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. 2016 Jun 1;20(6):297–303. doi: 10.1089/gtmb.2015.0186

MTR, MTRR, and MTHFR Gene Polymorphisms and Susceptibility to Nonsyndromic Cleft Lip With or Without Cleft Palate

Wei Wang 1, Xiao-Hui Jiao 1,✉, Xiao-Ping Wang 1, Xiang-Yu Sun 1, Chen Dong 1
PMCID: PMC4892192  PMID: 27167580

Abstract

Objective: To examine the associations of methionine synthase (MTR), methionine synthase reductase (MTRR), and methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms with the susceptibility to nonsyndromic cleft lip with or without cleft palate (NSCL/P). Methods: Between May 2012 and August 2014, 147 NSCL/P patients (case group) and 129 healthy volunteers (control group) were recruited for the study. The MTR A2756G, MTRR A66G, MTHFR C677T and MTHFR A1298C polymorphisms were assessed by polymerase chain reaction–restriction fragment length polymorphism. Haplotype analyses were performed with SHEsis software. Logistic regression analysis was used to evaluate the possible risk factors for NSCL/P. Generalized multifactor dimensionality reduction (GMDR) was applied to detect gene–gene interactions. Results: MTR A2756G, MTRR A66G, and MTHFR C677T gene polymorphisms were associated with the risk of NSCL/P (all p < 0.05). Logistic regression analysis revealed that MTR A2756G, MTR RA66G, and MTHFR C667T might increase the risk of NSCL/P (odds ratio [OR] = 0.270, 95% confidence interval [95% CI] = 0.106–0.689; OR = 0.159, 95% CI = 0.069–0.368; OR = 0.343, 95% CI = 0.139–0.844). The CA haplotype in the MTHFR gene may serve as a protective factor for NSCL/P (OR = 0.658, 95% CI = 0.470–0.923), and the TA haplotype might be a risk factor (OR = 2.001, 95% CI = 1.301–3.077). GMDR revealed that the optimal models were two- and four-dimensional models with prediction accuracies of 75.73% (p = 0.001) and 77.21% (p = 0.001) and the best cross-validation consistencies of 10/10 and 10/10, respectively. Conclusion: MTR A2756G, MTRR A66G, and MTHFR C677T polymorphisms may be related to NSCL/P, and interactions were detected between the MTR A2756G, MTRR A66G, and MTHFR C677T and A1298C polymorphisms.

Introduction

Orofacial clefts (OFCs) are a group of conditions that include cleft lip, cleft palate, and combined cleft lip and palate. As a common congenital anomaly, OFCs have average birth prevalences of ∼7.75 per 10,000 persons in the United States and ∼7.94 per 10,000 internationally, but the prevalence varies with geography, ethnicity, and socioeconomic status (Mossey et al., 2011; Tanaka et al., 2012; Shaye et al., 2015). OFCs occur in ∼1–2 per 1000 births in the developed world and 3.27 per 1000 births in China, and males account for 63.5% of all cases (Li et al., 2008; Kling et al., 2014; Watkins et al., 2014). Nonsyndromic cleft lip with or without cleft palate (NSCL/P) without any associated anomalies accounts for ∼70–95% of all OFC cases worldwide and elicits heavy health and economic burdens (Carinci et al., 2007; Borges et al., 2015).

Variations in the prevalence of NSCL/P result from ethnic and environmental differences, and multifactorial etiologies involving both genetic and environmental factors have been documented with accumulating evidence (Erickson, 2010; Ludwig et al., 2012, 2014; Jia et al., 2015). Moreover, genetic polymorphisms that encode folate metabolism enzymes, such as methionine synthase (MTR), methionine synthase reductase (MTRR), and methylenetetrahydrofolate reductase (MTHFR), may potentially influence the molecular pathophysiology of NSCL/P (Guo et al., 2009; Blanton et al., 2011; Pan et al., 2012).

Folate metabolism is an intricate process that depends on a series of enzymatic reactions involving numerous genes and pathways that produce active tetrahydrofolate (THF) derivatives (Biselli et al., 2012; Nazki et al., 2014). Aberrantly interacting folate metabolism pathways can result in frank folate deficiencies and affect DNA methylation and synthesis with the downstream effects of disrupting important biological processes, such as craniofacial development (Bhaskar et al., 2011; Blanton et al., 2011). The involvement of the MTR, MTRR, and MTHFR enzymes in folate metabolism and methyl group metabolism makes these enzymes crucial for the maintenance of proper DNA methylation and nucleic acid synthesis (Lopez-Cortes et al., 2013; Weiner et al., 2014).

