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Human Molecular Genetics Advance Access originally published online on June 16, 2006
Human Molecular Genetics 2006 15(16):2401-2408; doi:10.1093/hmg/ddl155
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Polymorphisms of estrogen-biosynthesis genes CYP17 and CYP19 may influence age at menarche: a genetic association study in Caucasian females

Yan Guo1,{dagger}, Dong-Hai Xiong2,{dagger}, Tie-Lin Yang1, Yan-Fang Guo4, Robert R. Recker2 and Hong-Wen Deng1,3,4,*

1 The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Peoples Republic of China, 2 Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University, Omaha, NE 68131, USA, 3 Department of Orthopedic Surgery, School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA and 4 Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic China

* To whom correspondence should be addressed at: Department of Basic Medical Science, School of Medicine, University of Missouri/Kansas City, 2411 Holmes Street, Room: M3-CO3, Kansas City, MO 64108-2792, USA. Tel: +1 8162355354; Fax: +1 8162356517; Email: dengh{at}umkc.edu

Received May 17, 2006; Accepted June 13, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Variation in age at menarche (AAM) is known to be substantially influenced by genetic factors, but the true causal genes remain largely unidentified. Because the increased amplitude of estrogen exposure of tissues initiates the onset of menarche, the genes involved in estrogen biosynthesis are natural candidate genes underlying AAM. Our study aimed to identify whether the CYP17 and CYP19, the two key genes involved in the biosynthesis of estrogen, are associated with AAM variation in 1048 females from 354 Caucasian nuclear families. We genotyped 38 SNPs and established the linkage disequilibrium blocks and haplotype structures that covered the full transcript length of those two genes. Family-based and population-based statistical analyses were used to test for associations with all of the single SNPs and haplotypes. Both methods consistently detected significant associations for five SNPs of CYP19 with AAM. Haplotype analyses corroborated our single-SNP results by showing that the haplotypes in block 1 were highly significant to AAM in population-based analyses. However, we could not find any association of CYP17 with AAM. Our study is the first to suggest the important effect of CYP19 on AAM variation in Caucasian females. It will be valuable to replicate and confirm these findings in other independent studies, aiming at eventually finding the hidden genetic mechanisms underlying the variation in AAM.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Age at menarche (AAM) is an important anthropological variable associated with women's health. It has been shown that AAM is regulated by a variety of genetic and environmental factors (1–4). Twin and family studies have indicated that genetic contribution can explain about 53–74% of the variation in AAM (5–7). However, it is not clear what specific genes are responsible for AAM variation in human populations.

Menarche depends on the maturation of the female reproductive system and the fine co-ordination between it and the hypothalamic–pituitary–ovarian axis and other endocrine organs. Estrogen plays an important role in the differentiation, maturation and function of the reproductive system (8). The increased amplitude of estrogen exposure during puberty initiates the onset of menarche (9). Estrogen biosynthesis (Fig. 1) involves many steps, and the genes regulating these steps may contribute to the variation in AAM. This study focuses on CYP17 and CYP19, two key genes that control the biosynthesis of estrogen in the lipid precursor cells.


Figure 1551
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  		Figure 1. Estrogen biosynthesis pathway.

 
The CYP17 gene, which encodes cytochrome P450c17{alpha}, mediates two successive early steps of endogenous estrogen biosynthesis by converting pregnenolone and progesterone to androgen and estrogen precursors. A polymorphism in the CYP17 5'-untranslated region (UTR), namely A1/A2, which results in an additional Sp1-type promoter site (10), was previously associated with AAM in healthy post-menopausal Japanese women (11). Another study (12) also supported the association between puberty of girls and CYP17 A2 allele. However, the findings of these studies were not consistent. For example, Lai et al. (13) could not find the significant effect of the CYP17 A1/A2 polymorphism on menarche age variation.

The CYP19 gene encodes aromatase (P450arom), a key steroidogenic enzyme that catalyzes the final step of estrogen biosynthesis by converting testosterone and androstenedione to estradiol and estrone, separately. The most well-studied polymorphism of CYP19 is the tetranucleotide (TTTA)n repeat in intron4 (14–17). However, till now, there are no other association studies to explore the relationship between CYP19 and AAM.

