Abstract

Background: Prenatal intake of folic acid is important for prevention of NSCL/P (nonsyndromic cleft lip with or without cleft palate). Associated genes in folate pathway are major enzymes of folic acid metabolism that is crucial for preventing birth defects. The present meta-analysis aims to investigate the association between four SNPs in folate pathway genes and the risk of NSCL/P.

Methods: Comprehensive bioinformatics analysis was used to predict the functional pathogenicity of genetic variation. The PubMed, Embase database and Google Scholar were searched by two researchers. Stata 11.0 software was used to analyze the results. Subgroup analysis was carried out to assess the influence of genetic background. Sensitivity analysis, regression analysis and publication analysis were also conducted to enhance the strength of our results.

Results: It is estimated that the probability of two missense mutation rs1801133 in MTHFR and rs1801394 in MTRR are more likely to be damaging by bioinformatics analysis. A significant association between rs1801133 and risk of NSCL/P in two genetic models: TT genotype vs CC genotype (OR = 1.333 95%CI = 1.062–1.674, P = 0.013), and recessive model (OR = 1.325 95%CI = 1.075–1.634, P = 0.008). A significant protective association between rs1801394 GG genotype and NSCL/P in Asian (GG vs AA, OR = 0.520 95%CI = 0.321–0.841, P = 0.008) was observed. Meta-regression, sensitivity analysis, and publication bias analysis confirmed that the results of the present study were statistically significant.

Conclusions: The present study identified that rs1801133 in MTHFR is associated with the risk of NSCL/P, and rs1801394 GG genotype in MTRR play a protective role in Asian. Further, larger studies should be performed to confirm these findings.

Background

NSCL/P (Nonsyndromic cleft lip with or without cleft palate) is one of the most common birth defects, characterized by craniofacial abnormality due to incomplete separation between the nasal and oral cavities [1]. NSCL/P can influence the quality of life by affecting communication problems and contributing to dysphagia. Cleft lip and palate occur in approximately one in 500–700 live births worldwide. Cleft lip is a hereditary disease with polygenic inheritance, however, the underlying genetic cause and fundamental molecular mechanism of the disease remains still elusive. However, the incidence of cleft lip varies substantially across different ethnic groups and geographical areas (http://www.who.int/oral_health/publications/factsheet/en/).

Although folic acid and multivitamin supplementation in prescribed period of pregnancy has been indicated as an effective method to prevent the risk of oral facial cleft. The significance of genetic locus in folate pathway and folate metabolism involved in disease pathogenesis is not clear [2,3]. Recently, many efforts have been made to find the genetic variants in folate pathway genes such as MTHFR (methylenetetrahydrofolate reductase), MTRR (Methionine synthase reductase), TCN2 (transcobalamin 2), and BHMT (betaine-homocysteine methyltransferase) and their susceptibility to cleft lip [4–10]. MTHFR plays an important role in primary circulation of folate and catalyzing the reaction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate. The substrate and metabolites are important for DNA biosynthesis, cell division and process during development. Currently, there is no targeted therapy for NSCL/P patients carried with MTHFR mutations, while there are some reports on other genetic diseases. In 2017, Martinez Saguer et al. reported successful management of hereditary angioedema during pregnancy in a patient carried with heterozygous MTHFR mutation [11]. Lahiri et al. reported successful conservative treatment of myocardial infarction in a teenager carried with MTHFR mutation [12]. Recently, Al-Eitan et al. also showed that MTHFR polymorphism was associated with treatment response in Jordanian population with epilepsy [13]. MTRR and TCN2 are essential in maintaining the levels of activated vitamin B12, and BHMT is vital for catalyzing betaine to dimethyl glycine (DMG), which are involved in remethylating Hcy (homocysteine) to Met (methionine) (Figure 1). The four genetic missense variations 677C>T in MTHFR (rs1801133), 66A>G in MTRR (rs1801394), 776C>G in TCN2 (rs1801198), and 716 G>A in BHMT (rs3733890) have influence on protein function (Table 1), and have been reported to be associated with cleft lip. However, there are different conclusions regarding the influence of these SNPs in different populations [4–10,14–41].

Folate pathway

Figure 1
Folate pathway

Abbreviations: SAH, S-adenosylhomocysteine; SAM, S-adenosyl methionine;

Figure 1
Folate pathway

Abbreviations: SAH, S-adenosylhomocysteine; SAM, S-adenosyl methionine;

Table 1
Information of four SNPs in the present study
SNPGeneCodonPolyphen2SIFTCADDPhyloPLRT
ScorePredictionScorePredictionScorePredictionScorePredictionScorePrediction
rs1801133 MTHFR C677T 0.998 probability damaging 0.027 damage 25.0 damaging 9.137 conserved deleterious 
rs1801394 MTRR A66G probability damaging 0.064 tolerable 23.3 damaging 0.098 nonconserved deleterious 
rs1801198 TCN2 C776G 0.315 benign 0.09 tolerable 18.9 tolerable 0.081 nonconserved 0.027 neutral 
rs3733890 BHMT G716A 0.064 benign 0.218 tolerable 21.8 damaging 2.864 conserved 0.070 neutral 
SNPGeneCodonPolyphen2SIFTCADDPhyloPLRT
ScorePredictionScorePredictionScorePredictionScorePredictionScorePrediction
rs1801133 MTHFR C677T 0.998 probability damaging 0.027 damage 25.0 damaging 9.137 conserved deleterious 
rs1801394 MTRR A66G probability damaging 0.064 tolerable 23.3 damaging 0.098 nonconserved deleterious 
rs1801198 TCN2 C776G 0.315 benign 0.09 tolerable 18.9 tolerable 0.081 nonconserved 0.027 neutral 
rs3733890 BHMT G716A 0.064 benign 0.218 tolerable 21.8 damaging 2.864 conserved 0.070 neutral 

