). Interindividual variation in the expression of CYP2D6 may alter susceptibility to disease induced by its substrates. The PM individuals metabolize drugs at slower rate causing accumulation of unmetabolized drugs thereby increasing the risk of drug toxicity and side-effects and will be therefore at higher risk to develop disease (Zanger et al., 2004). It is known that the CYP2D6 enzyme is involved in the metabolism of various drugs, including those for cardiovascular and
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ACCEPTED MANUSCRIPT central nervous system (CNS) conditions besides its implication in some CNS diseases viz. Parkinson’s disease, Lewy body dementia and Alzheimer’s disease (Barbeau et al., 1995; McCann et al., 1997; Tanaka et al., 1998). This raises an important hypothesis that the enzyme may also have a role in the development of CAD. It may act on some endogenous substances that predispose individuals to CAD or alternatively on some environmental substances that may contribute to the development of the disease.
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To the existing knowledge, no attempt has been made regarding the association of C2850T,
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G1846A and C100T polymorphisms of CYP2D6 with the risk of CAD in the North-West Indian
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population. In literature, only one cohort study has come to attention that suggested the role of G1846A and C100T polymorphisms of CYP2D6 with cardiovascular disease in Malaysian population (Teh et al., 2004), no report is available on the association of C2850T polymorphism
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of CYP2D6 with CAD. In view of the increased incidence of CAD and paucity of data available on the association between CYP2D6 genotypes and susceptibility to CAD in North-West Indian
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population, we aimed to investigate the association of C2850T, G1846A and C100T polymorphisms of CYP2D6 gene with CAD in the proposed study population..
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Materials and Methods
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Study population
In the present case-control study, 200 patients diagnosed with CAD were enrolled from local
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hospitals (A. P. Heart-Care Hospital and Mata Kaulan Ji Bandi Chod Charitable Hospital, Amritsar) and 200 age-, gender-and ethnicity-matched healthy controls with no present or past
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family history of CAD or any other disease were enrolled from general population of Punjab (a North Indian state). Smoking habits were absent in this study group due to religious restrains
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whereas alcohol consumption was present in both, cases and controls. Family history of CAD was also recorded. Total cholesterol was measured by semi-automated clinical chemistry analyser (Erba Chem-7) using commercially available kits (Erba). The details of sample collection, inclusion and exclusion criteria of cases and controls have been described in a previous paper (Bhat and Gandhi, 2016) and of on-going treatment in Bhat and Gandhi (2017). Patients were diagnosed by the cardiologists as per electrographic (ECG) findings, echocardiography, performance on the treadmill test and serum creatine phosphokinase (CPK) levels. Prescribed medication-combinations initiated on disease-diagnosis (4.03±0.32 y) included
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ACCEPTED MANUSCRIPT Metoprolol (a beta-blocker), Aspirin and Ecosprin (anti-platelet), Ramipril (Ace-inhibitor) and Nitrocontin (nitrate). The study was approved by the Institutional Ethics Committee of Guru Nanak Dev University, Amritsar and the study protocol complies with the guidelines of the Declaration of Helsinki. After obtaining the written informed voluntary consent, demographic and disease-specific information such as age, age-of-onset-of-disease, duration-of-disease, gender, height, weight, body mass index, waist-and hip-circumference and waist-hip-ratio of each participant was documented on a pre-designed questionnaire. Blood pressure of each
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individual was measured after a 5-min rest in the sitting position using a standard mercury
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sphygmomanometer as per the recommendations of American Heart Association (1981). The
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average of three subsequent measurements was used as blood pressure values. Blood samples from all the study participants were withdrawn by a trained phlebotomist and collected into tubes
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containing EDTA as anticoagulant.
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Genotyping of CYP2D6 polymorphisms
Genomic DNA was extracted from blood samples using the salting-out method (Miller et al.,
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1988). The C2850T, G1846A and C100T polymorphisms of the CYP2D6 were analyzed by polymerase chain reaction-restriction fragment length polymorphism technique (Theophilus et
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al., 2006) using primer sequences
obtained from
literature.
All polymerase chain
reactions were in a volume of 15 μl and contained 50 ng target DNA, 1.5 mM MgCl2, 0.2 mM of
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each dNTPs, 10 μM of each primer and 1U Taq DNA polymerase. The reactions were performed in Mastercycler gradient thermal cycler (Eppendorf, Hamburg, Germany) with an initial
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denaturation at 94°C for 5 min followed by 35 cycles of denaturation at 94°C for 45 sec, annealing at 69°C for 45 sec, elongation at 72°C for 45 sec for C2850T polymorphism, 35 cycles
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of denaturation at 94°C for 30 sec, annealing at 65°C for 45 sec, elongation at 72°C for 45 sec for G1846A polymorphism and 35 cycles of denaturation at 94°C for 45 sec, annealing at 65°C for 45 sec, elongation at 72°C for 45 sec for C100T polymorphism and a final extension at 72°C for 10 min. A negative control (PCR mixture without DNA template) was also run with each reaction to check for any contamination. The amplified products of C2850T (1029 bp), G1846A (309 bp) and C100T (345 bp) were observed on 1% agarose gel stained with ethidium bromide and then digested with Hha1, BstN1 and Hph1 restriction enzymes (New England Biolabs, USA), respectively. The digested products were resolved on ethidium bromide stained 2.5% agarose 5