In this study, we tested the associations of the two major genes responsible for estrogen metabolism, CYP17 and CYP19, with AAM variation, using high-density SNPs in a large Caucasian sample. Multiple statistical methods were used to cross-validate the results, with the aim of providing robust findings for follow-up studies.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
LD and haplotype analyses
Table 1 summarizes the basic characteristics of all the analyzed SNPs for the two candidate genes. The gene structures and associated linkage disequilibrium (LD) structures are shown in Figure 2. For CYP19, the 28 SNPs spanned ~130 kb genic region with an average density of 1 SNP per 4.6 kb (Fig. 2A). Five blocks with high LD were identified, which ranged in size from 9.0 to 34.6 kb (Table 3 and Fig. 2B). Block 1 encompassed exons I.1, 2a and I.4; block 2 spanned exons I.5, I.7 and I.f; block 3 was located between exons I.f and I.2; block 4 covered exons/promoters I.6, I.3 and PII and extended to intron 5 and block 5 ranged from intron 7 to 3'-UTR. SNP7, rs3751591, could not be assigned to any block due to its low LD with any other SNPs. Twelve haplotype-tagging SNPs (htSNPs) were identified to represent common haplotypes for each block (Table 3).


Figure 1552
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  		Figure 2. The genomic structures of CYP19 and CYP17. (A) The analysed SNPs are marked in the sequence by their locations. The SNP IDs correspond to those given in Table 1. (B) LD block structures. The boxes in black indicate the high-LD blocks, with pairwise D'>0.8. Block size and inter-block distances are indicated.

 


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  		Table 1. The basic characteristics of all SNPs for CYP19 and CYP17

 
The CYP17 is ~23 kb in length. Seven SNPs are indicated by their locations in Figure 2A. The average density is 1 SNP per 3.3 kb. The whole CYP17 gene was localized to a single LD block (Fig. 2B). Two htSNPs (Table 3) were sufficient to represent the haplotype diversity of CYP17.

Association analyses
For all the SNPs and haplotypes, no population stratification was found in our sample (data not shown). All the P-values of association results are presented in Tables 2 and 3. For the family-based association analyses of CYP19, five markers (SNPs 3, 5, 12, 14 and 27, P<0.05) were detected as nominally significant to AAM variation by QTDT test. SNP 3 and 5 remained significant after conservative Bonferroni correction (P=0.0010, 0.0003, separately). We also listed the association results for the most common haplotypes (frequency ≥5%) within each block (Table 3). Block 1-hap2 (P=0.0255) and block 5-hap2 (P=0.0218) were nominally significant.


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  		Table 2. Single-SNP analysis for AAM (including all htSNPs and SNPs with significant results)

 


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  		Table 3. Haplotype analyses for AAM

 
Subsequently, ANOVA was performed for the population-based association analyses. For the first random sample, the most significant markers were SNP 3 and 5 (P=5.9x10–6 and 1.2x10–6, separately), with contributions to AAM variation in our sample of 7.0 and 7.61%, respectively, as determined by the ANOVA r2-value (Table 2). We also found six nominal associations of AAM with SNPs 1, 11, 12, 13, 14 and 27. Within each block, we observed overall significant associations with block 1-hap1 and hap2 (the common haplotype T-A-A and T-G-A, P=1.7x10–6 and 0.00008, separately, significant after Bonferroni correction). Block 1-hap3 and block 5-hap2 were identified as nominally significant to AAM (Table 3). In addition, Figure 3 shows a histogram for illustrating these prominent results by ANOVA. For the second random sample, the results for all single-SNP and haplotypes (Tables 2 and 3) were nearly identical with the first group, which made our results more reliable.


Figure 1553
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  		Figure 3. Differences in the AAM value (years) across different genotypes of the SNP1, SNP3 and SNP5 for CYP19. The testing sample consisted of 348 unrelated female subjects.

