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Hum. Reprod. Advance Access published online on October 14, 2008
Human Reproduction, doi:10.1093/humrep/den353
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© The Author 2008. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Variants in the ACVR1 gene are associated with AMH levels in women with polycystic ovary syndrome

Marlies E. Kevenaar1, Axel P.N. Themmen1, Anke J. van Kerkwijk1, Olivier Valkenburg2, André G. Uitterlinden1,3,4, Frank H. de Jong1, Joop S.E. Laven2 and Jenny A. Visser1,5

1 Department of Internal Medicine, Erasmus MC, PO Box 2040, 3000 CA Rotterdam, The Netherlands 2 Department of Obstetrics and Gynaecology, Division of Reproductive Medicine, Erasmus MC, 3000 CA Rotterdam, The Netherlands 3 Department of Epidemiology and Biostatistics, Erasmus MC, 3000 CA Rotterdam, The Netherlands 4 Department of Clinical Chemistry, Erasmus MC, 3000 CA Rotterdam, The Netherlands

5 Correspondence address. Tel: +31-10-7043346; Fax: +31-10-7035430; E-mail: j.visser{at}erasmusmc.nl


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
BACKGROUND: Polycystic ovaries display an increased number of pre-antral and antral follicles compared with normal ovaries, suggesting that early and late follicle development are disturbed. The pathophysiology of this process is poorly understood. Since the transforming growth factor β family members, anti-Müllerian hormone (AMH) and bone morphogenetic proteins (BMPs), inhibit FSH sensitivity, their signalling may contribute to the aberrant follicle development in these women. Here, we investigated the role of ALK2, a type I receptor for AMH/BMP signalling, in PCOS using a genetic approach.

METHODS: Seven single nucleotide polymorphisms in the ACVR1 gene, encoding ALK2, were genotyped in 359 PCOS patients and 30 normo-ovulatory and 3543 population-based control women, and haplotypes were determined. Subsequently, the association of ACVR1 variants with ovarian parameters and hormone levels was investigated.

RESULTS: The polymorphisms rs1220134, rs10497189 and rs2033962 and their corresponding haplotypes did not show different frequencies from controls, but were associated with AMH levels in PCOS women (P = 0.001, P = 0.002 and P = 0.007, respectively). Adjustment for follicle number revealed that the association with AMH levels was, in part, independent from follicle number, suggesting that variants in ACVR1 also influence AMH production per follicle.

CONCLUSIONS: Genetic variation within ACVR1 is associated with AMH levels and follicle number in PCOS women, suggesting that ALK2 signalling contributes to the disturbed folliculogenesis in PCOS patients.

Key words: anti-Müllerian hormone/ALK2/polycystic ovary syndrome/folliculogenesis/polymorphism


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Polycystic ovary syndrome (PCOS) is the most frequent endocrine disorder and most common cause of anovulation in women of reproductive age (Franks, 1995Go; Laven et al., 2002Go). PCOS is a complex genetic disorder, in which multiple susceptibility genes interact with lifestyle and environmental factors (Vink et al., 2006Go). These genetic factors not only contribute to PCOS risk but also appear to modulate component traits of PCOS, such as insulin resistance, androgen levels and follicle development (Franks et al., 2001Go; Urbanek, 2007Go).

The disturbance of folliculogenesis, resulting in anovulation and infertility, is a major characteristic of PCOS. In polycystic ovaries, the selection of a dominant follicle is disturbed, suggesting aberrant FSH sensitivity of follicles at the antral stage. In addition, polycystic ovaries display an increased density of small pre-antral follicles compared with normal ovaries, suggesting that early follicle development is also abnormal (Webber et al., 2003Go; Franks et al., 2006Go).

Intraovarian growth factors that play an important role in early and late follicle development are members of the transforming growth factor β (TGFβ) superfamily, such as bone morphogenetic proteins (BMPs) and anti-Müllerian hormone (AMH) (Franks et al., 2001Go; Shimasaki et al., 2004Go). BMPs and AMH signal via a heteromeric receptor complex consisting of a ligand-specific type II receptor and shared type I receptors (ALK2, ALK3 and ALK6) (Massague and Chen, 2000Go; di Clemente et al., 2003Go; Jamin et al., 2003Go; Visser, 2003Go). The BMP/AMH signalling system is present in somatic cells and/or oocytes (Erickson and Shimasaki, 2003Go; Visser and Themmen, 2005Go), and can exert autocrine and/or paracrine actions. This signalling pathway has been implicated as a negative (AMH) or positive regulator (BMP4 and BMP7) of primordial follicle recruitment (Durlinger et al., 1999Go; Lee et al., 2001Go; Nilsson and Skinner, 2003Go). Furthermore, AMH and BMPs contribute to the FSH-dependent follicle selection by suppressing FSH actions (Durlinger et al., 2001Go; Lee et al., 2001Go; Otsuka et al., 2001Goa,bGo). Interestingly, serum AMH levels are elevated in PCOS women compared with normo-ovulatory women (Pigny et al., 2003Go; Laven et al., 2004Go), and therefore may contribute or further aggravate the disturbed follicle development and selection in PCOS patients (Visser et al., 2006Go). Indeed, in a recent study, we observed that the AMH Ile49Ser polymorphism contributes to the frequency of polycystic ovaries, number of follicles and level of androgens in PCOS patients (Kevenaar et al., 2008Go).