ACCEPTED MANUSCRIPT gel. Genotyping of the C2850T, G1846A and C100T polymorphisms are described in Figures. 1a-c. For genotyping accuracy, samples with known genotypes were used in each batch as positive controls to evaluate the completeness of PCR product digestion. To rule out genotypic error, 10% random samples of known genotypes of each polymorphism with exploratory bidirectional DNA sequencing of few samples was done and the results were 100% concordant. Statistical analysis
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All statistical analysis was performed using the Statistical Package for Social Sciences (SPSS
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version 16.0 for windows) software. Continuous variables have been expressed as mean ±
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standard deviation and categorical variables, as counts and percentages. Differences between the means of continuous variables were evaluated by Student’s t-test. Differences in categorical variables and genotype distribution were tested by Chi-squared test with Yates correction or
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Fisher’s exact test. The association between the C2850T, G1846A and C100T genotypes and CAD risk was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CI)
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using binary logistic regression analysis, adjusting for potential risk factors such as age, gender, BMI, alcohol, hypertension, total cholesterol and family history. The disease risk was also
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evaluated under different genetic models (dominant, co-dominant and recessive) using WebAssotest program (http://www.ekstroem.com). Multifactor dimensionality reduction (MDR)
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analysis was carried out to evaluate gene-gene interaction (Ritchie et al., 2003). Linkage disequilibrium (LD), SNP haplotype analysis and the deviation from Hardy-Weinberg
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equilibrium was done by SNPStats software (Sole et al., 2006). The corrections for multiple comparisons were done by Bonferroni method wherever required and there was no difference in
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statistical significance even after Bonferroni correction (i.e. reducing significance level to p= 0.017). Power calculations were performed using CaTS-Power Calculator (Skol et al., 2006) and
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the present study with a total sample size of 400 participants had a statistical power of 85% at significance level of 0.05 to detect an association with an odds ratio of 1.5. A p-value <0.05 (two-tailed) was considered to be statistically significant. Results The demographic, anthropometric and disease-specific characteristics of the study subjects have been previously described in Bhat and Gandhi (2016). Disease-specific characteristics of patients stratified by genotypes of C2850T, G1846A and C100T polymorphisms are presented in Table 1. Significant mean differences were observed for BMI (p=0.008), being higher in CAD patients 6
ACCEPTED MANUSCRIPT carrying CT+TT genotype and retained only in females (p=0.040) after gender analysis. However, no significant differences were found for disease-specific characteristics for different genotypes in G1846A and C100T. The genotype and allele frequency distribution for C2850T, G1846A and C100T polymorphisms are presented in Table 2. The distribution of genotype and allele frequencies in both cases and controls were in agreement with those predicted by Hardy-Weinberg equilibrium (p>0.05) for all
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the three SNPs. In C2850T polymorphism, the genotype and allele distribution between cases
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and controls was statistically significant in the total group and in females (p=0.038, p=0.021;
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p=0.001, p=0.001, respectively) but non-significant in males (p=0.104, p=0.629, respectively). Binary logistic regression analysis (Table 3) revealed that the T/T genotype was significantly associated with an almost 2.5-fold increased risk for CAD (OR: 2.44, 95% CI: 1.99-4.99,
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p=0.015). However after adjustment for risk factors of age, gender, BMI, alcohol, total cholesterol, hypertension and family history, the T/T genotype showed increased odds ratio,
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although not statistically significant (OR: 4.46, 95% CI: 0.82-24.37, p=0.085) while the C/T genotype independently conferred ~2.5-fold risk for CAD (OR: 2.47, 95% CI: 1.05-5.77,
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p=0.037). In addition, the CT genotype in co-dominant model (OR: 1.47, 95% CI: 1.08-2.01, p=0.013) and the TT genotype (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031) in recessive model have
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also shown significant association with increased risk of developing CAD (Table 2). On stratification for gender, the C/T (OR: 2. 52, 95% CI: 1.42-4.38, p=0.001) and T/T (OR: 3.2,
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95% CI: 1.36-7.59, p=0.008) genotypes showed 2.5-and 3.2-fold increased risk, respectively for CAD in females but not in males. On adjustment (Table 3), the C/T genotype independently
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conferred a five-fold risk for CAD (OR: 5. 10, 95% CI: 1.50-17.28, p=0.009) while the T/T genotype had an increased odds ratio, though not reaching statistical significance (OR: 6. 16,
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95% CI: 0.90-42.16, p=0.064). Genetic models (Table 2) have revealed that the minor T allele in the dominant model and CT genotype in the co-dominant model exhibit increased risk for CAD in females (OR: 2. 65, 95% CI: 1.56-4.48, p=0.001; OR: 2. 01, 95% CI: 1.35-2.99, p=0.001, respectively). For the G1846A polymorphism (Table 3), the genotype (p=0.048) and allele (p=0.012) frequencies were significantly different between cases and controls in total group but with only allelic difference in females (p=0.023) which was non-significant for both frequencies in males (p=0.533, p=0.117, respectively). The G/A genotype conferred 1.6-fold increased risk for CAD 7
ACCEPTED MANUSCRIPT (OR: 1.62, 95% CI: 1.02-2.56, p=0.041) which was however lost after adjustment (OR: 0.67, 95% CI: 0.27-1.65, p=0.381). The minor A allele in the dominant model (OR: 1.70, 95% CI: 1.10-2.62, p=0.016) and the AG genotype (OR: 1.55, 95% CI: 1.09-2.20, p=0.014) in the codominant model (Table 2) also conferred increased risk for CAD. On gender stratification, the G/A genotype (Table 3) conferred 1.9-fold increased risk for CAD in females (OR: 1.94, 95% CI: 1.07-3.52, p=0.030) which on adjustment was lost (OR: 0.38, 95% CI: 0.12-1.24, p=0.381). Among the genetic models, the minor A in the dominant model (OR: 1.95, 95% CI: 1.12-3.40,
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showed significant association with CAD in females (Table 2).
