FSHR gene polymorphisms affect the ovarian response to rFSH stimulation in Egyptian patients undergoing ARTs: a step toward individualized medicine : Medical Research Journal

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FSHR gene polymorphisms affect the ovarian response to rFSH stimulation in Egyptian patients undergoing ARTs

a step toward individualized medicine

EL-Garf, Waela; Salem, Sondosa; EL-Nouri, Amrb; Salama, Sameha; Mohamady, Mohammedc; Bibers, Mamdouha; Taha, Tamera; Azmy, Osamaa

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Medical Research Journal 13(2):p 61-67, December 2014. | DOI: 10.1097/01.MJX.0000457181.82015.bd
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ollicle-stimulating hormone (FSH) plays a central role in establishing and maintaining human fertility. Circulating FSH stimulates gametogenesis and steroidogenesis in gonads by binding to its receptor [follicle-stimulating hormone receptor (FSHR)] 1. The FSHR gene is localized on chromosome 2p21 and possesses a large number of single-nucleotide polymorphisms (SNPs). SNPs mean single-letter mutations – that is, a single-base mutation that substitutes one nucleotide for another resulting in polymorphism 2. More than 19 million SNPs have been identified in the human genome. Most SNPs seem to have no apparent effect on gene function. However, some SNPs have a profound impact on the function of associated genes, causing significant changes in drug efficacy and drug disposition 3. It is well known that there is individual variation in drug response; one aspect that can influence the effectiveness of therapies in patients during drug-based treatment are specific genetic variants 4. SNPs have been increasingly recognized as a possible mechanism of interindividual variation in drug response 5,6. However, for certain drugs genetic factors can account for up to 95% of interindividual variability in drug disposition and effect 7. The idea that genetic variability between patients might influence the response to drugs was described and termed pharmacogenetics by Vogel 8. Pharmacogenomics is the branch of pharmacology that deals with the influence of genetic variation on drug response in patients by correlating gene expression or SNPs with a drug’s efficacy or toxicity 9. About 60–90% of the individual variation of drug response depends on pharmacogenomic factors 10. By studying correlations between gene expression or SNPs and the efficacy or toxicity of a drug, pharmacogenomics aims to identify the inherited basis for interindividual differences in drug response and translate this to molecular diagnostics that can be used to individualize drug therapy. The ultimate goal is to provide new strategies for optimizing drug therapy 9. FSH polymorphisms exhibit a potential for pharmacogenetic applications in selecting appropriate treatment options in conditions that require or benefit from FSH therapy 10. Such approaches promise the advent of ‘personalized medicine’ in which drugs and drug combinations are tailored to each individual’s unique genetic makeup. SNPs in the FSHR gene have received a great deal of attention as they affect ovarian response to FSH in women undergoing assisted reproduction techniques (ARTs). In fact, the importance of mutations in the FSHR gene in ovarian response has been confirmed by various well-designed clinical studies 11–13 and reviewed in depth elsewhere 14–17.

The FSHR SNPs at nucleotide position 919 and 2039 in exon 10 are very common and result in the amino acid transition Thr/Ala at codon 307 and Asn/Ser at codon 680, respectively. The SNP in position 680 (Asp680Ser) in the amino acid chain has been correlated with an altered response to FSH 18. Studies have suggested that variation in the response to FSH is related to the fact that women with ovarian dysfunction tend to carry the Ser/Ser allelic variant, whereas good responders more often carry the Asn/Ser allelic variant, which has a higher FSH sensitivity 19.

The aim of the present study was to evaluate the role of FSHR gene polymorphism as a predictor of ovarian response to stimulation with gonadotropins, with a review of the literature on genetic screening that could provide specific information about a woman’s reproductive system that would not be accurately predicted by age or hormonal or functional biomarkers.


This is a prospective study including 150 infertile women who were attending the National Research Center infertility clinic during the period from March 2012 to December 2013. Women recruited for this study were younger than 40 years and had been diagnosed with infertility due to tubal and/or male factors, or due to unexplained factors. Women having polycystic ovary syndrome, endometriosis, or a previous history of ovarian surgery were excluded from the study. The patients were divided into two groups depending on the ovarian response to gonadotrophin stimulation during ARTs. Informed consent was obtained from all participants. The Ethical Committees of the National Research Centre approved the study.