In this study, we investigated whether ALK2, one of the type I receptors shared by the AMH/BMP signalling pathway, contributes to PCOS susceptibility and/or phenotype, by using a genetic approach. The common genetic variation across the ACVR1 gene, encoding ALK2, was captured by selecting tagging single nucleotide polymorphisms (SNPs). Subsequently, these SNPs and the corresponding haplotypes were analysed in a large cohort of PCOS women. We observed that variations in ACVR1 were associated with AMH levels and follicle number in PCOS patients.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Acknowledgements
 References
 
Subjects
The local Medical Ethics Review Committee approved this study, and informed consent was obtained from all participants. Dutch Caucasian patients attending our fertility clinic between 1993 and 2004, who fulfilled the definition of PCOS by the Rotterdam criteria (The Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, 2004Go) were enrolled in this study (n = 407). Hyperandrogenism was defined as an elevated (>4.5) free androgen index (testosterone x 100/SHBG) (with testosterone and SHBG both expressed in nmol/l). Polycystic ovaries were defined as 12 or more follicles (measuring 2–9 mm) per ovary and the ovarian volume was considered to be increased above 10 ml (Balen et al., 2003Go). Standardized initial screening (clinical investigation, transvaginal ultrasound and fasting blood withdrawal) was performed on a random day between 09.00 and 11.00 h, irrespective of the interval between blood sampling and the preceding menses.

For sonographic imaging, we used a 6.5 MHz vaginal transducer (model EUB-415, Hitachi Medical Corp., Tokyo, Japan). Ovarian volume and the mean follicle number were assessed as described earlier (van Santbrink et al., 1997Go). Serum hormone levels were assessed at the time patients were originally seen using the following assays: serum FSH and LH were measured by luminescence-based immunometric assays (Immulite 2000, Diagnostic Products Corporation, Los Angeles, CA, USA); serum estradiol and testosterone were measured using radioimmunoassays (Diagnostic Products Corporation); serum androstenedione was measured using the Immulite 2000; inhibin B was measured using an enzyme-immunometric assay (Oxford BioInnovation, Oxford, UK) and AMH levels were measured collectively in samples stored frozen using an in-house AMH ELISA assay (Kevenaar et al., 2006Go), commercially available through Diagnostic Systems Laboratories (Webster, TX, USA). Intra- and inter-assay coefficients of variation were <3% and 5.8% for FSH, <3.5% and 7.1% for LH, <10.9% and 10.7% for androstenedione, <10.2% and 8.8% for estradiol, <5.7% and 8.4% for testosterone, <7% and 15% for inhibin B and <3.5% and 4% for AMH.

DNA was available for 394 PCOS women, and genotyping of all selected ACVR1 polymorphisms was successful in 359 PCOS women. In a subset of 295 women, AMH serum levels were measured.

The control group consisted of a previously described cohort of Dutch Caucasian normo-ovulatory women (n = 32) (de Vet et al., 2002Go; Kevenaar et al., 2007Go), for which genotyping of the seven tagging SNPs in ACVR1 was successful in 30 women. Inclusion criteria were a regular menstrual cycle (26–30 days), age of 20–36 years and normal body mass index (18–25 kg/m2). In this cohort, assessment of serum hormone levels (FSH, LH, testosterone, estradiol, inhibin B and AMH) and transvaginal ultrasound were performed on Day 3 of the menstrual cycle, using the methods as described above.

In addition, a large population-based cohort of the elderly, the Rotterdam study, was used to determine the allele and genotype frequencies of the seven tagging SNPs in the ACVR1 gene in the general Dutch Caucasian population. The design and rationale of this study have been described earlier (Hofman et al., 1991Go). For the present study, only women in whom genotyping was successful were included (n = 3543). The mean age of these women was 70.4 years (range 55.0–99.2).