MTR is encoded by the MTR gene and is responsible for the regeneration of methionine from homocysteine, and MTR gene mutations may contribute to various diseases, including cardiovascular diseases, cancers, birth defects, and congenital anomalies (Weiner et al., 2012; Coppede et al., 2013; Hosseini, 2013; Yang et al., 2013, 2014). In addition, MTR A2756G (rs1805087) has been demonstrated to contribute to breast and prostate cancer, but no previous study has observed associations of MTR A2756G (rs1805087) with NSCL/P in Chinese populations (Guo et al., 2009; de Cassia Carvalho Barbosa et al., 2012; Lopez-Cortes et al., 2013). MTRR regulates the homocysteine metabolic pathway, and the MTRR gene A66G and C524T polymorphisms have been suggested to be related to congenital heart defects (Scazzone et al., 2009; Zeng et al., 2011).

A meta-analysis indicated that the MTRR A66G polymorphism, but not the MTR A2756G polymorphism, may increase the maternal risk for neural tube defects among Caucasians (Ouyang et al., 2013). In addition, a recent study reported that the MTRR A66G polymorphism, but not the MTR A2756G polymorphism, may contribute to NSCL/P in Indian populations (Murthy et al., 2015). MTHFR influences folate and homocysteine metabolisms, and MTHFR gene mutations may increase the occurrence of methylene THF reductase deficiency (Goyette et al., 1994; Trimmer, 2013). Evidence has demonstrated that the MTHFR C677T and A1298C polymorphisms decrease the enzymatic activity and these polymorphisms are the most commonly studied variants in the folate pathway with respect to NSCL/P (Blanton et al., 2011; Zhao et al., 2014; Ebadifar et al., 2015).

However, less information is currently available regarding the importance of the interactions between the MTR A2756G, MTRR A66G, and MTHFR C677T and A1298C polymorphisms and their haplotypes in the modulation of NSCL/P. Therefore, the present study aimed to assess the functional MTR A2756G, MTRR A66G, and MTHFR C677T and A1298C polymorphisms and NSCL/P susceptibility among a Chinese population.

Materials and Methods

Study subjects

Between May 2012 and August 2014, a total of 147 NSCL/P patients (Han nationality, 86 males and 61 females; mean age: 10.23 ± 2.78 years) were randomly recruited from the Department of Oral Maxillofacial Surgery of the First Affiliated Hospital, Harbin Medical University. Congenital NSCL/P patients were diagnosed based on the International Classification of Diseases (ICD-10) developed by the World Health Organization (Tanno et al., 2014).

The exclusion criteria were as follows: (1) cheilopalatognathus associated with other congenital disorders or congenital malformations, such as neural tube defects or congenital heart diseases with concomitant cheilopalatognathus, kabuki make-up syndrome, Van der Woude syndrome, Meckel syndrome, or velocardiofacial syndrome and (2) NSCL/P patients with associated hypertension, coronary heart disease, or other important organ diseases. In addition, 129 volunteers confirmed to be healthy by physical examinations with the corresponding period were enrolled in the control group (Han nationality, 76 males and 53 females; mean age, 10.35 ± 2.86 years). The characteristics of the case and control groups were comparable, including basal state of illness and age (all Ps > 0.05).

Importantly, this study was approved by the Ethics Committee of the First Affiliated Hospital, Harbin Medical University, and we obtained informed consent from the parents or legal guardians of the study subjects. The study protocols followed the ethical principles for medical research involving human subjects of the Declaration of Helsinki (Glas et al., 2013).

Single-nucleotide polymorphism screen and selection

Using the NCBI-Database of Single-Nucleotide Polymorphisms (dbSNPs; www.ncbi.nlm.nih.gov/SNP/) and the HapMap database (http://SNP.cshl.org/cgi-perl/gbrowse/hapmap27_B36/), we searched and downloaded data packets related to the MTHFR, MTRR, and MTR SNPs in the human gene pool. The MTHFR, MTRR and MTR SNPs were screened with Haploview 4.2 software. The parameters were set as follows: Chinese Han population, minor allele frequency (MAF) = 0.05, and r2 > 0.8. The linkage disequilibrium (D′) 95% confidence interval (95% CI) was calculated to classify the adjacent SNPs with D′ 95% CI of 0.70–0.98 into the same haplotype block. After selection, MTR rs1805087 (A2756G), MTRR rs1801394 (A66G), MTHFR rs1801133 (C677T) and rs1801131 (A1298C) were found to be eligible for further assessment (Table 1).