Here, a comprehensive bioinformatics analysis was used to predict the functional pathogenicity of genetic variation and a systematic review according to PRISMA2009 was performed to provide more precise statistical results. Our study could provide basic data for exploring effective therapeutic strategies for NSCL/P.

Methods

Literature search

All published studies before April 2019 were searched using the PubMed database, Embase database, and Google Scholar with the following terms: “NSCL/P”, “cleft lip”, “SNP”, “polymorphism”, “genetic”, “variant”, “MTHFR”, “MTHFR C677T”, “rs1801133”, “MTRR”, “MTRR A66G”, “rs1801394”, “TCN2”, “TCN2 C776G”, “rs1801198”, “BHMT”, “BHMT G716A”, and “rs3733890”. Relevant references of related articles were also included.

Inclusion criteria and exclusion criteria

All studies were independently reviewed by two researchers. Studies were included in the meta-analysis if they met the following criteria: (1) original study of human participants; (2) an association study between rs1801133 and/or rs1801394 and/or rs1801198 and/or rs3733890 and NSCL/P; (3) case–control study or cohort study; (4) allele data were available; (5) the largest sample size or sufficient useful data were included in duplicate publications from the same population. Studies were excluded if they met the following criteria: (1) allele data were not available; (2) publications duplicate from the same population; and (3) review article and meta-analysis.

Quality score assessment

The quality of research was evaluated to guarantee the strength of results and conclusions. The NOS (Newcastle–Ottawa scale) score was calculated to assess the quality of studies [42]. A maximum of nine scores, including selection, comparability and exposure items, could be awarded, <4, 4–6, and >6 indicate poor, moderate, and good quality, respectively. Any variances in comparison were decided by a third researcher.

Data extraction

The data were extracted independently by two researchers from all included studies using an integrated and standardized form. The following information was extracted: (1) first author name; (2) publication year; (3) population ethnicity; and (4) genotype distribution.

Computational and statistical analysis

Polyphen2, SIFT, CADD, phyloP, and LRT were used for bioinformatics prediction. The HWE (Hardy–Weinberg equilibrium) test was calculated by the chi-square test. The distribution of allelic frequencies in controls were considered to deviate from HWE when P < 0.05. STATA (11.0; Stata Corporation, College Station, TX, U.S.A.) software was used to calculate the results of meta-analysis. Heterogeneity across individual studies was assessed by Cochran's Q test and I2 statistic (P < 0.10 and I2 > 50% indicated evidence of heterogeneity).The fixed-effects model (Mantel–Haenszel method) was used to estimate the pooled OR when there was no evidence of the heterogeneity; otherwise, the random-effects model analyzed by DerSimonian and Laird method was used. Using rs1801133 C>T as an example: (1) allele model, T allele vs. C allele; (2) dominant model, (CT+TT vs. CC); (3) recessive model, (TT vs. CT+CC); and (4) genotype model, (CT vs. CC; TT vs. CC). The same genetic models were performed for “rs1801394”, “rs1801198”, and “rs3733890”. A P value of P < 0.05 was established as the significant difference. Two subgroups, including Caucasian and Asian, based on ethnicity were analyzed to reduce the heterogeneity and influences from the genetic background. Meta-regression, and one-way sensitivity analysis, and Egger’s regression test were also performed [43]. The trim and fill method was used when publication bias exists.

Results

Study characteristics

According to the search strategy, 926 publications were identified in the initial search. After evaluating the titles and abstracts, 801 publications were excluded, and 125 full-text publications were further reviewed (Figure 2). By applying the inclusion criteria, 34 publications were used for the final meta-analysis. Overall, 30 publications with 5517 cases and 7770 controls were included in the rs1801133 group; ten publications with 1767 cases and 2029 controls were included in the rs1801394 group; six publications with 1815 cases and 898 controls were included in the rs1801198 and five studies with 1253 cases and 1562 controls were included in the rs3733890 group. A total of seven studies in the control group (not excluded) were found to deviate from HWE. The main characteristics of the included publications are shown in Table 2.