Group 1 (poor responders) included 75 patients who had had their in-vitro fertilization (IVF) cycle cancelled because of poor ovarian response. Women were selected according to a consensus that was reached in 2011 by the European Society for Human Reproduction and Embryology (ESHRE) that resulted in the drafting of the ‘Bologna criteria’ on the minimal criteria needed to define poor ovarian response. Thus, to define a poor ovarian response to controlled ovarian hyperstimulation, the following two criteria had to be present:

  • A previous poor ovarian response (≤3 oocytes with a conventional stimulation protocol).
  • An abnormal ovarian reserve test (ORT) [i.e. antral follicle count (AFC)<5–7 follicles or anti-Müllerian hormone (AMH)<0.5–1.1 ng/ml].

Group II consisted of 75 patients (control group or good responders) with good ovarian response (more than five follicles and/or oocytes) after controlled conventional ovarian stimulation. Relevant clinical (age, cause of infertility), hormonal (FSH, E2, AMH), and sonographic data (AFC) were collected retrospectively from the women’s medical record.

For all patients, basal FSH level (on the third day of the menstrual cycle) with AFC was measured in a previous cycle. For controlled ovarian hyperstimulation, all patients underwent a GnRH agonist protocol. Recombinant FSH was used to induce ovulation according to the setup protocol. The protocol consisted of daily subcutaneous injection of a highly purified GnRH agonist (0.1 mg of Decapeptyl; Ferring Gmph, Kiel, Germany) started on day 21 of the cycle preceding the stimulation cycle and continued until downregulation of pituitary ovarian axis was achieved. This was detected by ultrasonography in the form of thin endometrium (<5 mm) without ovarian activity and E2 less than 40 pg/ml. Recombinant FSH (Puregon; NV Oragnon, Oss, the Netherlands) was administrated at a dose of 150–225 IU/day started on day 2 of treatment and continued daily. Patient response was achieved with an ultrasound and evaluation of E2 level 6 days after stimulation, followed by an ultrasound on alternating days and evaluation of E2 level on the day of ovulation triggering. Triggering was done using 10 000 IU of human chorionic gonadoptropin (HCG) (Pregnyl; NV Organon) when at least two leading follicles between 18 and 20 mm were detected by vaginal ultrasound. In cases of insufficient follicular growth, FSH dosage was increased gradually with a maximum dose of 450 IU/day. The FSH starting dose was chosen according to the patient’s previous or anticipated response, age, basal FSH, AFC, and BMI. The patients were then classified according to ovarian response into poor responders (producing ≤3 follicles) and good responders (producing 4–15 follicles). Transvaginal ultrasound-guided collection of oocytes was performed 34–36 h after HCG administration. The luteal phase was supported by 400 mg cyclogest (Actavis, Devon, UK) administered vaginally once daily started on the day of follicular retrieval.


A venous blood sample of 5 ml was taken, and DNA was isolated from peripheral blood lymphocytes using a QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions and stored at 4°C. Analysis of the FSHR gene polymorphism at position 680 was carried out using predesigned TaqMan allelic discrimination assays (rs 6166; Life Technologies Corporation, Carlsbad, California, USA). Real-time PCR was performed using the 7900 system from Applied Biosystems (Carlsbad, California, USA) following the manufacturer’s instructions. The analysis was carried out in accordance with the instructions for the device used.

Statistical analysis

IBM SPSS statistics (V. 22.0 2013; IBM Corp., Armonk, New York, USA) was used for data analysis. Data were expressed as both number and percentage for categorized data. The χ2-test was used to study the association between two variables or between two independent groups as regards the categorized data. We also calculated relative risk assessments (relative risk ratio) and presented them as absolute figures and as a standard error of estimate. The Hardy–Weinberg model was used. Comparison of quantitative data between two groups was made using an unpaired independent sample t-test; P value less than 0.05 was considered significant.


Genotype frequency distributions

All women included in this study were categorized as poor responders (≤3 ovarian follicles) or good responders (>3 ovarian follicles) following controlled ovarian stimulation according to ESHRE guidelines. The FSHR polymorphism N680S was studied in all cases and controls. In total, the results indicated that 9/150 had the Ser/Ser genotype (6%), 74/150 (49.3%) had the Ser/Asn genotype, and 67/150 (44.7%) had the Asn/Asn genotype. The genotype frequencies were consistent with the Hardy–Weinberg equilibrium, which showed that the relative proportions of each genotype remained constant with frequencies of p2, 2pq, and q2; that is, the population is said to be in a state of Hardy–Weinberg equilibrium for that particular genotype. It was found that among poor responders (n=75) 34.7% were Ser/Asn and 65.3% were Asn/Asn, whereas among good responders (n=75) 12% were Ser/Ser, 64% were Ser/Asn, and 24% were Asn/Asn, which means that the highest Asn/Asn percentage was found among poor responders and the highest Ser/Asn percentage was detected among good responders, as shown in Table 1. Distribution of the Asn allele versus the Ser allele in poor and good responders was 65.3 and 34.7%, respectively, whereas among good responders 76% were Ser and 24% were Asn, which indicated that the highest Asn percentage was among poor responders and the highest Ser percentage was among good responders. The frequency of Asn among poor responders was 5.97 times compared with that among good responders.