Table 1.

SNP Variation Information of MTR, MTRR, and MTHFR

Gene dbSNP Function Alleles Allele frequency (CHB)
MTR rs1805087 (A2756G) Missense A/G A:0.892, G:0.108
MTRR rs1801394 (A66G) Missense A/G A:0.750, G:0.250
MTHFR rs1801133 (C677T) Missense C/T C:0.561, T:0.439
  rs1801131 (A1298C) Missense A/C A:0.756, C:0.244
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CHB, Chinese Han population; dbSNP, database of single-nucleotide polymorphisms; MTHFR, methylenetetrahydrofolate reductase; MTR, methionine synthase; MTRR, methionine synthase reductase; SNP, single-nucleotide polymorphism.

Polymerase chain reaction–restriction fragment length polymorphism

Blood samples from veins in the elbow (5 mL) were collected from all subjects after overnight fasting, placed in tubes with EDTA anticoagulant, and stored in a −70°C refrigerator until further use. The DNA of each subject was extracted using the phenol/chloroform extraction method. The MTR rs1805087 (A2756G), MTRR rs1801394 (A66G), MTHFR rs1801133 (C677T) and rs1801131 (A1298C) genotypes were sequenced and analyzed by polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP). The PCR primer sequences (Biological Engineering Co., Ltd.) are summarized in Table 2. We then hired the Beijing Genomics Institute to verify the sequencing of a randomly selected 10% of the PCR products.

Table 2.

Primers Designed for MTR, MTRR, and MTHFR Gene Polymorphism Loci

Genes Primers Product length (bp)
MTR A2756G Forward: 5′-CATGGAAGAATATGAAGATATTAGAC-3′ 189
  Reverse: 5′-GAACTAGAAGACAGAAATTCTCTA-3′  
MTRR A66G Forward: 5′-GCAAAGGCCATCGCAGAAGACAT-3′ 151
  Reverse: 5′-AAACGGTAAAATCCACTGTAACGG-3′  
MTHFR C677T Forward: 5′-TGAAGGAGAAGGTGTCTGCGGGA-3′ 198
  Reverse: 5′-AGGACGGTGCGGTGAGAGTG-3′  
MTHFR A1298C Forward: 5′-AAGGAGGAGCTGCTGCTGAAGATG-3′ 237
  Reverse: 5′-CTTTGCCATGTCCACAGCATG-3′  
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The PCR-amplified products were digested overnight at 37°C with specific restriction enzymes (HaeIII for MTR A2756G, NdeI for MTRR A66G, Hinf for MTHFR C677T, and MboII for MTHFR A1298C) in a total volume of 20 μL containing 17 μL PCR-amplified products, ddH2O, 2 μL 10× reaction buffer, and 1 μL restriction enzyme (10 U/μL). Six percent polyacrylamide gel electrophoresis was used to separate the enzyme-digested products. The genotypes were determined according to the enzyme map.

Statistical analyses

The continuous variables are presented as the means ± standard deviations. The categorical variables are presented as frequencies or percentages. We conducted t-tests to compare the continuous variables and chi-square or Fisher's exact tests as appropriate to compare the categorical variables. The deviations from the Hardy–Weinberg equilibrium were estimated. Odds ratios (ORs) and their 95% CI were calculated for the associations of the MTR, MTRR, and MTHFR polymorphisms with NSCL/P, which were obtained from logistic regression analysis. Haplotype analysis was performed with the SHEsis software.

Generalized multifactor dimensionality reduction (GMDR) was performed using a JAVA-based free software (GMDR 0.9) to detect the gene–gene interactions (Lou et al., 2007). After sorting according to NSCL/P and health control status, four SNPs of the MTR, MTRR, and MTHFR genes were introduced into the GMDR model as variables and subjected to sign and permutation tests to calculate the cross-validation consistencies and balance test accuracies of the different factor combinations in the various dimensions.