Study flow diagram

Figure 2
Study flow diagram
Figure 2
Study flow diagram
Table 2
Characteristics of included studies about associations between four SNPs of folate pathway gene and NSCL/P
StudyYearEthnicityGenotype in caseGenotype in controlP value of HWE testNOS scoreSource of controlGenotyping method
MTHFR rs1801133TotalCCCTTTAFTotalCCCTTTAF
Shaw et al. 1998 Caucasian 310 143 127 40 0.334 383 156 178 49 0.360 0.873 PB PCR- 
Tolarova et al. 1998 Caucasian 111 43 49 19 0.392 106 46 52 0.321 0.195 NA NA 
Gaspar et al. 1999 Caucasian 77 30 39 0.357 103 49 49 0.286 0.096 HB NA 
Wyszynski et al. 2000 Caucasian 259 114 109 36 0.349 327 129 154 44 0.370 0.854 PB Q-PCR, Taqman 
Martinelli et al. 2001 Caucasian 64 22 30 12 0.422 106 46 43 17 0.363 0.205 PB PCR 
Grunert et al. 2002 Caucasian 66 34 26 0.288 184 90 69 25 0.323 0.052 PB PCR 
Shotelersuk et al. 2003 Asian 109 84 25 0.115 202 154 46 0.124 0.478 PB PCR 
van Rooij et al. 2003 Caucasian 105 54 45 0.271 128 70 54 0.242 0.091 PB PCR 
Gaspar et al. 2004 Caucasian 644 327 269 48 0.283 424 213 172 39 0.295 0.616 HB PCR 
Pezzetti et al. 2004 Caucasian 110 28 58 24 0.482 289 95 151 43 0.410 0.174 HB PCR 
Brandalize et al. 2007 Caucasian 114 49 46 19 0.368 100 45 41 14 0.345 0.353 HB PCR 
Chevrier et al. 2007 Caucasian 148 66 60 22 0.351 165 51 81 33 0.445 0.935 HB PCR 
Little et al. 2008 Caucasian 96 39 47 10 0.349 224 94 101 29 0.355 0.819 PB MS-PCR 
Mills et al. 2008 Caucasian 492 217 221 54 0.334 1599 715 721 163 0.327 0.341 HB PCR 
Ali et al. 2009 Asian 323 225 87 11 0.169 214 176 36 0.093 0.916 PB PCR 
Sozen et al. 2009 Caucasian 179 81 80 18 0.324 138 66 65 0.286 0.073 PB PCR 
Mostowska et al. 2010 Caucasian 163 81 65 17 0.304 171 78 77 16 0.319 0.629 PB PCR 
Ebadifar et al. 2010 Asian 61 21 18 22 0.508 215 114 72 29 0.302 0.003 PB PCR 
Han et al. 2011 Asian 187 46 106 35 0.471 213 74 110 29 0.394 0.236 HB PCR 
Aslar et al. 2013 Caucasian 80 13 57 10 0.481 125 59 62 0.280 0.010 PB PCR 
Kumari et al. 2013 Asian 467 327 125 15 0.166 469 364 100 0.117 0.518 Mixed PCR 
Murthy et al. 2014 Asian 123 104 19 0.077 141 107 31 0.131 0.672 HB PCR 
Estandia-Ortega et al. 2014 Caucasian 132 39 55 38 0.496 370 143 172 55 0.381 0.780 PB PCR 
Jiang et al. 2015 Asian 204 59 107 38 0.449 226 62 108 56 0.487 0.512 PB Sequenom 
Bezerra et al. 2015 Caucasian 140 74 54 12 0.279 175 85 70 20 0.314 0.341 PB PCR 
Abdollahi-Fakhim et al. 2015 Asian 121 38 58 25 0.446 103 27 54 22 0.476 0.605 PB PCR 
Wang et al. 2016 Asian 147 28 66 53 0.585 129 19 97 13 0.477 <0.001 PB PCR 
Marini et al. 2016 Caucasian 330 119 159 52 0.398 360 148 154 58 0.375 0.097 HB Taqman 
Karas Kuzelicki et al. 2018 Caucasian 103 45 45 13 0.345 199 85 96 18 0.332 0.214 Mixed Taqman 
Rafik et al. 2019 Africa 52 44 0.077 182 97 74 11 0.264 0.526 PB PCR 
MTRR rs1801394  Total AA AG GG AF Total AA AG GG AF      
Brandalize et al. 2007 Caucasian 114 36 69 0.382 100 33 61 0.365 0.002 HB PCR 
Mostowska et al. 2010 Caucasian 164 31 81 52 0.564 166 34 70 62 0.584 0.089 PB PCR 
Aslar et al. 2014 Caucasian 100 14 72 14 0.500 125 13 107 0.468 <0.001 PB PCR 