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p=0.017) and G/A genotype in the co-dominant model (OR: 1.65, 95% CI: 1.06-2.58, p=0.024)
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In the case of C100T polymorphism, the genotype (p=0.098) and allele (p=0.117) frequencies did not differ significantly between cases and controls. However on stratification by gender, a
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significant difference was observed for the genotype (p=0.003) and allele (p=0.001) frequency distribution only in females and not in males (p=0.493, p=0.117, respectively). The C/T
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genotype (Table 3) and dominant model (Table 2) increased 3.2-fold risk for CAD (OR: 3.18, 95% CI: 1.52-6.67, p=0.002) in females, which however was not retained after adjustment (OR:
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2.78, 95% CI: 0.69-11.18, p=0.150).
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The Multifactor Dimensionality Reduction (MDR) analysis was used to analyze the possible interactions between the C2850T, G1846A and C100T SNPs for CAD (Table 4). The 2-loci
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interaction model of C2850T and G1846A emerged as the best predictor of CAD potential based on the cross validation consistency (CVC) of 10/10, with the highest testing balance accuracy of
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56% and p<0.001. The entropy interaction graphical model (Figure 2a) revealed that C2850T and G1846A have significant synergistic interaction (0.34%) sharing the positive information
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gain with respect to CAD, whereas separately, there was contribution of both, the C2850T (1.19%) and G1846A (1.1%) towards CAD susceptibility (Table 5). The one-locus model of C2850T was in fact the best predictor of CAD potential, both in males (Figure 2b) with TBA 58% (CVC=10/10, p=0.043) and in females (Figure 2c) with TBA 62% (10/10, p<0.001), although prediction was higher for females. The selected SNPs of CYP2D6 were neither in LD in the total group nor after gender stratification even though they are located in the same gene (Figure 3a-3c). There was nonetheless significant association of global haplotypes with CAD in the total group (p=0.002) as well as in the females (p=0.001). In the total group, the observed haplotypes, CAT (OR: 2.61, 8
ACCEPTED MANUSCRIPT 95% CI: 1.07-6.34, p=0.035) and TAC (OR: 15.22, 95% CI: 1.97-117.58, p=0.009) conferred 2.6-and 15.2-fold increased risk for CAD. Furthermore after stratification, in females (Table 5), the TGC (OR: 1.64, 95% CI: 1.02-2.62, p=0.042) and CAT (OR: 4.21, 95% CI: 1.12-15.59, p=0.035) haplotypes showed 1.6-and 4.2-fold increased risk for CAD, respectively. Discussion In the present study, the association of C2850T, G1846A and C100T polymorphisms of the
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CYP2D6 with CAD in North-West Indian population was investigated. In literature, numerous
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reports have documented the role of CYP2D6 polymorphisms in predisposing to several kinds of
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tumors (Sobti et al., 2005; Surekha et al., 2010; Levkovich et al., 2011; Shukla et al., 2012) and central nervous system disorders Parkinson’s or Alzheimer’s diseases (Smith et al., 1992; Lu et
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al., 2014) in different ethnic groups. A single report has come to attention on the role of CYP2D6 polymorphisms in cardiovascular diseases but not from the North-West Indian population. This
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therefore is the first study of its kind investigating the association of C2850T, G1846A and C100T polymorphisms of CYP2D6 in North-West Indian CAD patients. In the present study, the
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frequency of homozygous T/T genotype of C2850T was found to be significantly higher in CAD patients compared to controls (13.5% vs. 7.0%). The T/T genotype conferred ~2.5-fold increased
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risk for CAD development, which is also supported by the recessive model. When stratified by gender, it was observed in female patients with the C/T genotype, there was a 2.5-fold and with
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the T/T genotype, there was 3.2-fold increased risk for developing CAD, suggestive of an association of the dominant and co-dominant models. This observation as a first study therefore
reported.
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exhibits an association of C2850T polymorphism with CAD as no studies on this SNP have been
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For G1846A, the frequency of heterozygous G/A genotype was observed to be significantly higher in CAD patients (29.0%) than in controls (21.0%). The G/A genotype provided 1.6-fold increased risk for developing CAD in the total group and 1.9-fold increased risk for CAD female patients, as also evidenced by the dominant and co-dominant models. Conversely, no significant association of C100T polymorphism with CAD was observed in the total group while there was a 3.2-fold increased risk in CAD females for the C/T genotype, indication of dominant mode of inheritance. For this SNP also, case-control studies are lacking in literature except for the work of Teh et al. (2004) in a Malaysian cohort reporting the higher frequency of both G1846A and 9
ACCEPTED MANUSCRIPT C100T SNPs in cardiovascular disease patients, suggested the role of CYP2D6 genotypes in cardiovascular disease. The potential explanations of these discrepancies in different populations might be attributed through differences in ethnic background, variations in the population structure, diverse genetic and environmental backgrounds, varied sample sizes and different study designs. SNP-SNP interaction was examined by MDR analysis because of the limitation of logistic
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regression analysis for modeling multifactor interactions. The MDR analysis showed that the
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C2850T alone does not play any significant role in increasing disease risk with the C2850T and
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G1846A interaction conferring increased risk for CAD. The observations on MDR and logistic regression analysis of the C2850T and G1846A polymorphisms exhibit both, there association and interaction for increased disease-risk. As haplotype analysis is more informative for
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investigating the genetic influence on disease manifestation compared to the effect of individual genotypes, haplotypes were constructed and analyzed for the possible association with CAD. The
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haplotype analysis inferred that CAT and TAC haplotypes have overall risk for CAD patients and the TGC and CAT haplotypes confer gender-specific risk in women for developing CAD in
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the studied North Indian group.