Genotyping and haplotype determination
We selected seven SNPs, rs1220134, rs10497189, rs2033962, rs10933441, rs17798043, rs10497192 and rs1372115, which span the genomic length of ACVR1. These seven SNPs were selected because they are predicted to tag the haplotypes across the entire gene including 40 kb of the promoter region (upstream of the first translated exon) and 20 kb of the 3'-UTR region, and occurred at >5% frequency in the Caucasian population of the HapMap database (The International HapMap Consortium, 2003Go). These SNPs were genotyped using Taqman allelic discrimination assays. For four SNPs, Assays-on-Demand (i.e. Pre-Designed Assay) with the following assay numbers were used: rs1220134, C_7544932_10; rs10933441, C_31158472_10; rs17798043, C_33166336_10 and rs10497192, C_8503188_10 (Applied Biosystems, Nieuwerkerk aan den IJssel, The Netherlands). For the additional three SNPs, Assays-by-Design (i.e. Custom Assay) with the following probes were used: rs10497189, 5'-ACTAATGTCCaAGAACAC-3' and 5'-AATGTCCgAGAACAC-3'; rs2033962, 5'-TCAGCTTTCcGAGCTC-3' and 5'-AGCTTTCaGAGCTC-3'; rs1372115, 5'-TTCAGTCCaTGGTTTAT-3' and 5'-CAGTCCgTGGTTTAT-3'. Each PCR reaction contained 2 ng of dried genomic DNA, 1 µl of Taqman Universal PCR Master Mix 2x, 0.025 µl of the 80x Assay-on-Demand mix or 0.05 µl of the 40x Assay-by-Design mix in a total volume of 2 µl. The PCR reaction was performed according to the instructions of the manufacturer. The genotyping results were analysed using an ABI prism 7900HT Sequence Detection System. In the Rotterdam study, a random selection of 5% of samples was independently repeated to confirm genotyping results. The disagreement rate for each SNP in ACVR1 was <0.4%.

Statistical analysis
The PHASE program (Stephens et al., 2001Go) and Haploview version 3.32 (Barrett et al., 2005Go) were used to construct haplotypes and haplotype blocks. To estimate linkage disequilibrium (LD) between SNPs, the pair-wise LD coefficient (D') and the correlation coefficient (r2) were calculated using Haploview. The solid spine of LD method was used to define haplotype blocks.

In the 359 PCOS patients, the 30 normo-ovulatory controls and the 3543 women of the Rotterdam study, genotype frequencies of each ACVR1 SNP were tested for Hardy–Weinberg equilibrium proportions using Haploview. Differences in single marker or haplotype frequencies were compared between cases and controls using the chi-squared test in Haploview. Furthermore, an empirical P-value by permutation analysis was obtained using Haploview. The phenotypic status of each individual was permuted 10 000 times and association analysis was performed to obtain the test statistic under the null hypothesis of no association. The empirical P-value was obtained as the proportion of the 10 000 replicates that had a P-value less than or equal to the one obtained from the actual (unshuffled) data (Barrett et al., 2005Go; Goodarzi et al., 2006Go).

If appropriate, hormone levels were log transformed to normalize their distribution. Correlations between hormone levels were determined using Spearman's rank correlation coefficient. Within the subset of PCOS women in whom AMH levels were measured (n = 295), differences in hormone levels and ovarian parameters were tested between the genotype and haplotype groups using one-way analysis of (co)variance [AN(C)OVA] with adjustment for age and BMI. Because of statistical power, only haplotypes with an allele frequency of >5% were included in this analysis. Trend analysis assuming an additive genetic model was performed for the presence of zero, one or two copies of the associated allele, incorporating the genotype or haplotype variable as a continuous term in a linear regression model (Sasieni, 1997Go). Correction for multiple testing was performed by applying a Bonferroni correction to the level of significance, which was reset from P < 0.05 to P < 0.0072 considering the number of SNPs (n = 7) or haplotypes (n = 7) analysed in this study. All statistical analyses were performed using Statistical Package for Social Sciences, SPSS, version 11.0.1 (SPSS Inc., Chicago, IL, USA).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Acknowledgements
 References
 
Clinical characteristics of the PCOS cohort
Characteristics of the 359 PCOS women, the 30 normo-ovulatory controls and the 3543 women of the Rotterdam study are shown in Table I. As previously shown (Laven et al., 2004Go), AMH serum levels were elevated in the PCOS women compared with the normo-ovulatory controls (Table I). AMH levels were strongly correlated with total follicle number (r = 0.51, P < 0.001) and total testosterone levels (r = 0.47, P < 0.001). Total testosterone levels were also positively correlated with total follicle number (r = 0.30, P < 0.001).


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  		Table I. Clinical characteristics of the study population.