Waltrick-Zambuzzi et al. 2015 Caucasian 342 95 194 53 0.439 401 136 193 72 0.420 0.806 HB Q-PCR 
Jiang et al. 2015 Asian 204 123 71 10 0.223 226 124 84 18 0.265 0.480 PB Sequenom 
Bezerra et al. 2015 Caucasian 140 98 37 0.168 175 112 60 0.189 0.111 PB PCR 
Murthy et al. 2015 Asian 123 42 81 0.329 141 65 76 0.270 <0.001 HB PCR 
Wang et al. 2016 Asian 147 71 26 50 0.429 129 29 59 41 0.547 0.380 PB PCR 
Marini et al. 2016 Caucasian 330 160 134 36 0.312 367 175 161 31 0.304 0.478 HB Sequenom 
Karas Kuzelicki et al. 2018 Caucasian 103 14 56 33 0.592 199 30 111 58 0.570 0.051 Mixed PCR 
TCN 2 rs1801198  Total CC CG GG AF Total CC CG GG AF      
Martinelli et al. 2006 Caucasian 218 85 110 23 0.358 289 89 150 50 0.433 0.330 PB PCR 
Mills et al. 2008 Caucasian 316 99 153 64 0.445 1097 347 532 218 0.441 0.243 HB Taqman 
Mostowska et al. 2010 Caucasian 163 46 88 29 0.448 181 48 103 30 0.450 0.044 HB Sequenom 
Jin et al. 2015 Asian 429 76 215 138 0.572 461 75 231 155 0.587 0.475 PB Sequenom 
Waltrick-Zambuzzi et al. 2015 Caucasian 359 139 160 60 0.390 440 179 199 62 0.367 0.576 Mixed Sequenom 
Marini et al. 2016 Caucasian 330 135 140 55 0.379 366 160 155 51 0.351 0.177 PB PCR 
BHMT rs3733890  Total GG GA AA AF Total GG GA AA AF      
Mostowska et al. 2010 Caucasian 174 95 76 0.236 176 82 75 19 0.321 0.766 HB PCR 
Hu et al. 2011 Asian 166 90 56 20 0.289 268 130 118 20 0.295 0.334 HB PCR 
Jin et al. 2015 Asian 481 219 202 60 0.335 554 265 245 44 0.301 0.222 PB PCR 
Marini et al. 2016 Caucasian 330 140 150 40 0.348 366 156 163 47 0.351 0.665 PB Sequenom 
Karas Kuzelicki et al. 2018 Caucasian 102 42 51 0.338 198 98 84 16 0.293 0.734 HB Q-PCR 
StudyYearEthnicityGenotype in caseGenotype in controlP value of HWE testNOS scoreSource of controlGenotyping method
MTHFR rs1801133TotalCCCTTTAFTotalCCCTTTAF
Shaw et al. 1998 Caucasian 310 143 127 40 0.334 383 156 178 49 0.360 0.873 PB PCR- 
Tolarova et al. 1998 Caucasian 111 43 49 19 0.392 106 46 52 0.321 0.195 NA NA 
Gaspar et al. 1999 Caucasian 77 30 39 0.357 103 49 49 0.286 0.096 HB NA 
Wyszynski et al. 2000 Caucasian 259 114 109 36 0.349 327 129 154 44 0.370 0.854 PB Q-PCR, Taqman 
Martinelli et al. 2001 Caucasian 64 22 30 12 0.422 106 46 43 17 0.363 0.205 PB PCR 
Grunert et al. 2002 Caucasian 66 34 26 0.288 184 90 69 25 0.323 0.052 PB PCR 
Shotelersuk et al. 2003 Asian 109 84 25 0.115 202 154 46 0.124 0.478 PB PCR 
van Rooij et al. 2003 Caucasian 105 54 45 0.271 128 70 54 0.242 0.091 PB PCR 
Gaspar et al. 2004 Caucasian 644 327 269 48 0.283 424 213 172 39 0.295 0.616 HB PCR 
Pezzetti et al. 2004 Caucasian 110 28 58 24 0.482 289 95 151 43 0.410 0.174 HB PCR 
Brandalize et al. 2007 Caucasian 114 49 46 19 0.368 100 45 41 14 0.345 0.353 HB PCR 
Chevrier et al. 2007 Caucasian 148 66 60 22 0.351 165 51 81 33 0.445 0.935 HB PCR 
Little et al. 2008 Caucasian 96 39 47 10 0.349 224 94 101 29 0.355 0.819 PB MS-PCR 
Mills et al. 2008 Caucasian 492 217 221 54 0.334 1599 715 721 163 0.327 0.341 HB PCR 
Ali et al. 2009 Asian 323 225 87 11 0.169 214 176 36 0.093 0.916 PB PCR 
Sozen et al. 2009 Caucasian 179 81 80 18 0.324 138 66 65 0.286 0.073 PB PCR 
