Abstract

Introduction: recurrent implantation failure (RIF) is the most common cause of pregnancy failure after in vitro fertilization (IVF). Among the different causes of RIF, the role of maternal genetics factors is as one of most important factors. P53 codon 72 polymorphism is one of the notable polymorphisms in this field. According to evidences about the association between P53 polymorphism and increased risk of RIF in different population, if this relationship be justified in Iranian population, this polymorphism can be used to predict IVF success rate. This study was performed with aim to evaluate the association between this polymorphism and incidence of RIF. Methods: This case-control study was performed on 80 infertile couples in Mashhad Montaserieh Infertility Treatment Center in 2010. The infertile couples were divided into three groups of control, RIF-1, and RIF-2. The control group included 40 women who were successfully pregnant after IVF. The patient group 1 were 20 women who no type of pregnancy was observed after two IVF, and patient group 2 were 20 women who failed to be pregnant after at least three IVF. P53 gene polymorphism genotype (Arg72pro allele) was determined using PCR- ARMS technique. Statistical analysis was performed by SPSS software (version 16) and Pearson Chi-Square test. P<0.05 was considered significant. Results: The frequency of Arg/Pro genotype in control group, patient group 1, patient group 2 and total patients was 92.5, 75, 80 and 77.5. The frequency of Arg/Arg genotype in these groups was 7.5, 25, 20 and 22.5, respectively. Pro/Pro genotype was observed in none of the groups. According to Pearson Chi-square test results, distribution of genotypes was not significantly different between patient group 1 and patient group 2 and also total patients in comparison with control group. Conclusion: According to the results of present study in the studied population, there is no association between P53 codon 72 polymorphism and occurrence of RIF. So this polymorphism cannot be used to predict the risk of RIF. © 2015, Mashhad University of Medical Sciences. All rights reserved.