The cross-validation consistency was used to measure the degree of consistency and to determine whether the selected interaction was the optimal model, and the balance test accuracy scores, which ranged from 0.50 (indicating an unsatisfactory model) to 1.00 (indicating the optimal model), were used to accurately predict the degree of the interaction. The predictive accuracy of the permutation test was used to measure the significance of each model (p < 0.05). SPSS 19.0 software (SPSS, Inc.) was utilized for the statistical analyses. A two-tailed p value below 0.05 indicated statistical significance.

Results

Genotype and allele frequency distributions

The genotype distributions of the studied polymorphisms were consistent with the Hardy–Weinberg predictions in both groups (all Ps > 0.05). The MTR A2756G polymorphism was related to the development of NSCL/P (AA vs. AG: OR = 1.709, 95% CI = 1.047–2.789, p = 0.031; A vs. G: OR = 1.553, 95% CI = 1.017–2.371, p = 0.041). The MTRR A66G polymorphism increased the risk of NSCL/P (AA vs. AG: OR = 5.556, 95% CI = 2.953–10.45, p < 0.001; AA vs. GG: OR = 2.008, 95% CI = 1.104–3.650, p = 0.021; A vs. G: OR = 1.607, 95% CI = 1.147–2.250, p = 0.006), and an obvious association between the MTHFR C677T polymorphism and NSCL/P risk was observed (CC vs. CT: OR = 2.166, 95% CI = 1.118–4.196, p = 0.020; CC vs. TT: OR = 0.362, 95% CI = 0.156–0.838, p = 0.016; C vs. T: OR = 0.646, 95% CI = 0.461–0.905, p = 0.011).

In addition, no significant differences were observed in terms of the genotype or allele frequency distributions of MTHFR A1298C polymorphism between the case group and control group (all Ps > 0.05) (Table 3).

Table 3.

The Genotype and Allele Frequency Distributions of MTR A2756G, MTRR A66G, MTHFR C677T, and MTHFR A1298C Between the Case Group and the Control Group

SNPs   Case (n = 147), n (%) Control (n = 129), n (%) χ2 p OR (95% CI)
MTR A2756G AA 99 (67.3) 70 (54.3) Ref    
  AG 48 (32.7) 58 (44.9) 4.629 0.031 1.709 (1.047–2.789)
  GG 0 (0.0) 1 (0.8) 1.403 0.236 4.234 (0.169–105.5)
  AG+GG 48 (32.7) 59 (45.7) 4.954 0.026 0.575 (0.353–0.938)
  A 246 (83.7) 198 (76.7) Ref    
  G 48 (16.3) 60 (23.3) 4.193 0.041 1.553 (1.017–2.371)
MTRR A66G AA 71 (48.3) 29 (22.5) Ref    
  AG 26 (17.7) 59 (45.7) 30.091 <0.001 5.556 (2.953–10.45)
  GG 50 (34.0) 41 (31.8) 5.289 0.021 2.008 (1.104–3.650)
  AG+GG 76 (51.7) 100 (77.5) 19.821 <0.001 0.310 (0.184–0.525)
  A 168 (57.1) 117 (45.3) Ref    
  G 126 (42.9) 141 (54.7) 7.654 0.006 1.607 (1.147–2.250)
MTHFR C677T CC 28 (19.1) 19 (14.7) Ref    
  CT 66 (44.9) 97 (75.2) 5.373 0.020 2.166 (1.118–4.196)
  TT 53 (36.0) 13 (10.1) 5.811 0.016 0.362 (0.156–0.838)
  CT+TT 119 (80.9) 110 (85.3) 0.907 0.341 0.734 (0.388–1.389)
  C 122 (41.5) 135 (52.3) Ref    
  T 172 (58.5) 123 (47.7) 6.476 0.011 0.646 (0.461–0.905)
MTHFR A1298C AA 66 (44.9) 57 (44.2) Ref    
  AC 67 (45.6) 57 (44.2) 0.003 0.953 0.985 (0.597–1.625)
  CC 14 (9.5) 15 (11.6) 0.273 0.602 1.241 (0.552–2.789)
  AC+CC 81 (55.1) 72 (55.8) 0.014 0.906 0.972 (0.604–1.564)
  A 199 (67.7) 171 (66.3) Ref    
  C 95 (32.3) 87 (33.7) 0.123 0.726 1.066 (0.747–1.521)
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95% CI, 95% confidence interval; OR, odds ratio; Ref, reference.