Mostowska et al. 2010 Caucasian 163 81 65 17 0.304 171 78 77 16 0.319 0.629 PB PCR 
Ebadifar et al. 2010 Asian 61 21 18 22 0.508 215 114 72 29 0.302 0.003 PB PCR 
Han et al. 2011 Asian 187 46 106 35 0.471 213 74 110 29 0.394 0.236 HB PCR 
Aslar et al. 2013 Caucasian 80 13 57 10 0.481 125 59 62 0.280 0.010 PB PCR 
Kumari et al. 2013 Asian 467 327 125 15 0.166 469 364 100 0.117 0.518 Mixed PCR 
Murthy et al. 2014 Asian 123 104 19 0.077 141 107 31 0.131 0.672 HB PCR 
Estandia-Ortega et al. 2014 Caucasian 132 39 55 38 0.496 370 143 172 55 0.381 0.780 PB PCR 
Jiang et al. 2015 Asian 204 59 107 38 0.449 226 62 108 56 0.487 0.512 PB Sequenom 
Bezerra et al. 2015 Caucasian 140 74 54 12 0.279 175 85 70 20 0.314 0.341 PB PCR 
Abdollahi-Fakhim et al. 2015 Asian 121 38 58 25 0.446 103 27 54 22 0.476 0.605 PB PCR 
Wang et al. 2016 Asian 147 28 66 53 0.585 129 19 97 13 0.477 <0.001 PB PCR 
Marini et al. 2016 Caucasian 330 119 159 52 0.398 360 148 154 58 0.375 0.097 HB Taqman 
Karas Kuzelicki et al. 2018 Caucasian 103 45 45 13 0.345 199 85 96 18 0.332 0.214 Mixed Taqman 
Rafik et al. 2019 Africa 52 44 0.077 182 97 74 11 0.264 0.526 PB PCR 
MTRR rs1801394  Total AA AG GG AF Total AA AG GG AF      
Brandalize et al. 2007 Caucasian 114 36 69 0.382 100 33 61 0.365 0.002 HB PCR 
Mostowska et al. 2010 Caucasian 164 31 81 52 0.564 166 34 70 62 0.584 0.089 PB PCR 
Aslar et al. 2014 Caucasian 100 14 72 14 0.500 125 13 107 0.468 <0.001 PB PCR 
Waltrick-Zambuzzi et al. 2015 Caucasian 342 95 194 53 0.439 401 136 193 72 0.420 0.806 HB Q-PCR 
Jiang et al. 2015 Asian 204 123 71 10 0.223 226 124 84 18 0.265 0.480 PB Sequenom 
Bezerra et al. 2015 Caucasian 140 98 37 0.168 175 112 60 0.189 0.111 PB PCR 
Murthy et al. 2015 Asian 123 42 81 0.329 141 65 76 0.270 <0.001 HB PCR 
Wang et al. 2016 Asian 147 71 26 50 0.429 129 29 59 41 0.547 0.380 PB PCR 
Marini et al. 2016 Caucasian 330 160 134 36 0.312 367 175 161 31 0.304 0.478 HB Sequenom 
Karas Kuzelicki et al. 2018 Caucasian 103 14 56 33 0.592 199 30 111 58 0.570 0.051 Mixed PCR 
TCN 2 rs1801198  Total CC CG GG AF Total CC CG GG AF      
Martinelli et al. 2006 Caucasian 218 85 110 23 0.358 289 89 150 50 0.433 0.330 PB PCR 
Mills et al. 2008 Caucasian 316 99 153 64 0.445 1097 347 532 218 0.441 0.243 HB Taqman 
Mostowska et al. 2010 Caucasian 163 46 88 29 0.448 181 48 103 30 0.450 0.044 HB Sequenom 
Jin et al. 2015 Asian 429 76 215 138 0.572 461 75 231 155 0.587 0.475 PB Sequenom 
Waltrick-Zambuzzi et al. 2015 Caucasian 359 139 160 60 0.390 440 179 199 62 0.367 0.576 Mixed Sequenom 
Marini et al. 2016 Caucasian 330 135 140 55 0.379 366 160 155 51 0.351 0.177 PB PCR 
BHMT rs3733890  Total GG GA AA AF Total GG GA AA AF      
Mostowska et al. 2010 Caucasian 174 95 76 0.236 176 82 75 19 0.321 0.766 HB PCR 
Hu et al. 2011 Asian 166 90 56 20 0.289 268 130 118 20 0.295 0.334 HB PCR 
Jin et al. 2015 Asian 481 219 202 60 0.335 554 265 245 44 0.301 0.222 PB PCR 
Marini et al. 2016 Caucasian 330 140 150 40 0.348 366 156 163 47 0.351 0.665 PB Sequenom 
Karas Kuzelicki et al. 2018 Caucasian 102 42 51 0.338 198 98 84 16 0.293 0.734 HB Q-PCR 