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Therefore the present study after association and haplotype analysis and SNP-SNP interaction, has revealed both, an individual and/or cumulative effect of C2850T and G1846A
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polymorphisms of CYP2D6 on the development of CAD. Stratification by gender further revealed the association of C100T and of C2850T and G1846A with disease risk and show cases
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that gender-stratification can provide more insights for disease association in male and female patients and the present findings are indicative that genetic polymorphisms of CYP2D6 may
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exert a greater influence on CAD in females than in males. Indeed, epidemiological data have shown that the risk of CAD differs among males and females because of hormonal differences. Coban et al. (2015) also observed gender differences while studying the relationship between (CYP19A1) polymorphism and hypertension in a Turkish population. As a first study of its kind, the results of the present study can be used as a baseline data for association of CYP2D6 polymorphisms with CAD. This can enable the construction of a panel of candidate genes for identifying the susceptible genotypes responsible for CAD at an earlier stage, so that the preventive measures and life-style modifications can be initiated to prevent disease progression.
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ACCEPTED MANUSCRIPT A number of limitations in the present study include the smaller sample size wherein a larger sample size could have provided more insights for CAD risk. Moreover analysis of other SNPs of CYP2D6 and functional studies are needed for elucidation of CYP2D6 in CAD. The lack of similar studies in literature especially from this region of India has not allowed for comparisons. Nevertheless the study has relevance as a first study to report the association of CYP2D6 polymorphisms with CAD. In conclusion, the present results showed that C2850T, G1846A and C100T polymorphisms of
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CYP2D6 are strong pre-disposing genetic variants for CAD, predominantly in women of North-
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West Indian Punjabi population. Extrapolation of other conclusions from the present study is
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limited and requires elucidation in large ethnic-specific and case-control studies to understand the role of CYP2D6 gene polymorphisms in CAD susceptibility.
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Acknowledgments
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We are thankful to Dr. Ajinder Pal Singh (A. P. Heart-Care Hospital) and Dr. Sudhir Abrol (Mata Kaulan Ji Bandi Chod Charitable Hospital) Amritsar for providing contact with CAD patients and their clinical information. We would also like to thank our study participants for
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providing their valuable information and blood samples.
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Funding/Support: This work was supported from the UGC DRS-SAP grant. Mohd. Akbar Bhat is grateful to Guru Nanak Dev University for providing the University with Potential for
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Excellence Fellowship for his Ph.D. programme.
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References
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Conflict of interest: The authors declare that there is no conflict of interest.
American Heart Association (1981). Report of subcommittee of post graduate education committee recommendation for human blood pressure determination of sphygmomanometer. Circulation 64: 510A-599B. Barbeau, A., Cloutier, T., Roy, M., Plasse, L., Paris, S. and Poirier, J. (1985). Ecogenetics of Parkinson’s disease 4-hydroxylation of debrisoquine. Lancet 2:1213-1216. Bhat M.A. and Gandhi G. (2016). Association of GSTT1 and GSTM1 gene polymorphisms with coronary artery disease in North Indian Punjabi population: a casecontrol study. Postgraduate Medical Journal 92:701-706.
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Bhat, M.A. and Gandhi, G. (2017). Assessment of DNA Damage in Leukocytes of Patients with Coronary Artery Disease by Comet Assay. International Heart Journal. 58: 1-4. Coban, N., Onat, A., Guclu-Geyik, F., Can, G. And Erginel-Unaltuna, N. (2015). Sexand obesity-specific association of aromatase (CYP19A1) gene variant with apolipoprotein B and hypertension. Archives of Medical Research 46: 564-571. Kitada, M. (2003). Genetic polymorphism of cytochrome P450 enzymes in Asian population: focus on CYP2D6. International Journal of Clinical Pharmacology 23: 3135. Kurose, K., Sugiyama, E. and Saito, Y. (2012). Population differences in major fuctional polymorphisms of Pharmacokinetics/ pharmacodynamics-related genes in Eastern Asians and Europeans: Implications in the clinical trials for novel drug development. Journal of Drug Metabolism and Pharmacokinetics 27: 9-54. Levkovich, N. N., Gorovenko, N. G. and Myasoedov, D. V. (2011). Association of polymorphic G1934a variant (Allele *4) Of Cyp2d6 Gene with increased risk of breast cancer development in Ukrainian women. Experimental Oncology 33: 136-139. Liu, D., Jiang, Z., Dai, L., Zhang, X., Yan, C. and Han, Y. (2014). Association between the-786 TfiC1 polymorphism in the promoter region of endothelial nitric oxide synthase (eNOS) and risk of coronary artery disease: a system at review and meta-analysis. Gene 545: 175-183. Lu, Y., Qin, X., Li, S., Zhang , X., He, Y., Peng ,Q., Deng, Y., Wang, J., Xie, L., Li, T. and Zeng, Z. (2014). Quantitative assessment of CYP2D6 polymorphisms and risk of Alzheimer's disease: a meta-analysis. Journal of the Neurological Sciences 343: 15-22. McCann, S.J., Pond, S.M., James, K.M. and Le Couteur, D. G. (1997). The association between polymorphismsin the cytochrome P-450 2D6 gene and Parkinson’s disease: A case-control study and meta-analysis. Journal of Neurological Sciences 153:50-53. Miller, S.A., Dykes, D.D. and Polesky, H.F. (1988). A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acid Research 16: 1215. Pranavchand, R. and Reddy, B.M. (2013). Current status of understanding of the genetic etiology of coronary heart disease. Journal of Postgraduate Medicine 59: 30-41. Ritcchie, M. D., Hahn, L. W. and Moore, J. H. (2003). Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogenecity. Genetic Epidemiology 24: 150-157. Sheng, H. H., Zeng, A. P., Zhu, W. X., Zhu, R. F., Li, H. M., Zhu, Z. D., Qin, Y., Jin, W., Liu, Y., Du, Y. L., Sun, J. and Xiao, H. S. (2007). Allelic distributions of CYP2D6 gene copy number variation in the Eastern Han Chinese population. Acta Pharmacologica Sinica 28: 279-286. Shukla, P., Gupta, D., Pant, M. C. and Parmar, D. (2012). CYP2D6 polymorphism: A predictor of susceptibility and response to chemoradiotherapy in head and neck cancer. Journal of Cancer Research and Therapeutics 8: 40-45.