Table 1:
Distribution of genotype in the two groups

Ovarian stimulation compared between genotypes

The general and clinical characteristics of the studied genotypes for the FSHR 680 polymorphism showed no differences with respect to age. Among patients with the Asn/Asn genotype, the mean age of poor responders was 33.2±2.9 years and that of good responders was 29.2±2.9 years (P>0.05). Among patients with the Asn/Ser genotype the mean age of poor responders was 31.7±2.5 and that of good responders was 32.5±3.7 years (P>0.05), whereas for the Ser/Ser genotype the mean age of good responders was 28.3±3.2 years. No differences were reported in FSH level between the genotypes. Among poor responders the FSH level was 8.5±3.2 and 8.4±3.1 IU/ml, respectively (P>0.05), for those with the Asn/Asn and Asn/Ser genotypes, and among the good responders the levels were 6.3±2.5 and 6.0±1.6 IU/ml, respectively. Statistical differences were reported between the genotypes with respect to AFC. The AFC was 5.2±1.5 follicles for poor responders with the Asn/Asn phenotype and 5.9±3.6 follicles for those with the Asn/Ser phenotype. In the good responder group the AFC was 7.8±3.6 for those with the Asn/Asn phenotype and 7.2±1.3 for those with the Asn/Ser phenotype (P<0.05). A significant difference was detected between poor responders and good responders with the Asn/Asn genotype with respect to the number of ampoules of exogenous FSH given: 49.0±3.0 and 36.0±1.5 ampoules, respectively (P<0.05). Number of days of induction was longer in poor responders (14.33±1.72 days) compared with good responders (12.32±2.52 days) (P<0.05). The levels of estradiol on the day of HCG administration (pg/ml) were significantly different between poor responders (523±25.4 pg/ml) and good responders (1125±35.5 pg/ml) (P<0.005). The number of follicles detected by ultrasound in poor responders was 4.2±3.77 follicles and that in good responders was 10.87±8.86 follicles (P<0.05).

When correlating the Asn/Ser genotype with the FSHR 680 polymorphism, differences were detected in doses of exogenous FSH (IU): women from the poor responder group required significantly more gonadotropin (43±2.5 ampoules) compared with good responders (34±2.1 ampoules) (P<0.05). The number of days of stimulation for poor responder women was also higher (13.35±2.1 days) compared with good responders (12.25±1.3 days) (P<0.001). With respect to the number of follicles in the two studied groups, poor responder women had 4.7±4.1 follicles compared with 13.72±5.2 follicles in good responders (P<0.05), as shown in Table 2.

Table 2:
Comparison between poor and good response as regards different studied genotypes


The term ovarian reserve aims to correlate reproductive potential with the number and quality of remaining oocytes in women of reproductive age 20. Ovarian reserve is currently defined as the number and quality of follicles left in the ovary at any given time 21,22. It is also defined as an estimate of oocytes remaining in the ovary that are capable of fertilization resulting in a healthy and successful pregnancy 23. Ovarian reserve testing aims to quantify this relationship by measuring either oocyte quality, quantity, or the ability of an individual to achieve pregnancy, either through biochemical measures or through ultrasound imaging of the ovary 24. They were expected to help identify individuals who would be unlikely to achieve pregnancy through ARTs 20. The first indicator of ovarian reserve that was taken into account was the patient’s age. However, fertility varies significantly among women of similar age, and therefore they might exhibit different responses to ovarian stimulation 25. Consequently, an individual’s chronological age may not be as valuable a predictor of fertility as her ‘biological age’, as defined by hormonal and functional profiles 26. In fact, in addition to age, several clinical, endocrine (FSH, estradiol and inhibins and AMH), and ultrasound markers (three-dimensional assessment of ACF–AFC–ovarian volume), combined ORTs, and dynamic tests have been proposed for the prediction of ovarian response to stimulation 27. According to a recent study, neither basal hormone measurements nor such dynamic tests provide direct information concerning the responsiveness of the ovaries to exogenous gonadotropins used in ovarian stimulation for assisted reproductive treatment 28.