Note: AF, allele frequency of minor allele; HB, hospital based; HWE, Hardy–Weinberg equilibrium; PB, population based.

Associations between the four SNPs of folate pathway gene and NSCL/P in the overall population

The meta-analysis results showed that there was a significant association between rs1801133 and NSCL/P risk in two genetic models: TT genotype vs CC genotype (OR 1.333 95% CI=1.062–1.674, P= 0.013) and recessive model (OR=1.325 95%CI= 1.075–1.634, P= 0.008) (Table 3, Figures 3 and 4). There was no statistically significant association between rs1801394 of the MTRR, rs1801198 of the TCN2, rs3733890 of the BHMT and NSCL/P risk in the overall population (Tables 46).

Forest plot for pooled ORs for the associations between TT vs CC model of rs1801133 and NSCL/P risk

Figure 3
Forest plot for pooled ORs for the associations between TT vs CC model of rs1801133 and NSCL/P risk
Figure 3
Forest plot for pooled ORs for the associations between TT vs CC model of rs1801133 and NSCL/P risk

Forest plot for pooled ORs for the associations between recessive model of rs1801133 and NSCL/P risk

Figure 4
Forest plot for pooled ORs for the associations between recessive model of rs1801133 and NSCL/P risk
Figure 4
Forest plot for pooled ORs for the associations between recessive model of rs1801133 and NSCL/P risk
Table 3
Association between the rs1801133 (CC/CT/TT*) and NSCL/P
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
T allele vs C allele 
  Overall 73.1 1.111 (0.992–1.244) 0.069 0.820 random 
  Caucasian 58.5 0.001 1.085 (0.976–1.206) 0.131 0.361 random 
  Asian 79.1 1.244 (0.961–1.611) 0.098 0.438 random 
TT vs CC 
  Overall 62.6 1.333 (1.062–1.674) 0.013# 0.102 random 
  Caucasian 56.2 0.001 1.230 (0.976–1.551) 0.080 0.239 random 
  Asian 70.5 0.001 1.701 (0.949–3.049) 0.075 0.365 random 
CT vs CC 
  Overall 57.6 1.026 (0.901–1.169) 0.696 0.587 random 
  Caucasian 40.3 0.033 1.027 (0.904–1.166) 0.686 0.287 random 
  Asian 62.4 0.006 1.081 (0.821–1.422) 0.580 0.041 random 
Dominant model 
  Overall 66.2 1.075 (0.936–1.234) 0.305 0.804 random 
  Caucasian 52.8 0.003 1.067 (0.932–1.223) 0.347 0.208 random 
  Asian 67.9 0.002 1.172 (0.883–1.557) 0.272 0.089 random 
Recessive model 
  Overall 62.5 1.325 (1.075–1.634) 0.008# 0.220 random 
  Caucasian 40.9 0.030 1.190 (0.989–1.433) 0.066 0.434 random 
  Asian 79.1 1.737 (0.940–3.210) 0.078 0.404 random 
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
T allele vs C allele 
  Overall 73.1 1.111 (0.992–1.244) 0.069 0.820 random 
  Caucasian 58.5 0.001 1.085 (0.976–1.206) 0.131 0.361 random 
  Asian 79.1 1.244 (0.961–1.611) 0.098 0.438 random 
TT vs CC 
  Overall 62.6 1.333 (1.062–1.674) 0.013# 0.102 random 
  Caucasian 56.2 0.001 1.230 (0.976–1.551) 0.080 0.239 random 
  Asian 70.5 0.001 1.701 (0.949–3.049) 0.075 0.365 random 
CT vs CC 
  Overall 57.6 1.026 (0.901–1.169) 0.696 0.587 random 
  Caucasian 40.3 0.033 1.027 (0.904–1.166) 0.686 0.287 random 
  Asian 62.4 0.006 1.081 (0.821–1.422) 0.580 0.041 random 
Dominant model 
  Overall 66.2 1.075 (0.936–1.234) 0.305 0.804 random 
  Caucasian 52.8 0.003 1.067 (0.932–1.223) 0.347 0.208 random 
  Asian 67.9 0.002 1.172 (0.883–1.557) 0.272 0.089 random 
Recessive model 
  Overall 62.5 1.325 (1.075–1.634) 0.008# 0.220 random 
  Caucasian 40.9 0.030 1.190 (0.989–1.433) 0.066 0.434 random 
  Asian 79.1 1.737 (0.940–3.210) 0.078 0.404 random 

Note: *wild homozygote (CC), heterozygote (CT), mutation (TT); # indicates statistically significance.

Table 4
Association between rs1801394 (AA/AG/GG*) and NSCL/P
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
G allele vs A allele 
  Overall 36.0 0.120 0.986 (0.895–1.085) 0.766 0.713 fixed 
  Caucasian 0.938 1.037 (0.928–1.158) 0.520 0.401 fixed 
  Asian 77.7 0.011 0.863 (0.569–1.309) 0.488 0.480 random 
GG vs AA 
  Overall 30.7 0.172 0.977 (0.781–1.223) 0.841 0.405 fixed 
  Caucasian 0.816 1.176 (0.909–1.520) 0.217 0.218 fixed 
  Asian 0.820 0.520 (0.321–0.841) 0.008# — fixed 
AG vs AA 
  Overall 78.3 0.879 (0.630–1.227) 0.449 0.374 random 
  Caucasian 31.9 0.184 1.037 (0.872–1.234) 0.679 0.443 fixed 
  Asian 93.2 0.644 (0.211–1.968) 0.441 0.599 random 
Dominant model 
  Overall 69.4 0.001 0.921 (0.704–1.205) 0.550 0.586 random 
  Caucasian 0.514 1.047 (0.886–1.236) 0.591 0.353 fixed 
  Asian 90.3 0.746 (0.351–1.768) 0.506 0.908 random 
Recessive model 
  Overall 37.8 0.117 1.032 (0.853–1.248) 0.748 0.164 fixed 
  Caucasian 45.1 0.090 1.062 (0.858–1.315) 0.581 0.073 fixed 
  Asian 39.6 0.198 0.922 (0.605–1.406) 0.707 — fixed 
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
G allele vs A allele 
  Overall 36.0 0.120 0.986 (0.895–1.085) 0.766 0.713 fixed 
  Caucasian 0.938 1.037 (0.928–1.158) 0.520 0.401 fixed 
  Asian 77.7 0.011 0.863 (0.569–1.309) 0.488 0.480 random 
GG vs AA 
  Overall 30.7 0.172 0.977 (0.781–1.223) 0.841 0.405 fixed 
  Caucasian 0.816 1.176 (0.909–1.520) 0.217 0.218 fixed 
  Asian 0.820 0.520 (0.321–0.841) 0.008# — fixed 
AG vs AA 
  Overall 78.3 0.879 (0.630–1.227) 0.449 0.374 random 
  Caucasian 31.9 0.184 1.037 (0.872–1.234) 0.679 0.443 fixed 
  Asian 93.2 0.644 (0.211–1.968) 0.441 0.599 random 
Dominant model 
  Overall 69.4 0.001 0.921 (0.704–1.205) 0.550 0.586 random 
  Caucasian 0.514 1.047 (0.886–1.236) 0.591 0.353 fixed 
  Asian 90.3 0.746 (0.351–1.768) 0.506 0.908 random 
Recessive model 
  Overall 37.8 0.117 1.032 (0.853–1.248) 0.748 0.164 fixed 
  Caucasian 45.1 0.090 1.062 (0.858–1.315) 0.581 0.073 fixed 
  Asian 39.6 0.198 0.922 (0.605–1.406) 0.707 — fixed 