 
PCOS risk by ACVR1 gene variants
The seven selected polymorphisms in ACVR1 were all located within non-coding regions of the gene (Fig. 1A and Table II). Genotype frequencies of the seven ACVR1 polymorphisms were in Hardy–Weinberg equilibrium proportions in the cohort of PCOS patients, the normo-ovulatory controls and the post-menopausal women of the Rotterdam study (results not shown). The minor allele frequencies (MAF) of the seven SNPs in the different populations are shown in Table II. The T-allele of rs17798043 was less common in PCOS patients compared with women of the Rotterdam study (OR = 0.58, 95% CI 0.40–0.84, P = 0.003, P after permutation analysis=0.02), although the allele frequencies of rs17798043 were not significantly different between PCOS patients and normo-ovulatory controls (OR = 0.65, 95% CI 0.22–1.91, P = 0.43, Table II). Allele frequencies of the other six ACVR1 SNPs were similar between the PCOS patients, the normo-ovulatory women and the women of the Rotterdam study (Table II). In addition, the allele frequencies of the ACVR1 SNPs in the two control groups were similar to the Caucasian allele frequencies of these SNPs in the HapMap database (www.hapmap.org) (The International HapMap Consortium, 2003Go).


Figure 1
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  		Figure 1: Gene structure, LD plot and haplotype frequencies for ACVR1.

(A) The ACVR1 gene, encoding ALK2, has nine exons (black boxes) and two untranslated exons (grey boxes) and is located on the reverse strand of chromosome 2 (2q23–q24). Arrows indicate the locations of the genotyped SNPs. (B) The LD plot displays D' values (%) for each pair of SNPs in the box at the intersection of the diagonals from each SNP. The dark solid boxes indicate D'=1 (100%) for the corresponding pair of variants. The SNPs were considered in two haplotype blocks as indicated. On the basis of the HapMap database, these haplotype blocks cover the common genetic variation across 40 kb of the 5'-UTR (upstream of the first translated exon) and 20 kb of the 3'-UTR of the ACVR1 gene. (C) Common ACVR1 haplotypes and their frequencies in PCOS women, normo-ovulatory women and women of the Rotterdam study. aSignificantly different from allele frequencies of PCOS women (P-value from {chi}2 test = 0.004, P-value obtained using permutation analysis=0.05).

 

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  		Table II. Frequencies and locations of ACVR1 polymorphisms.

 
LD (D’) among the seven ACVR1 SNPs in our subjects ranged from 0.21 to 1.00 (Fig. 1B). The patterns of LD among the SNPs determined two haplotype blocks within the gene, the first block comprised the first and second SNP at the 3' part of the gene; the second block comprised the five SNPs remaining at the 5' part of the gene (Fig. 1B). Frequencies of the haplotypes of block 1 and 2 did not differ between PCOS patients and both control groups (Fig. 1C, and results not shown). Only the GCTTG haplotype in block 2 had a lower allele frequency in PCOS patients compared with the Rotterdam study, which is in line with the individual marker rs17798043 that fully drives this haplotype (results not shown).

Ovarian phenotype in PCOS women by ACVR1 gene variants
Within the group of 295 PCOS women, the different ACVR1 genotypes and haplotypes were not associated with general characteristics, such as age, BMI and waist–hip ratio (results not shown). The mean ovarian volume, the percentage of women exhibiting polycystic ovaries and the percentage of women with amenorrhoea were not different between the various genotype groups. However, three ACVR1 polymorphisms, rs1220134, rs10497189 and rs2033962, were associated with serum AMH levels in PCOS patients, in a manner that suggests an allele-dose effect (P-trend = 0.001, 0.002 and 0.007, respectively, Table III). For each of these three polymorphisms, women homozygous for the minor allele (rs1220134 A/A, n = 23; rs10497189 C/C, n = 4; or rs2033962 T/T, n = 16) had, respectively, 30%, 70% and 34% higher AMH levels compared with women homozygous for the major allele. Polymorphism rs10497189 was also associated with follicle number. Women with the rs10497189 C/C genotype had on average 14.9 more follicles (1.4-fold) compared with women with the T/T genotype (P-trend = 0.001, Table III). Moreover, the other two polymorphisms tended to be associated with follicle number, although these associations did not reach the Bonferroni corrected significance level (P < 0.0072, Table III). Since serum AMH levels are correlated with follicle number, we adjusted the AMH levels of the different genotypes for follicle number, using ANCOVA. After adjustment, the rs1220134 polymorphism remained significantly associated with AMH levels (P-trend = 0.007, Table III). Furthermore, the effect size of the difference in AMH levels did not differ substantially before or after adjustment ({Delta}3.3 versus {Delta}2.8, respectively), indicating that the observed association was (in part) independent of follicle number. For the rs10497189 and rs2033962 polymorphisms, the effect sizes also remained present after adjustment ({Delta}5.4 and {Delta}2.0, respectively), but because of power, they failed to reach significance. Polymorphisms rs10497192 and rs1372115 also tended to be associated with serum AMH levels (P = 0.03 and P = 0.02, respectively), but these associations did not reach the Bonferroni corrected significance level (Table III).


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  		Table III. AMH levels and follicle count in PCOS women by ACVR1 genotypes.