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Skol, A. D., Scott, L. J., Abecasis, G. R. and Boehnke, M. (2006). Joint analysis is more efficient than replication-based analysis for two stage genome wide association studies. Nature Genetics 38: 209-213. Smith, C. A., Gough, A. C., Leigh, P. N., Summers, B. A., Harding, A. E., Maranganore, D. M., Sturman, S. G., Schapira, A. H, Williams, A. C., Spurr, N.K. and Wolf, C.R. (1992). Debrisoquine hydroxylase gene polymorphism and susceptibility to Parkinson’s disease. Lancet 339: 1375-1377. Sobti, R. C., Al-Badrana, A. I., Sharma, S., Sharmab, S. K., Krishanc, A. and Mohand, H. (2005). Genetic polymorphisms of CYP2D6, GSTM1 and GSTT1 genes and bladder cancer risk in North India. Cancer Genetics and Cytogenetics 156: 68-73. Sole, X., Guino, E., Valls, J., Iniesta, R. and Moreno, V. (2006). SNPStats: a web tool for the analysis of association studies. Bioinformatics 22: 1928-1929. Surekha, D., Sailaja, K., Nageswara, R. D., Padma, T., Raghunadharao, D. and Vishnupriya, V. (2010). CYP2D6*4 polymorphisms and breast cancer risk. Biology and Medicine 2: 49-55. Tanaka, S., Chen, X., Xia, Y., Kang, D. E., Matoh, N., Sundsmo, M., Thomas, R.G., Katzman, R., Thal, L.J., Trojanowski, J.Q., Saitoh, T., Ueda, K. and Masliah, E. (1998). Association of CYP2D microsatellite polymorphism with Lewy body variant of Alzheimer’s disease. Neurology 50:1556-1562. Teh, L. K., Zilfalil, B. A., Marina, I., Rosemi, B. S. and Ismail, R. (2004). Genetic polymorphism of CYP2D6 in patients with cardiovascular disease-a cohort study. Journal of Clinical Pharmacy and Therapeutics 29: 559-564. Theophilus, N.A., Chanfrasekaran, A., Sam, S.S., Gerard, N. and Rajagopal, K. (2006). CYP2D6 genetic polymorphism in South Indian populations. Biological and Pharmaceutical Bulletin 29: 1655-1658. Zanger, U.M., Raimundo, S. and Eichelbaum, M. (2004). Cytochrome P450 2D6: overview and update on pharmacology, genetics, and biochemistry. NaunynSchmiedeberg's Archives of Pharmacology 369: 23-37.
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ACCEPTED MANUSCRIPT CAPTIONS TO THE FIGURES Figure 1a: Restriction band pattern of CYP2D6*2 (C2850T). Lane M: 100bp molecular weight marker; lanes 1, 4, 6, 8, 9: a homozygous wild type CC genotype; lanes 2, 5, 7, 10, 11: a heterozygous CT genotype; lane 3: homozygous mutant TT genotype; lane 12: undigested control
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Figure 1b: Restriction band pattern of CYP2D6*4 (G1846A). Lane M: 100bp molecular weight marker; lanes 3, 4, 7: a homozygous wild type GG genotype; lanes 1, 6: a heterozygous GA genotype; lanes 2, 5: a homozygous mutant (AA) genotype; lane 8: undigested control
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Figure 1c: Restriction band pattern of CYP2D6*10 (C100T). Lane M: 100bp molecular weight marker; lanes 1, 3, 4, 6, 8, 9: a homozygous wild type CC genotype; lanes 2, 5: a heterozygous CT genotype; lane 10: undigested control
M
AN
Figure 2: Interaction entropy model for C2850T, G1846A and C100T polymorphisms of CYP2D6 and CAD risk in total group (a), males (b) and females (c). This graphical model describes the percent entropy explained by each SNP. Positive percent entropy indicates synergy whereas the negative percent indicates redundancy. Orange line indicated positive interaction between CYP2 and CYP4, green and blue color indicated weak and no interactions.
AC
CE
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Figure 3: Pair-wise linkage disequilibrium plot of CYP2D6 (C2850T, G1846A and C100T) SNPs in the total study group (a), males (b) and females (c). The hatch marks contain D´values, multiplied by100 to indicate the strength of LD between SNPs.