 
For CYP17, there was no evidence of association (all P-values>0.05) with AAM for any single-SNP and haplotype. The results of both QTDT and ANOVA tests were consistent.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
On the basis of the biological role of estrogen-biosynthesis genes on AAM, we explored the associations of CYP17 and CYP19 with AAM, using both family- and population-based statistical procedures. The target genes were represented by both single-SNP and haplotype markers. A single-SNP can be more statistically efficient when the true risk allele is in higher LD with it than with a haplotype; however, there are also many cases in which haplotypes provide more information than single SNPs. Therefore, it is necessary to test both.

To our best knowledge, this is the first study to detect significant associations of CYP19 with AAM. Aromatase is encoded by CYP19, which is located in chromosome 15q21.2, and spans over 123 kb. The common translation initiates from exon 2 and the coding region covers only ~30 kb, whereas a large ~93 kb 5'-UTR serves as the regulatory unit comprising nine tissue-specific promoters (Fig. 2A, promoter 2a-PII) (18–20). As we can see, most SNPs we found associated with AAM are localized in this regulatory region. In estrogen-dependent tissues, aromotase is up-regulated via inappropriate activation of multiple promoters in 5'-UTR (21). Alternative use of these promoters, which regulate mature aromatase mRNA levels by changing transcription rate and splicing of each first exon or 5'-UTR onto a common splice junction immediately upstream of the coding region, is the key molecular mechanism conferring tissue-specific expression of the CYP19 gene. Therefore, these SNPs probably participate in gene expression and regulation (22).

For CYP19, the most promising findings were the three single-locus markers, SNPs 1, 3 and 5, which were all covered by block 1. The LD blocks we established here were compatible with that observed by Haiman et al. (23). The results of haplotype analyses corroborated our single-marker analyses by showing that haplotypes in block 1 were also significantly associated with AAM variations. Vista program also showed that block 1 region was highly conserved between the human and mouse genomic sequences, suggesting the existence of functional variants within this block that may be the ‘causal’ ones influencing AAM variation. Within block 1, the most significant polymorphism is SNP 5, which lies adjacent to the promoter 2a and has never been studied previously. In our population sample, women with the AA genotype of SNP 5 had more than 1 year earlier onset of menarche than those with the GG genotype (12.66 versus 13.76 years, Fig. 3), showing an allele dose effect of 0.5 year earlier onset of menarche per copy of the A allele. It is suggested that SNP 5 may be correlated with functional alleles influencing the gene expression of CYP19 via the regulating transcription rate. SNP 1 and 3 are both located near the exon/promoter I.1. It has been reported that the activity of promoter I.1 is the basis of strikingly elevated levels of circulating estrogen (100–1000 times the normal-level) in pregnant women (24,25). A previous study (26) demonstrated the activity of human promoter 1.1 transgene in mouse placenta. Although mouse placental tissue does not express aromatase endogenously, it contains the necessary transcriptional factors for human promoter I.1 expression. Intriguingly, SNP 1 falls within a region of transcription factor binding sites (TFBSs) named CAAT-BOX, which is typically accompanied by a conserved consensus sequence and is supposed to regulate the transcription of CYP19. Thus, SNP 1 per se or the functional loci in strong LD with SNP 1, may be implicated in modifying the transcription of CYP19 and affecting protein synthesis. However, the real molecular mechanisms await further functional analyses.

In this investigation, we could not find any association between CYP17 and AAM, which was inconsistent with the results of Gorai et al. (27). The difference may be partially explained by the different genetic background between Caucasians and Japanese, or the different markers used by the two studies.

The two statistical methods we used have their own merits and limitations, and can complement each other. Family-based analyses examine allele transmissions from parent to offspring and is robust against population stratification (28,29). In contrast, a population-based test is more powerful than family-based analyses to detect association if there is no population stratification (30). In our case, both approaches obtained nearly consistent results and can support each other, which made our results more reliable.