 
Inhibin B levels were not significantly associated with the different ACVR1 genotypes, but for the rs1220134, rs10497189 and rs10497192 genotypes trends in inhibin B levels in the same direction as the AMH levels were observed (results not shown). Furthermore, no associations were observed between the different ACVR1 genotypes and LH, FSH, androgen (testosterone and androstenedione) and estradiol levels in the PCOS cohort. In the normo-ovulatory women (n = 30), the ACVR1 genotypes or haplotypes were not associated with follicle number or serum AMH levels, which may be due to lack of power (results not shown).

Subsequently, the association of the ACVR1 haplotypes with AMH levels and follicle number in PCOS women was analysed (Table IV). Consistent with the results of the individual markers in haplotype block 1 (rs1220134 and rs10497189), the haplotypes TT and AC of this block were associated with serum AMH levels in PCOS women (P-trend = 0.001 and P-trend = 0.002, respectively). Carriers of the TT haplotype had lower AMH levels, whereas carriers of the AC haplotype had higher AMH levels compared with non-carriers. After adjustment for follicle number, the TT haplotype remained significantly associated with AMH levels (similar to the rs1220134 genotypes, P-trend = 0.007). Indeed, scatter plots showing the correlation between AMH and total follicle number for the TT haplotypes revealed a lower AMH production per follicle number in carriers of the TT haplotype compared with non-carriers (Fig. 2). In line, testosterone production per follicle number also appeared lower in carriers of the TT haplotype compared with non-carriers (Fig. 2), although the association of the TT haplotype with testosterone levels was not significant (P = 0.25). The AC haplotype failed to reach significance after adjustment for follicle number, although the effect size remained present ({Delta}5.4) (similar to the rs10497189 genotypes), indicating that the association of this haplotype with AMH levels is in part driven by the observed association with follicle number. The haplotypes GCCTG and TCCCA of block 2 also tended to be associated with AMH levels, but this association did not reach significance (P-trend = 0.02 and 0.02). After adjustment for follicle number, the association of the GCCTG haplotype with AMH levels nearly reached significance (P-trend = 0.01, effect size {Delta}2.8).


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  		Table IV. AMH levels and follicle count in PCOS women by ACVR1 haplotypes.

 

Figure 2
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  		Figure 2: Scatter plots depicting the correlations between the individual total follicle number and AMH or testosterone serum levels in 295 PCOS women, carrying 0 (black squares, black solid line, n = 23), 1 (grey squares, grey solid line, n = 123) or 2 (open squares, black dotted line, n = 149) copies of the ACVR1 TT haplotype.

Spearman's rank correlation coefficient (r) and its significance level (P) are depicted.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Acknowledgements
 References
 
Polycystic ovaries contain an increased number of pre-antral and antral follicles compared with normal ovaries, suggesting that early and late follicle development are disturbed (Hughesdon, 1982Go; Franks et al., 2000Go; Laven et al., 2002Go; Webber et al., 2003Go; Franks et al., 2006Go). Intra-ovarian factors, in particular members of the TGFβ family, such as GDF9, follistatin and AMH, may play a role in this pathophysiologic process (Roberts et al., 1994Go; Teixeira Filho et al., 2002Go; Franks et al., 2006Go; Visser et al., 2006Go). In this study, we observed an association of genetic variants in one of the type I receptors for members of the TGFβ superfamily, ALK2, with follicle number and AMH levels in PCOS women, supporting the above-mentioned hypothesis.

ALK2 is a type I receptor for AMH and BMP ligands. Interestingly, both AMH and BMPs inhibit FSH sensitivity of growing follicles (Durlinger et al., 2001Go; Lee et al., 2001Go; Otsuka et al., 2001Goa,bGo; Lee et al., 2004Go), and therefore may contribute to the disturbed follicle selection and the accumulation of growing follicles in PCOS patients. Indeed, AMH serum levels are strongly elevated in PCOS women (Pigny et al., 2003Go; Laven et al., 2004Go), although differences in the degree of elevation are observed among studies. The variation in contribution of women actually exhibiting polycystic ovaries to PCOS cohorts most likely contributes to this variation in serum AMH levels. In a previous study, we found an association of the AMH Ile49Ser polymorphism with follicle number and androgen levels in PCOS women, suggesting that this functional polymorphism in the AMH ligand may influence FSH sensitivity and FSH-induced aromatase activity (Kevenaar et al., 2008Go). In the present study, genetic variations in ACVR1 were associated with AMH levels and follicle number, but not with androgen levels.