14
ACCEPTED MANUSCRIPT Table1.Comparison of demographic and disease-specific characteristics according to the C2850T, G1846A and C100T polymorphisms of CYP2D6 Variables
G1846A G/A+A/A, (n=48) 59±1.2
0.902
C100T C/C, (n=155) 59±0.9
C100T C/T, (n=45) 60±1.4
pvalue
pvalue
55±1.2
55±1.0
0.939
55±1.0
55±1.2
0.863
55±0.9
56±1.5
0.662
5±0.6 26.2±0.5 1.0±0.0
4±0.4 28.2±0.5 1.0±0.0
0.156 0.008 0.928
4±0.4 27.3±0.4 1.0±0.01
4±0.5 27.7±0.7 1.0±0.0
0.397 0.631 0.914
4±0.4 27.6±0.4 1.0±0.1
5±0.6 27.2±0.8 1.0±0.1
0.477 0.635 0.553
SBP (mmHg)
125.9±1.8
126.5±1.32
0.781
126.8±1.3
125.4±1.8
0.540
126.3±1.2
126.3±2.1
0.996
DBP (mmHg) MABP Pulse pressure Total cholesterol (mg/dl)
79.9±0.97 95.2±1.1 46.0±1.4
81.3±0.76 96.4±0.9 45.2±1.0
0.255 0.421 0.628
81.0±0.8 96.3±0.9 45.7±1.0
80.4±1.0 95.4±1.2 45.0±1.3
0.627 0.550 0.654
80.7±0.7 95.9±0.8 45.5±0.9
80.0±1.3 96.1±1.4 45.3±1.7
0.846 0.908 0.889
279.2±25.4
277.2±25.5
0.600
279.5±24.6
272.9±27.5
0.145
278.8±26.7
274.9±20.2
0.366
C/C, (n=34) 59±8.8
C/T+T/T, (n=39) 61±12.0
pvalue 0.444
G/G, (n=57) 60±10.1
G/A+A/A, (n=16) 60±12.6
pvalue 0.975
C/C, (n=60) 60±10.7
C/T, (n=13) 63±10.1
pvalue 0.359
55±9.2
59±12.0
0.228
57±10.5
57±12.4
0.903
57±10.5
58±12.4
0.615
4±5.1 26.1±4.7 1.0±0.1 124.3±17.1
3±3.9 27.9±4.5 1.0±0.1 127.2±16.8
0.278 0.089 0.156 0.474
3±4.6 27.6±4.4 1.0±0.1 127.1±17.6
3±4.3 26..0±5.3 1.0±0.1 121.4±13.4
0.716 0.153 0.172 0.235
3±4.4 27.4±4.8 1.0±0.1 126.4±18.0
4±4.9 25.7±4.0 1.0±0.1 123.2±10.2
0.322 0.249 0.625 0.533
IP
CR
US
AN
0.397
DBP (mmHg)
78.3±7.7
80.9±9.3
0.201
80.5±9.1
76.8±6.1
0.135
80.0±9.1
78.3±5.8
0.535
MABP
93.6±10.0
96.3±10.6
0.270
96.0±10.9
91.7±7.0
0.138
95.5±11.1
93.3±5.6
0.493
Pulse pressure
46.1±12.9
46.3±13.5
0.934
46.6±13.3
44.6±12.5
0.583
46.5±13.6
44.9±10.7
0.693
Total cholesterol (mg/dl)
279.4±21.2
279.7±26.4
M
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR SBP (mmHg)
T
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR
0.956
282.5±25.0
269.8±17.4
0.067
281.0±25.3
272.5±15.6
0.247
C/C, (n=37) 60±9.7
C/T+T/T, (n=90) 58±10.9
pvalue 0.229
G/G, (n=95) 58±11.0
G/A+A/A, (n=32) 59±9.4
pvalue 0.815
C/C, (n=95) 58±11.2
C/T, (n=32) 59±8.7
pvalue 0.602
53±10.3
0.521
54±10.9
55±8.7
0.638
54±10.9
54±8.9
0.650
5±4.8 26.5±3.8 1.0±0.1 127.3±13.7 81.4±8.7 96.7±9.4 45.9±10.6
4±4.5 28.2±5.6 1.0±0.1 126.2±14.1 81.4±8.3 96.4±9.5 44.8±10.1
0.157 0.040 0.095 0.694 0.996 0.848 0.587
5±4.8 27.4±4.7 1.0±0.1 127.8±13.3 81.3±8.2 95.5±9.1 45.5±9.9
4±4.1 28.8±6.3 1.0±0.1 122.6±15.2 79.8±8.6 94.4±10.0 43.8±11.0
0.430 0.180 0.228 0.067 0.153 0.234 0.412
5±4.8 27.7±4.9 1.0±0.1 126.2±13.4 81.2±8.1 96.2±9.1 45.0±9.6
5±4.1 27.8±6.0 1.0±0.1 127.5±15.7 82.1±9.5 97.3±10.6 45.4±11.8
0.996 0.960 0.928 0.632 0.607 0.588 0.818
279.0±29.0
276.1±25.2
0.577
277.8±24.3
274.5±31.5
0.589
277.3±27.6
275.8±21.9
0.780
Age (years) Age-of-onset (years) Duration (years) BMI (kg/m2) WHR SBP (mmHg) DBP (mmHg) MABP Pulse pressure Total cholesterol (mg/dl)
55±10.8
AC
CE
Female
0.488
G1846A G/G, (n=152) 59±1.0
pvalue
ED
Male
C2850T C/T+T/T, (n=129) 59±1.0
PT
Total
C2850T C/C, (n=71) 60±1.1
BMI: body mass index; WHR: waist-to-hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; MABP: mean arterial blood pressure
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ACCEPTED MANUSCRIPT Table 2. Genotype, allele distributions and genetic models of C2850T, G1846A and C100T polymorphisms in patients and controls Patients (n=200/73/127)
Controls (n=200/85/115)
C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele
72 (36.0) 101 (50.5) 27 (13.5) 245 (61.0) 155 (39.0) 34 (46.6) 32 (43.8) 7 (9.6) 100 (68.5) 46 (31.5) 38 (29.9) 69 (54.3) 20 (15.8) 145 (57.1) 109 (42.9)
91 (45.5) 95 (47.5) 14 (7.0) 27 (69.0) 123 (31.0) 30 (35.3) 51 (60.0) 4 (4.7) 111 (65.3) 59 (34.7) 61 (53.0) 44 (38.3) 10 (8.7) 166 (72.2) 64 (27.8)
G/G G/A A/A G allele A allele G/G G/A A/A G allele A allele G/G G/A A/A G allele A allele
129 (64.5) 58 (29.0) 13 (6.5) 316 (79.0) 84 (21.0) 51 (64.5) 18 (29.0) 4 (6.5) 120 (82.2) 26 (17.8) 78 (61.4) 40 (31.5) 9 (7.1) 196 (77.2) 58 (22.8)
151 (75.5) 42 (21.0) 7 (3.5) 344 (86.0) 56 (14.0) 64 (75.5) 19 (21.0) 2 (3.50) 147 (86.5) 23 (13.5) 87 (75.7) 23 (20.0) 5 (4.3) 197 (85.7) 33 (14.3)
C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele C/C C/T T/T C allele T allele
155 (77.5) 45 (22.5) 0 355 (89.0) 45 (11.0) 60 (82.2) 13 (17.8) 0 133 (91.1) 13 (8.9) 95 (74.8) 32 (25.2) 0 190 (89.0) 32 (11.0)