Note: *wild homozygote (AA), heterozygote (AG), mutation (GG); #indicates statistically significance.

Table 5
Association between rs1801198 (CC/CG/GG*) and NSCL/P
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
G allele vs C allele 38.8 0.147 0.990 (0.907–1.080) 0.821 0.541 fixed 
GG vs CC 43.4 0.116 0.987 (0.824–1.181) 0.883 0.438 fixed 
CG vs CC 0.820 0.966 (0.840–1.112) 0.631 0.196 fixed 
Dominant model 0.428 0.976 (0.855–1.114) 0.717 0.248 fixed 
Recessive model 27.4 0.229 1.002 (0.860–1.167) 0.982 0.747 fixed 
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
G allele vs C allele 38.8 0.147 0.990 (0.907–1.080) 0.821 0.541 fixed 
GG vs CC 43.4 0.116 0.987 (0.824–1.181) 0.883 0.438 fixed 
CG vs CC 0.820 0.966 (0.840–1.112) 0.631 0.196 fixed 
Dominant model 0.428 0.976 (0.855–1.114) 0.717 0.248 fixed 
Recessive model 27.4 0.229 1.002 (0.860–1.167) 0.982 0.747 fixed 

Note: *wild homozygote (CC), heterozygote (CG), mutation (GG).

Table 6
Association between rs3733890 (GG/GA/AA*) and NSCL/P
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
A allele vs G allele 61.0 0.036 0.994 (0.820–1.204) 0.948 0.409 random 
AA vs GG 73.6 0.004 0.993 (0.558–1.764) 0.980 0.182 random 
GA vs GG 23.7 0.264 0.968 (0.826–1.133) 0.685 0.961 fixed 
Dominant model 33.2 0.200 0.994 (0.856–1.155) 0.940 0.709 fixed 
Recessive model 75.1 0.003 1.002 (0.569–1.767) 0.994 0.192 random 
Genetic modelI2 (%)P for heterogeneityOR (95% CI)P valueP for publication biasEffects model
A allele vs G allele 61.0 0.036 0.994 (0.820–1.204) 0.948 0.409 random 
AA vs GG 73.6 0.004 0.993 (0.558–1.764) 0.980 0.182 random 
GA vs GG 23.7 0.264 0.968 (0.826–1.133) 0.685 0.961 fixed 
Dominant model 33.2 0.200 0.994 (0.856–1.155) 0.940 0.709 fixed 
Recessive model 75.1 0.003 1.002 (0.569–1.767) 0.994 0.192 random 

Note: *wild homozygote (GG), heterozygote (GA), mutation (AA).

Subgroup analysis

To decrease the heterogeneity, and a subgroup analysis was conducted according to genetic backgroud (i) Asian and (ii) Caucasian. The results showed that there was a significant association between rs1801394 and NSCL/P risk in Asian (GG genotype vs AA genotype, OR=0.520 95% CI=0.321–0.841, P= 0.008), but no associations in Caucasian (Table 4), which confers a protective role of GG genotype in Asian.

Meta-regression and influence analysis

Publication year, sample size and HWE were considered as covariates for meta-regression. The results showed that the above factors have no influence on the results (P >0.05). To avoid one single study affected the overall OR estimates, one-way sensitivity analysis was performed. The results showed that no study was found to exert an excessive influence on the pooled effect.

Publication bias

There was publication bias for rs1801133 in the Asian population in genotype model CT vs CC (Table 3). Trim and fill results showed that the adjusted risk estimate unchanged, which confirmed that the results of present study are statistically reliable.

Discussion

NSCL/P is a multifactorial disease caused by genetic and environmental factors. In previous years, various genomic susceptibility regions have been identified in association studies, linkage studies, family sequencing studies, and animal experiments suggesting that gene mutations influence the development of maxillofacial area. However, the underlying biological mechanisms remain unclear.

Folic acid is an important factor that influences the metabolism and the synthesis of nucleotides and amino acids. Previous studies have suggested that folic acid plays an important role in decreasing the risk of NSCL/P [2,3]. Folic acid metabolism is a complex process and many genes are involved in the pathway, such as MTHFR, MTRR, TCN2, and BHMT. However, there are no consistent results regarding the association between the genetic variations of these genes and NSCL/P in different populations. To clarify these inconsistent results, we carried out the meta-analysis in this study.