Multiple logistic regression analysis

In the multiple logistic regression analysis, the MTR A2756G, MTR RA66G, and MTHFR C667T polymorphisms were used as independent variables, and NSCL/P was used as the dependent variable. The results revealed that the MTR A2756G (OR = 0.270, 95% CI = 0.106–0.689, p = 0.006), MTR RA66G (OR = 0.159, 95% CI = 0.069–0.368, p < 0.001), and MTHFR C667T (OR = 0.343, 95% CI = 0.139–0.844, p = 0.020) polymorphisms might increase the risk of NSCL/P (Table 4).

Table 4.

Multiple Logistic Regression Analysis for NSCL/P

Variables β SE Wald Significance Exp(β) 95% CI
MTR A2756G −1.310 0.479 7.488 0.006 0.270 0.106–0.689
MTRR A66G −1.840 0.428 18.464 <0.001 0.159 0.069–0.368
MTHFR C667T 1.071 0.460 5.419 0.020 0.343 0.139–0.844
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β, partial regression coefficient; Exp(β), exponent function (partial regression coefficient); NSCL/P, nonsyndromic cleft lip with or without cleft palate; SE, standard error.

MTHFR C677T and A1298C haplotype analysis

Haplotype analysis was performed with SHEsis software, and the haplotypes with frequencies below 3% were ignored regarding the MTHFR at different loci. The haplotype analysis demonstrated that the CA haplotype frequency was significantly lower in the case group than in the control group (χ2 = 5.907, p = 0.015), whereas the TA haplotype frequency was significantly greater in the case group (χ2 = 10.201, p = 0.001; Table 5).

Table 5.

Haplotype Analyses of MTHFR C677T and MTHFR A1298C Polymorphisms

  Frequency  
Haplotype Case Control χ2 p OR (95% CI)
CA 122.00 (0.415) 132.52 (0.514) 5.907 0.015 0.658 (0.470–0.923)
TA 77.00 (0.262) 38.48 (0.149) 10.201 0.001 2.001 (1.301–3.077)
TC 95.00 (0.323) 84.52 (0.328) 0.036 0.849 0.966 (0.676–1.381)
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Interactions of the MTR, MTRR, and MTHFR genotypes and the NSCL/P risk

The four SNPs of MTR, MTRR, and MTHFR were incorporated as variables into the GMDR model according to the NSCL/P or health control status. The result revealed that the two-, three-, and four-dimensional model combinations were statistically significant. The optimal models were the two- and four-dimensional models, which had prediction accuracies of 75.73% (p = 0.001) and 77.21% (p = 0.001) and the best cross-validation consistencies of 10/10 and 10/10, respectively. These results indicated that the MTR, MTRR, and MTHFR SNPs exhibited interaction effects on NSCL/P (Table 6).

Table 6.

Interactions of MTR, MTRR, And MTHFR And NSCL/P Risk in Generalized Multifactor Dimensionality Reduction

Dimensional model Combinations Cross-validation consistency Prediction accuracy (%) p
1 MTHFRC677T 9/10 63.66 0.055
2 MTRRA66G/MTHFRC677T 10/10 75.73 0.001
3 MTHFRA1298C/MTRRA66G/MTHFRC677T 5/10 73.74 0.001
4 MTHFRA1298C/MTRA2756G/MTRRA66G/MTHFRC677T 10/10 77.21 0.001
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Discussion

Extensive studies have explored the relationships of gene polymorphisms related to the folic acid/homocysteine metabolic pathway with NSCL/P, and the results have revealed that the relationships of genetic mutations with NSCL/P susceptibility vary between different populations (Birnbaum et al., 2009; Bohmer et al., 2013; Wang et al., 2013; Borges et al., 2015). Regarding the findings of the present study, the MTR A2756G, MTRR A66G, and MTHFR C677T gene polymorphisms, but not the MTHFR A1298C (glutamate-alanine) polymorphism in exon 7, may increase the risk of NSCL/P among the Chinese population, and these findings were further confirmed by logistic regression analysis.