Genotypes
χ2
p-value
Dominant model
Co-dominant model
Recessive model
(CC vs. CT/TT) OR (95% CI) 1.48 (0.99-2.22) p-value=0.053
(CT vs.CC/TT) OR (95% CI) 1.47 (1.08-2.01) p-value=0.013
(TT vs.CC/CT) OR (95% CI) 2.07 (1.05-4.08) p-value=0.031
OR (95% CI) 0.63 (0.33-1.19) p-value=0.150
OR (95% CI) 0.84 (0.50-1.41) p-value=0.505
OR (95% CI) 2.15 (0.60-7.65) p-value=0.229
OR (95% CI) 2.01 (1.35-2.99) p-value=0.001
OR (95% CI) 1.96 (0.88-4.39) p-value=0.093
(GG vs.GA/AA) OR (95% CI) 1.70 (1.10-2.62) p-value=0.016
(GAvs.GG/AA) OR (95% CI) 1.55 (1.09-2.20) p-value=0.014
(AAvs.GG/AA) OR (95% CI) 1.92 (0.75-4.91) p-value=0.166
OR (95% CI) 1.31 (0.65-2.65) p-value=0.445
OR (95% CI) 1.34 (0.75-2.41) p-value=0.320
OR (95% CI) 2.41 (0.43-13.54) p-value=0.304
OR (95% CI) 1.95 (1.12-3.40) p-value=0.017
OR (95% CI) 1.65 (1.06-2.58) p-value=0.024
OR (95% CI) 1.68 (0.55-5.16) p-value=0.358
Female
0.021
4.53
0.104
0.232
0.629
13.65
0.001
11.32
0.001
IP
5.30
OR (95% CI) 2.65 (1.56-4.48) p-value=0.001
CR
Male
0.038
US
Total
6.52
T
C2850T
G1846A
Total
Male
Female
6.31
0.012
M
AN
0.048
1.26
0.533
0.79
0.373
5.64
0.060
5.15
0.023
ED
PT
Female
CE
Male
AC
Total
6.09
169 (84.5) 31 (15.5) 0 369 (92.0) 31 (8.0) 65 (76.5) 20 (23.5) 0 150 (88.2) 20 (11.8) 104 (90.4) 11 (9.6) 0 219 (95.2) 11 (4.8)
C100T 2.74
0.098
2.47
0.117
0.47
0.493
2.47
0.117
9.05
0.003
11.08
0.001
OR: odds ratio; CI: confidence interval 16
(CC vs. CT) OR (95% CI) 1.58 (0.96-2.63) p-value=0.076 OR (95% CI) 0.70 (0.32-1.54) p-value=0.379
OR (95% CI) 3.18 (1.52-6.67) p-value=0.002
ACCEPTED MANUSCRIPT Table 3. Crude and adjusted odds ratios for C2850T, G1846A and C100T polymorphisms in patients and controls Patients (n=200/73/127)
Genotypes
Controls (n=200/85/115)
Association test Odds ratio (95% CI)
p-value
Association test *Odds ratio (95% CI)
p-value
91 (45.5)
Reference
Total
C/T
101 (50.5)
95 (47.5)
1.34 (0.89-2.04)
0.165
2.47 (1.05-5.77)
0.037
27 (13.5) 34 (46.6) 32 (43.8)
14 (7.0) 30 (35.3) 51 (60.0)
2.44 (1.19-4.99) Reference 0.55 (0.29-1.07)
0.015
4.46 (0.82-24.37)
0.085
Male
T/T C/C C/T
0.079
0.946
T/T
7 (9.6)
4 (4.7)
1.54 (0.41-5.79)
IP
1.04 (0.29-3.66)
0.520
3.34 (0.02-636.13)
0.652
C/C
38 (29.9)
61 (53.0)
Reference
C/T
69 (54.3)
44 (38.3)
2.52 (1.45-4.38)
0.001
5.10 (1.50-17.28)
0.009
T/T
20 (15.7)
10 (8.7)
3.21 (1.36-7.59)
0.008
6.16 (0.90-42.16)
0.064
G1846A Reference 1.62 (1.02-2.56)
0.041
Reference 0.67 (0.27-1.65)
0.381
0.109
1.04 (0.12-8.81)
0.971
0.648 0.299
1.63 (0.36-7.32) 2.01 (0.03-146.58)
0.522 0.750
0.030 0.229
0.38 (0.12-1.24) 0.77 (0.07-9.01)
0.108 0.838
Total
Male
Female
7 (3.5) 64 (75.5) 19 (21.0) 2 (3.5) 87 (75.7) 23 (20.0) 5 (4.3)
C/C
155 (77.5)
C/T T/T C/C C/T T/T C/C C/T T/T
45 (22.5) 0 60 (82.2) 13 (17.8) 0 95 (74.8) 32 (25.2) 0
AN
13 (6.5) 51 (64.5) 18 (29.0) 4 (6.5) 78 (61.4) 40 (31.5) 9 (7.1)
2.17 (0.84-5.61) Reference 1.19 (0.57-2.49) 2.51 (0.44-14.25) Reference 1.94 (1.07-3.52) 2.01 (0.64-6.24)
M
A/A G/G G/A A/A G/G G/A A/A
ED
Female
151 (75.5) 42 (21.0)
PT
Male
129 (64.5) 58 (29.0)
C100T
169 (84.5)
Reference
31 (15.5) 0 65 (76.5) 20 (23.5) 0 104 (90.4) 11 (9.6) 0
1.58 (0.95-2.63) Reference 0.70 (0.32-1.54)
CE
Total
G/G G/A
AC
Female
Reference
T
72 (36.0)
US
C/C
CR
C2850T
Reference 3.18 (1.52-6.67)
Reference 0.076 -
1.95 (0.72-5.31) -
0.188
0.379
1.29 (0.27-6.11)
0.751
0.002
2.78 (0.69-11.18)
0.150
CI: confidence interval. *Adjusted for age, gender, body mass index, alcohol, total cholesterol, hypertension and family history
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ACCEPTED MANUSCRIPT Table 4. Multifactor dimensionality reduction method for C2850T, G1846A and C100T variants and CAD risk TBA
Total
CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.557 0.578 0.589
0.515 0.56 0.525
7/10 10/10 10/10
0.016 0.000 0.000
Male
CYP2D6*2 (C2850T) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.581 0.590 0.604
0.581 0.561 0.528
10/10 8/10 10/10
0.043 0.027 0.012
Female
CYP2D6*2 (C2850T) CYP2D6*2 (C2850T), CYP2D6*10 (C100T) CYP2D6*2 (C2850T), CYP2D6*4 (G1846A), CYP2D6*10 (C100T)
0.616 0.629 0.652
0.616 0.547 0.606
10/10 7/10 10/10
0.000 0.000 0.000
T
TrBA
IP
SNP interaction
CVC
p-value
CR
TrBA: training balance accuracy, TBA: testing balance accuracy, CVC: cross validation consistency, p-values obtained after 10,000
AC
CE
PT
ED
M
AN
US
permutations.