Although at present there is no ideal ORT reflecting the fertility potential of a woman reliably, there is mounting evidence that AMH and AFC are promising markers for decreased or diminished ovarian reserve 20,29,30, with superiority being controversial: AMH being superior to AFC in most studies 29,31 and AFC being a better predictor compared with AMH in others. In contrast, there is emerging evidence to suggest that a low AMH (undetectable) has high specificity for poor ovarian response but there is insufficient evidence to support its use as a screening test for failure to conceive 20.

Other parameters have been proposed recently: for example, ovarian response prediction index (ORPI) 32, calculated by multiplying the AMH level (ng/ml) by the number of antral follicles (2–9 mm) and dividing the result by the age (years) of the patient [ORPI=(AMH×AFC)/patient age]. The authors concluded that ORPI might be used to improve the cost–benefit ratio of ovarian stimulation regimens by guiding the selection of medications and by modulating the doses and regimens according to the actual needs of the patients. Another parameter is the FSH/luteinizing hormone ratio, which has been found to be a useful marker of ovarian reserve 24. The success of ARTs depends on the number and quality of mature oocytes retrieved after controlled ovarian stimulation 33. Obtaining an adequate number of high-quality oocytes in women with poor ovarian response is a major challenge in controlled ovarian hyperstimulation. Efforts have been made to optimize protocols for controlled ovarian stimulation to provide adequate numbers of good-quality oocytes, which is the cornerstone of assisted reproductive techniques. ORTs were expected to help exclude these couples from ART, thereby reducing healthcare costs, futile medical treatment, risks of surgical procedures, and negative psychological impacts. However, to be able to successfully achieve this, an ORT needs to have a high specificity and predictive value, as well as reproducibility 34. Clinically, there is a need to identify women of relatively young age with reduced ovarian reserve as well as women whose fertility is naturally impaired by age who may still have satisfactory ovarian potential 2. For ovarian stimulation in IVF cycles, different protocols have been developed to induce multifollicular development, which increases the number of available oocytes and thereby the number of embryos for selection and transfer. The Cochrane Database of Systematic Reviews is regularly published to compare different protocols but often fail to demonstrate the purported superiority of the new approaches 35–38. In contrast, new protocols for infertility treatment and ARTs are greatly needed, especially considering the constantly increasing age of women among couples undergoing ARTs 39, a factor that decreases the efficacy of treatment by affecting both pregnancy and abortion rates. In such a scenario, pharmacogenetic approaches are appealing and have been proposed 40. Research over the last two decades has revealed the role of common genetic variants in the determination of individual serum hormone levels and target organ response 41.

For the foregoing reasons and in an attempt to assess the potential of the FSH polymorphism for predicting ovarian response to FSH stimulation, we retrospectively analyzed the clinical data of 150 women younger than 40 years considered as poor ovarian responders according to the ‘Bologna criteria’ who were undergoing IVF at the National Research Center infertility clinic in Egypt. In addition to the hormonal status, these women were genotyped, and analysis of FSHR gene polymorphism at position 680 was carried out. It was found that genotype 2039G/A (Asn680/Ser680) of FSHR is a good predictor of ovarian response upon controlled FSH stimulation. Our results agree with those obtained by the majority of studies on the subject 42–44. However, there is discrepancy between our results and some others – for example, in the study by Laven et al. 45, normogonadotropic anovulatory infertile women with the Ser/Ser 680 polymorphism presented with higher median FSH serum compared with those with the Asn/Asn 680 and Asn/Ser 680 variants. However, ovarian responsiveness to FSH was similar among anovulatory women with the various polymorphisms. This may be due to bias in that study 46.

There is discrepancy between our results and those obtained in a study conducted in 2013 by Mohiyiddeen et al.47. In their study, 421 women undergoing their first cycle of controlled ovarian stimulation for IVF were compared with 83 healthy, ethnically matched controls with respect to the Ser680Asn polymorphism. The FSHR p.Asn680Ser genotype frequencies were similar in IVF patients and controls.

Also, Van Disseldorp et al.48 found no statistically significant difference in the distribution of genotypes between the groups with poor and normal ovarian response. Thus, they concluded that FSHR p.Asn680Ser was not predictive of ovarian response; however, the authors could not rule out clinically relevant differences.


In the light of actual knowledge, the FSHR genotype may be useful in predicting response and/or deciding the FSH starting dose. This would result in personalized ovarian stimulation with lower incidence of side effects (e.g. ovarian hyperstimulation syndrome) and reduced cost. By considering hormonal (FSH and AMH), functional (AFC), and genetic biomarkers in combination, a complete picture of a patient’s overall reproductive status can be formulated to provide a basis for designing an optimal treatment plan.



Conflicts of interest

There are no conflicts of interest.


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