The associations observed for the AMH variant and ACVR1 variants do not fully overlap. A simple explanation is that ACVR1 variants not only affect the actions of AMH but also of other BMP ligands. In addition, BMPs and AMH may use different type I receptors for signalling, depending on cell-type and function (Visser, 2003Go; Shimasaki et al., 2004Go). Furthermore, it cannot be excluded that, compared with genetic variation in AMH, genetic variation in ACVR1 may result in a different effect on the AMH receptor complex, and thus on AMH-mediated function. The association of ACVR1 variants with AMH levels, but not with androgen levels, may suggest that ALK2 signalling affects predominantly granulosa cell function and not theca cell function in PCOS women. Alternatively, the lack of an association with androgen levels could be explained by the stronger correlation between follicle number and AMH levels than between follicle number and androgen levels. Indeed, in agreement with decreased AMH levels in carriers of the ACVR1 TT haplotype, androgen levels also tended to be lower in TT haplotype carriers.

The polymorphisms rs1220134, rs10497189 and rs2033962 and haplotypes TT and AC (block 1) of the ACVR1 gene were associated with AMH levels, and these associations were in part independent of follicle number. This suggests that these variants not only influence the number of follicles but also the amount of AMH produced per follicle. Indeed, it was recently suggested that granulosa cells of PCOS women produce more AMH per follicle than granulosa cells of normo-ovulatory women (Pellatt et al., 2006Go). So far, little is known about the factors that regulate AMH production in normal or polycystic ovaries. FSH and LH do not regulate AMH production in granulosa cells of normal ovaries, but in polycystic ovaries, FSH may suppress and LH may stimulate AMH production (Pellatt et al., 2006Go). Our findings suggest that ALK2 is one of the factors regulating AMH expression in polycystic ovaries. Studies in AMHRII null mice suggest that AMH expression is not under strong control of its own signalling pathway (Kevenaar et al., unpublished results); hence, AMH expression may be regulated by other BMP ligands using ALK2. Taken together, in PCOS women, ALK2 may not only inhibit follicle selection via AMH or BMP signalling, leading to the accumulation of follicles, but it may also enhance AMH production by these follicles, thereby exaggerating the disturbance of folliculogenesis even more. In the 30 normo-ovulatory women, the ACVR1 genotypes or haplotypes were not observed to be associated with AMH levels or follicle growth; however it is difficult to draw firm conclusions since the statistical power is limited.

One of the strengths of our study is that we investigated the genetic variation within the entire ACVR1 gene, including 40 kb of the 5'-UTR and 20 kb of the 3'-UTR of the gene. Using a conservative Bonferroni correction for multiple testing, we observed that three SNPs and the corresponding haplotypes were associated with AMH levels. For two other polymorphisms and haplotypes, we observed associations that nearly reached significance. Since the tested markers in ACVR1 are not independent of each other, it is possible that the Bonferroni correction results in an overly stringent correction of our results (Cardon and Bell, 2001Go), implying that in fact several more ACVR1 variants may be involved in folliculogenesis in PCOS women. Because of the haplotype/SNP tagging approach used in this genetic study, it is difficult to elucidate which of the polymorphisms in the ACVR1 gene are causative. Nevertheless, the most significant associations were observed for the polymorphisms located in haplotype block 1, which mainly comprises the 3'-UTR region of the gene. Since this region is known to regulate mRNA stability, it may be worthwhile to investigate whether genetic variants in the 3'-UTR region influence ALK2 expression levels in polycystic ovaries.

Although our PCOS cohort is among the largest studied in PCOS genetics, our results need to be replicated in other cohorts before definite conclusions can be obtained. Nevertheless, the observed associations of polymorphisms in ACVR1 with AMH levels are highly significant, show consistent effects of higher AMH levels associated with the less frequent allele and correspond to an allele-dose model, making it unlikely that our results can be explained by chance alone.

In conclusion, our study demonstrates for the first time that genetic variants of the ACVR1 gene are associated with follicle number and AMH levels in PCOS women, suggesting that ALK2 contributes to the disturbed folliculogenesis and the production of AMH per follicle. These results provide new insight into the pathophysiology of PCOS and may be important for the interpretation of AMH levels as a marker for PCOS in the clinic. Furthermore, our results indicate that members of the TGFβ superfamily contribute to the complex pathogenesis of PCOS. Hence, it will be of interest to investigate the contribution of other ligands and receptors in this signalling pathway in the future.