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ACCEPTED MANUSCRIPT Table 5. Haplotype analysis of C2850T, G1846A and C100T polymorphisms and CAD risk
M
AC
CE
PT
ED
OR: odds ratio; CI: confidence interval.
T
OR (95% CI) 1.34 (0.92-1.95) 0.99 (0.57-1.71) 2.61 (1.07-6.34) 1.19 (0.36-3.92) 15.22 (1.97-117.58) 1.89 (0.41-8.67)
US
CR
IP
0.93 (0.50-1.74) 1.17 (0.43-3.23) 1.15 (0.34-3.92) 0.88 (0.20-3.81) 1.57 (0.15-16.0)
AN
Haplotype C2850T G1846A C100T Frequency 1 T G C 0.294 2 C A C 0.101 3 C A T 0.040 4 C G T 0.029 Total 5 T A C 0.027 6 T G T 0.019 Global haplotype association 1 T G C 0.294 2 C A C 0.092 3 C A T 0.044 Male 4 C G T 0.042 5 T A C 0.020 Global haplotype association 1 T G C 0.293 2 C A C 0.105 Female 3 C A T 0.035 4 C G T 0.024 Global haplotype association
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1.64 (1.02-2.62) 0.96 (0.49-1.91) 4.21 (1.12-15.89) 1.71 (0.40-7.32)
p-value 0.130 0.970 0.035 0.770 0.009 0.410 0.002 0.280 0.760 0.820 0.860 0.710 0.720 0.042 0.920 0.035 0.470 0.001
ACCEPTED MANUSCRIPT Abbreviations
AC
CE
PT
ED
M
AN
US
CR
IP
T
µl: Microlitre A: Adenine BMI: Body mass index bp: Basepair C: Cytosine CAD: Coronary artery disease CI: Confidence intervals CNS: Central nervous system CVC: Cross validation consistency CYP2D6: Cytochrome P450 2D6 EDTA: Ethylenediaminetetra acetic acid EM: Extensive metabolizer G: Guanine IM: Intermediate metabolizer LD: Linkage disequilibrium MDR: Multifactor dimensionality reduction min: Minute mM: Millimolar OR: Odds ratio p: Significance value PCR-RFLP: Polymerase chain reaction-restriction fragment length polymorphism PM: Poor metabolizer SNP: Single nucleotide polymorphism SPSS: Statistical Package for Social Sciences T: Thiamine UEM: Ultra-extensive metabolizer
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Highlights
“200 cases and 200 controls from North India”
The 2850T and1846A variants were significantly associated with the increased risk of
T
IP
developing CAD, as observed from the odds ratios for the 2850 T/T and 1846 G/A
CR
genotypes (OR: 2.44, 95% CI: 1.99-4.99, p=0.015 and OR: 1.62, 95% CI: 1.02-2.56, p=0.041, respectively).
The recessive model in C2850T and the dominant model in G1846A are the best fit
US
inheritance models to predict the susceptible gene effects (OR: 2. 07, 95% CI: 1.05-4.08, p=0.031 and OR: 1.70, 95% CI: 1.10-2.62, p=0.016, respectively). Multifactor dimensionality reduction (MDR) analysis showed that the C2850T and
AN
G1846A polymorphisms formed a significant model in predicting the CAD risk. Haplotypes CAT and TAC conferred 2.6-and 15.2-fold increased risk for CAD,
M
ED
respectively.
A gender-specific association between CYP2D6 polymorphisms and CAD was also observed.
The C/T and T/T genotypes of C2850T, G/A genotype of G1846A and C/T genotype of
PT
CE
C100T showed significant association with CAD in females (OR: 2.52, 95% CI: 1.424.38, p=0.001; OR: 3.2, 95% CI: 1.36-7.59, p=0.008; OR: 1.94, 95% CI: 1.07-3.52, p=0.030; OR: 3.18, 95% CI: 1.52-6.67, p=0.002, respectively). Haplotypes TGC and CAT conferred 1.6-and 4.2-fold increased risk for CAD in females,
AC
respectively.
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Figure 1
Figure 2
Figure 3