These three SNPs can alter homocysteine levels and reduce enzymatic activity, which may lead to DNA damage associated with folate deficiency that may further contribute to the development of NSCL/P (Lopez-Cortes et al., 2013; Wang et al., 2013). More specifically, MTRA2756G (rs1805087) might result in an Asp919Gly amino acid substitution that has been demonstrated to contribute to alterations in the homocysteine and folate levels (Diwakar et al., 2008; Weiner et al., 2012). The MTRR enzyme catalyzes methylcobalamin regeneration and plays roles in maintaining the activation of the MTR. The MTRR polymorphism at nucleotide 66 (A-to-G) may result in variant enzyme activity with lowered affinity for MTR (Han et al., 2012).

MTHFR C677T causes the substitution of alanine to valine and reduces enzyme activity, and the MTHFR 677T allele might increase the risk of NSCL/P in Asian populations, including Chinese populations (Nan et al., 2014; Zhao et al., 2014). Furthermore, MTHFR 677T homozygotes are involved in NSCL/P, and 677CT heterozygosity is a minor risk factor, whereas MTHFR A1298C poses no risk for NSCL/P with any combination of alleles among Indian population (Ali et al., 2009). Partially consistent with our study results, the heterozygous genotypes of MTHFR 677AT, MTR 2756AG, and MTRR 66AG may be associated with increased risks of NSCL/P among Western Ukrainian inhabitants, especially regarding the risk of mothers delivering NSCL/P infants (Chorna et al., 2011).

However, conflicting results regarding the MTR A2756G polymorphism and the risk of NSCL/P have been reported among Chinese and Indian populations; thus, future studies among Chinese populations are needed (Guo et al., 2009).

Three SNPs (i.e., MTR rs6428977, rs12060264, and MTRR rs7730643), but not MTHFR, exhibit nominally significant associations with NSCL/P based on a transmission disequilibrium test of 806 NSCL/P trios among a Chinese population (Bohmer et al., 2013). In addition, a significant association of the CA haplotype (i.e., the C allele of rs1801133 and the A allele of rs2274976) with NSCL/P has been observed and may be driven by the strong individual effect of the rs2274976 polymorphism. Moreover, the combination of MTHFR rs2274976, MTHFD1 rs2236225, and SLC19A1 rs1051266 best predicted the maternal risk for NSCL/P among a Brazilian population (Bufalino et al., 2010).

The allelic combination of MTHFR rs1801131 and rs1801133 has been found to be associated with haplotype A-T overtransmission in the affected NSCL/P offspring among a Brazilian population (Mostowska et al., 2012; de Aguiar et al., 2015). The aforementioned information indicates the important role of gene–gene interactions and haplotype analyses in predicting NSCL/P risk among Chinese populations. Another significant finding suggested that the CA haplotype may serve as a protective factor against NSCL/P, while the TA haplotype acted as a risk factor, and the MTR, MTRR, and MTHFR SNPs exhibited interactive effects regarding NSCL/P, particularly in the two- and four-dimensional models.

A recent study indicated that the interaction between the MTR and BHMT genes plays a vital role in NSCL/P pathogenesis among Chinese populations (Jin et al., 2015). Interestingly, significant differences were observed in the allele frequencies and haplotype analyses of rs4077829 and rs10802565 in MTR between the NSCL/P and control groups, but these differences were not significant after correction with 10,000 permutations. Polymorphisms related to alterations in folate metabolism were not found to be involved in NSCL/P among a Chinese population (Jiang et al., 2014).

There are some limitations to the present study. First, the limited sample size of this study may have biased the associations of the MTR A2756G, MTRR A66G, and MTHFR C677T gene polymorphisms with the NSCL/P risk. Second, this study did not consider the possibility of linkage disequilibrium between SNP-SNP interactions. Third, gene–gene or gene–environment interactions may have influenced the results. These limitations could be overcome with more specific studies with larger samples and with more advanced molecular genetics technology.

In summary, these preliminary findings revealed that the MTR A2756G, MTRR A66G, and MTHFR C677T gene polymorphisms, but not the MTHFR A1298C polymorphism, were associated with increased risks for NSCL/P in a Chinese population. Future studies will uncover additional mechanistic insights and potential gene–environment interactions of the MTR A2756G, MTRR A66G, and MTHFR C677T gene polymorphisms with NSCL/P.

Acknowledgment

We acknowledge the helpful comments about this article that we received from our reviewers.

Author Disclosure Statement

No competing financial interests exist.

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