Strong association of 677 C>T substitution in the MTHFR gene with male infertility--a study on an indian population and a meta-analysis

Abstract

Background: Methylenetetrahydrofolate reductase (MTHFR) is an important enzyme of folate and methionine metabolism, making it crucial for DNA synthesis and methylation. The objective of this study was to analyze MTHFR gene 677C>T polymorphism in infertile male individuals from North India, followed by a meta-analysis on our data and published studies.

Methodology/principal findings: We undertook genotyping on a total of 837 individuals including well characterized infertile (N = 522) and confirmed fertile (N = 315) individuals. The SNP was typed by direct DNA sequencing. Chi square test was done for statistical analysis. Published studies were searched using appropriate keywords. Source of data collection for meta-analysis included 'Pubmed', 'Ovid' and 'Google Scholar'. Those studies analyzing 677C>T polymorphism in male infertility and presenting all relevant data were included in meta-analysis. The genotype data for infertile subjects and fertile controls was extracted from each study. Chi square test was done to obtain odds ratio (OR) and p-value. Meta-analysis was performed using Comprehensive Meta-analysis software (Version 2). The frequency of mutant (T) allele (p = 0.0025) and genotypes (CT+TT) (p = 0.0187) was significantly higher in infertile individuals in comparison to fertile controls in our case-control study. The overall summary estimate (OR) for allele and genotype meta-analysis were 1.304 (p = 0.000), 1.310 (p = 0.000), respectively, establishing significant association of 677C>T polymorphism with male infertility.

Conclusions/significance: 677C>T substitution associated strongly with male infertility in Indian population. Allele and genotype meta-analysis also supported its strong correlation with male infertility, thus establishing it as a risk factor.

Figure 1. Allele and genotype distribution.

Bar diagram showing distribution of allele and genotypes in control and different groups of infertile individuals in our population.

Figure 2. Forest plot.

Meta-analysis using allele frequency (a), cumulative allele meta-analysis using fixed effect (b) and random effect models (c).

Figure 3. Forest plot.

Group-wise allele meta-analysis for azoospermia (a), oligozoospermia (b) and oligoasthenoteratozoospermia (c).

Figure 4. Forest plot.

Meta-analysis using genotype frequency (a), cumulative genotype meta-analysis using fixed effect (b) and random effect models (c).

Figure 5. Forest plot.

Group-wise genotype meta-analysis for azoospermia (a), oligozoospermia (b) and oligoasthenoteratozoospermia (c).

Figure 6. Publication bias (Allele meta-analysis).

Funnel plot of standard error by log odds ratio using fixed model for observed set of studies (a) and after imputation (b). Funnel plot of precision by log odds ratio using fixed model for observed set of studies (c) and after imputation (d). Funnel plot of standard error by log odds ratio using random effect model for observed set of studies (e) and after imputation (f). Funnel plot of precision by log odds ratio using random effect model for observed set of studies (g) and after imputation (h).

Figure 7. Publication bias (Genotype meta-analysis).

Funnel plot of standard error by log odds ratio using fixed model for observed set of studies (a) and after imputation (b). Funnel plot of precision by log odds ratio using fixed model for observed set of studies (c) and after imputation (d). Funnel plot of standard error by log odds ratio using random effect model for observed set of studies (e) and after imputation (f). Funnel plot of precision by log odds ratio using random effect model for observed set of studies (g) and after imputation (h).