   Acknowledgements
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
References
 
The authors thank S. Lie Fong for data collection of the PCOS cohort and Prof. H.A.P. Pols for critical reading of the manuscript. Furthermore, the authors thank the participants of the three study cohorts and acknowledge all participating general practitioners and the many field workers in the research centre of the Rotterdam study in Ommoord, Rotterdam, The Netherlands.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Balen AH, Laven JSE, Tan SL, Dewailly D. Ultrasound assessment of the polycystic ovary: international consensus definitions. Hum Reprod Update (2003) 9:505–514.[Abstract/Free Full Text]

Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (2005) 21:263–265.[Abstract/Free Full Text]

Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet (2001) 2:91–99.[CrossRef][Web of Science][Medline]

de Vet A, Laven JSE, de Jong FH, Themmen APN, Fauser BCJM. Antimullerian hormone serum levels: a putative marker for ovarian aging. Fertil Steril (2002) 77:357–362.[CrossRef][Web of Science][Medline]

di Clemente N, Josso N, Gouedard L, Belville C. Components of the anti-Mullerian hormone signaling pathway in gonads. Mol Cell Endocrinol (2003) 211:9–14.[CrossRef][Web of Science][Medline]

Durlinger ALL, Kramer P, Karels B, de Jong FH, Uilenbroek JT, Grootegoed JA, Themmen APN. Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary. Endocrinology (1999) 140:5789–5796.[Abstract/Free Full Text]

Durlinger ALL, Gruijters MJ, Kramer P, Karels B, Kumar TR, Matzuk MM, Rose UM, de Jong FH, Uilenbroek JT, Grootegoed JA, et al. Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary. Endocrinology (2001) 142:4891–4899.[Abstract/Free Full Text]

Erickson GF, Shimasaki S. The spatiotemporal expression pattern of the bone morphogenetic protein family in rat ovary cell types during the estrous cycle. Reprod Biol Endocrinol (2003) 1:9.[CrossRef][Medline]

Franks S. Polycystic ovary syndrome. N Engl J Med (1995) 333:853–861.[Free Full Text]

Franks S, Mason H, Willis D. Follicular dynamics in the polycystic ovary syndrome. Mol Cell Endocrinol (2000) 163:49–52.[CrossRef][Web of Science][Medline]

Franks S, Gharani N, McCarthy M. Candidate genes in polycystic ovary syndrome. Hum Reprod Update (2001) 7:405–410.[Abstract/Free Full Text]

Franks S, McCarthy MI, Hardy K. Development of polycystic ovary syndrome: involvement of genetic and environmental factors. Int J Androl (2006) 29:278–285. discussion 286–290.[CrossRef][Web of Science][Medline]

Goodarzi MO, Shah NA, Antoine HJ, Pall M, Guo X, Azziz R. Variants in the 5alpha-reductase type 1 and type 2 genes are associated with polycystic ovary syndrome and the severity of hirsutism in affected women. J Clin Endocrinol Metab (2006) 91:4085–4091.[Abstract/Free Full Text]

Hofman A, Grobbee DE, de Jong PT, van den Ouweland FA. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. Eur J Epidemiol (1991) 7:403–422.[CrossRef][Web of Science][Medline]

Hughesdon PE. Morphology and morphogenesis of the Stein–Leventhal ovary and of so-called ‘hyperthecosis. Obstet Gynecol Surv (1982) 37:59–77.[Medline]

Jamin SP, Arango NA, Mishina Y, Hanks MC, Behringer RR. Genetic studies of the AMH/MIS signaling pathway for Mullerian duct regression. Mol Cell Endocrinol (2003) 211:15–19.[CrossRef][Web of Science][Medline]

Kevenaar ME, Meerasahib MF, Kramer P, van de Lang-Born BM, de Jong FH, Groome NP, Themmen APN, Visser JA. Serum anti-Mullerian hormone levels reflect the size of the primordial follicle pool in mice. Endocrinology (2006) 147:3228–3234.[Abstract/Free Full Text]

Kevenaar ME, Themmen APN, Laven JSE, Sonntag B, Lie Fong S, Uitterlinden AG, de Jong FH, Pols HAP, Simoni M, Visser JA. Anti-Mullerian hormone and anti-Mullerian hormone type II receptor polymorphisms are associated with follicular phase estradiol levels in normo-ovulatory women. Hum Reprod (2007) 22:1547–1554.[Abstract/Free Full Text]

Kevenaar ME, Laven JSE, Lie Fong S, Uitterlinden AG, de Jong FH, Themmen APN, Visser JA. A functional AMH polymorphism is associated with follicle number and androgen levels in polycystic ovary syndrome patients. J Clin Endocrinol Metab (2008) 93:1310–1316.[Abstract/Free Full Text]

Laven JSE, Imani B, Eijkemans MJC, Fauser BCJM. New approach to polycystic ovary syndrome and other forms of anovulatory infertility. Obstet Gynecol Surv (2002) 57:755–767.[CrossRef][Web of Science][Medline]

Laven JSE, Mulders AGMGJ, Visser JA, Themmen APN, De Jong FH, Fauser BCJM. Anti-Mullerian hormone serum concentrations in normoovulatory and anovulatory women of reproductive age. J Clin Endocrinol Metab (2004) 89:318–323.[Abstract/Free Full Text]

Lee WS, Otsuka F, Moore RK, Shimasaki S. Effect of bone morphogenetic protein-7 on folliculogenesis and ovulation in the rat. Biol Reprod (2001) 65:994–999.[Abstract/Free Full Text]