The present meta-analysis results demonstrated a significant association between rs1801133 and NSCL/P risk in two genetic models: TT genotype vs CC genotype (OR=1.333 95% CI=1.062–1.674, P= 0.013) and recessive model (OR=1.325 95%CI = 1.075–1.634, P = 0.008). There were a significant protective association between rs1801394 GG genotype and NSCL/P in Asian (GG genotype vs AA genotype, OR=0.520 95% CI=0.321–0.841, P= 0.008).

TCN2, encode transcobalamin2, transports vitamin B12 to cells, have been reported to be associated with multiple diseases, such as cancer, Alzheimer and other congenital abnormalities [44–46]. In 2006, Martinelli et al. found that the C776G in TCN2 was associated with risk of cleft lip, but subsequent studies didn't get the significant results [10]. Similarly, the present study didn't find the significant association between the C776G and NSCL/P.

BHMT, a zinc dependent cytosolic enzyme, is important for homocysteine metabolism and methionine synthesis. In 2010, Mostowska et al. first found that rs3733890 of the BHMT was associated with NSCL/P, and other studies also indicated its association with coronary artery disease and neural tube defects [23]. In the present study, we found no evidence showing rs3733890 playing any significant role [23]. We inferred several factors may contribute to the result. First, we found a relative high value of heterogeneity among studies, which cause a different distribution of genotype. Second, the number of included studies and sample size are relatively small. So, the subgroup analysis was not conducted based on ethnicity.

MTRR plays a vital role in functional regeneration of methionine synthase, and it may be associated with increasing the congenital heart disease risk [18]. But the meta-analysis conducted by Zhang et al. in 2013 and Lei et al. in 2018 showed no association between rs1801394 and the risk of NSCL/P [47,48]. In the present study, we found a significant protective association between rs1801394 GG genotype and the NSCL/P risk in Asian, but no association in Caucasian. Considering the different background, we also summarized the data from 1000 genomes and ExAC database (S-Table 1), and we found the allelic frequencies vary in different background groups, and no significant association study between rs1801394 and the NSCL/P was found in Caucasian [6,14,25]. However, the sample size of the MTRR analysis is a limitation, and the present study did not consider the possibility of linkage disequilibrium, so further well-designed studies are required to establish these findings.

MTHFR is an important enzyme in homocysteine metabolism and C677T rs1801133 is one of the most important functional polymorphisms. Prediction by bioinformatics tools showed that the change of genetic variant will influence the protein function and predispose to cause the disease (Table 1). The allelic frequencies vary in different ethnic groups and the minor allele frequency (MAF) of MTHFR rs1801133 in Asian are lower than that in European and American, so it is very valuable to summarize and analyze by systematic statistical methods. In 1998, Tolarava found TT genotype of rs1801133 increase the risk of CL/P, later on, several studies also found the associations between rs1801133 and NSCL/P in different population [19,29,34]. However, there were several studies failed to find association between rs1801133 and the risk of NSCL/P [38,41]. In the present study, we included 30 studies including 5517 cases and 7770 controls and found TT genotype can increase the risk of NSCL/P.

The strength of this meta-analysis is that it expands to a large number of related studies, and the most updated publications were included. A strict procedure for search strategy, literature inclusion, data extraction, and quality assessment by two researchers was performed to guarantee the quality. Meta-regression and sensitivity analysis were also performed to strengthen the conclusions. We confirmed the previous investigation by summarizing a larger number of closely related studies.

There are some limitations in the present meta-analysis. First, studies published only in English were included in the meta-analysis, and studies published in other languages were excluded. Second, environmental factors also contribute to NSCL/P, and in the present study, non-genetic factors and other potential interactions such as age, sex, folate level were not included in the analysis due to insufficient information.

Conclusion

In the present study, we successfully identified rs1801133 in MTHFR is associated with the increasing risk of NSCL/P, and GG of rs1801394 in MTRR confers a protective role in Asian. Further well-designed studies are required to establish these findings.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported by the National Key Research and Development Program [grant number #2016YFC1000504 (to S.F.)].

Author Contribution

All authors have contributed to the paper. Q.L., L.X., W.S., and S.F. conceived and designed the study; Q.L., L.X., X.J., K.S., and T.Z. extracted and analyzed the data. Q.L., L.X., X.J., K.S., T.Z., W.S., and S.F. drafted the manuscript. All authors revised and approved the final draft.

Data Availability

All the data in the present research is contained in this manuscript.

Acknowledgements

Thank you for the invaluable devotion of all participants in this study.

Abbreviations

     
  • BHMT

    betaine-homocysteine methyltransferase

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • MAF

    minor allele frequency

  •  
  • MTHFR

    methylenetetrahydrofolate reductase

  •  
  • MTRR

    methionine synthase reductase

  •  
  • NSCL/P

    nonsyndromic cleft lip with or without cleft palate

  •  
  • TCN2

    transcobalamin

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Author notes

*

These authors contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data