Lee WS, Yoon SJ, Yoon TK, Cha KY, Lee SH, Shimasaki S, Lee S, Lee KA. Effects of bone morphogenetic protein-7 (BMP-7) on primordial follicular growth in the mouse ovary. Mol Reprod Dev (2004) 69:159–163.[CrossRef][Web of Science][Medline]

Massague J, Chen YG. Controlling TGF-beta signaling. Genes Dev (2000) 14:627–644.[Free Full Text]

Nilsson EE, Skinner MK. Bone morphogenetic protein-4 acts as an ovarian follicle survival factor and promotes primordial follicle development. Biol Reprod (2003) 69:1265–1272.[Abstract/Free Full Text]

Otsuka F, Moore RK, Shimasaki S. Biological function and cellular mechanism of bone morphogenetic protein-6 in the ovary. J Biol Chem (2001) a 276:32889–32895.[Abstract/Free Full Text]

Otsuka F, Yamamoto S, Erickson GF, Shimasaki S. Bone morphogenetic protein-15 inhibits follicle-stimulating hormone (FSH) action by suppressing FSH receptor expression. J Biol Chem (2001) b 276:11387–11392.[Abstract/Free Full Text]

Pellatt L, Hanna L, Brincat M, Galea R, Brain H, Whitehead S, Mason H. Granulosa cell production of anti-Mullerian hormone is increased in polycystic ovaries. J Clin Endocrinol Metab (2006) 92:240–245.[CrossRef][Web of Science][Medline]

Pigny P, Merlen E, Robert Y, Cortet-Rudelli C, Decanter C, Jonard S, Dewailly D. Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome: relationship to the ovarian follicle excess and to the follicular arrest. J Clin Endocrinol Metab (2003) 88:5957–5962.[Abstract/Free Full Text]

Roberts VJ, Barth S, el-Roeiy A, Yen SS. Expression of inhibin/activin system messenger ribonucleic acids and proteins in ovarian follicles from women with polycystic ovarian syndrome. J Clin Endocrinol Metab (1994) 79:1434–1439.[Abstract]

Sasieni PD. From genotypes to genes: doubling the sample size. Biometrics (1997) 53:1253–1261.[CrossRef][Web of Science][Medline]

Shimasaki S, Moore RK, Otsuka F, Erickson GF. The bone morphogenetic protein system in mammalian reproduction. Endocr Rev (2004) 25:72–101.[Abstract/Free Full Text]

Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet (2001) 68:978–989.[CrossRef][Web of Science][Medline]

Teixeira Filho FL, Baracat EC, Lee TH, Suh CS, Matsui M, Chang RJ, Shimasaki S, Erickson GF. Aberrant expression of growth differentiation factor-9 in oocytes of women with polycystic ovary syndrome. J Clin Endocrinol Metab (2002) 87:1337–1344.[Abstract/Free Full Text]

The International HapMap Consortium. The International HapMap Project. Nature (2003) 426:789–796.[CrossRef][Web of Science][Medline]

The Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod (2004) 19:41–47.[Abstract/Free Full Text]

Urbanek M. The genetics of the polycystic ovary syndrome. Nat Clin Pract Endocrinol Metab (2007) 3:103–111.[CrossRef][Web of Science][Medline]

van Santbrink EJP, Hop WCJ, Fauser BCJM. Classification of normogonadotropic infertility: polycystic ovaries diagnosed by ultrasound versus endocrine characteristics of polycystic ovary syndrome. Fertil Steril (1997) 67:452–458.[CrossRef][Web of Science][Medline]

Vink JM, Sadrzadeh S, Lambalk CB, Boomsma DI. Heritability of polycystic ovary syndrome in a Dutch twin-family study. J Clin Endocrinol Metab (2006) 91:2100–2104.[Abstract/Free Full Text]

Visser JA. AMH signaling: from receptor to target gene. Mol Cell Endocrinol (2003) 211:65–73.[CrossRef][Web of Science][Medline]

Visser JA, Themmen APN. Anti-Mullerian hormone and folliculogenesis. Mol Cell Endocrinol (2005) 234:81–86.[CrossRef][Web of Science][Medline]

Visser JA, de Jong FH, Laven JSE, Themmen APN. Anti-Mullerian hormone: a new marker for ovarian function. Reproduction (2006) 131:1–9.[Abstract/Free Full Text]

Webber LJ, Stubbs S, Stark J, Trew GH, Margara R, Hardy K, Franks S. Formation and early development of follicles in the polycystic ovary. Lancet (2003) 362:1017–1021.[CrossRef][Web of Science][Medline]

Submitted on July 18, 2008; resubmitted on August 22, 2008; accepted on September 1, 2008.


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