The statistical power of our study is estimated by the program Genetic Power Calculator (GPC, http://pngu.mgh.harvard.edu/~purcell/gpc/qtlassoc.html). Under the condition of the conservative significance level of P=0.001, assuming a marker is in strong LD (D'=0.9) with a functional mutation that accounts for ~4% variation of a phenotype, our sample can achieve >90% power, which is large enough to yield substantial statistical power to detect association between estrogen-biosynthesis genes and AAM.

Several issues need to be discussed. First, AAM is also affected by environmental factors. Such information was not recorded and used to adjust the raw data, which may affect the accuracy of the results. Second, our data of AAM was collected through retrospective self-reporting from adults, which may incur recalling bias. However, as menarche is a significant milestone which marks the start of a female's reproductive period, a recent study found a correlation of 0.79 between the original AAM and the information recalled 30 years later (31). Lots of previous studies have demonstrated that it was reasonable and reliable to use the retrospective method to acquire AAM data (32–34). Therefore, our study followed such common practice in the field that is feasible and convenient to apply.

In summary, we first reveal the effect of one key estrogen biosynthesis gene, CYP19, on the AAM phenotype in Caucasian females, which might have important implications for understanding the mechanism underlying the onset of menarche. It is necessary to perform further statistical and molecular functional analyses to replicate and confirm our findings.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Subjects
This study was approved by the Creighton University Institutional Review Board. All the participants signed informed-consent documents before entering the project. The subjects, who were recruited from 1997 to 2003, came from an expanding database created for ongoing studies in the Osteoporosis Research Center of Creighton University to search for genes underlying common human complex diseases/traits. The detailed design and recruitment procedures have been published earlier (35). All of the 1873 participants from 405 nuclear families, including 740 parents, 389 male children and 744 female children, were US Caucasians of European origin. The average family size was 4.63±1.78 (mean±SD, Standard deviation), ranging from 3 to 12. The overall sample yielded a total of 1512 sib pairs.

To study AAM in family-based analyses, we selected families consisting of at least two females of mother–daughter pair with available AAM data and discarded 51 uninformative families. In the final 354 informative families for AAM, there were a total of 1051 female subjects, with an average family size of 2.97±1.45, ranging from 2 to 9, thus providing adequate power for the association tests.

Data on AAM
Data from all female subjects were recorded in the nurse-administered questionnaire, and included a detailed medical history, such as menstrual history including the date when menstruation began. AAM was calculated as the date of menarche following the onset of menses minus the date of birth (in years rounded to the tenth decimal). The distribution of AAM was tested for normality by the Kolmogorov–Smirnov test implemented in the software Minitab (Minitab Inc., State College, PA, USA). The AAM data of family-based samples followed normal distribution after excluding three outliers, whose AAM were ≥18 years (diagnosed as primary amenorrhea). The mean age (year) of the 1048 females from the 354 nuclear families when recruited was 45.8 and their AAM ranged from 8.5 to 17.0, with the mean of 13.0 (SD=1.4).

SNP selection and genotyping
DNA was extracted from whole blood using a commercial isolation kit (Gentra Systems, Minneapolis, MN, USA) following the procedure detailed in the kit. Dense SNPs were identified by searching through public databases including dbSNP (http://www.ncbi.nlm.nih.gov/SNP/), JSNP (http://snp.ims.u-tokyo.ac.jp/), HGVbase (http://hgv-base.cgb.ki.se/), SNP Consortium (TSC) (http://snp.cshl.org/) and SNPper (http://snpper.chip.org/bio/snpper-enter). We initially selected 40 SNPs in and around the two candidate genes (CYP19: 33 SNPs; CYP17: 7 SNPs) on the basis of the following criteria, in order of importance in our selection scheme: (i) validation status, i.e. experimentally validated in Caucasians, (ii) an average density of 1 SNP per 4 kb, (iii) degree of heterozygosity, i.e. minor allele frequencies (MAF) >0.05, (iv) functional relevance and importance, namely the potential ‘functional’ SNPs residing within the TFBSs in the 5'promoter region, in the mRNA stability regulatory protein binding sites in 3'-UTR, in exons that change amino acid sequences, or in exon-intron boundaries that alter mRNA splicing, (v) reported to dbSNP by various sources.

Of the 40 SNPs attempted, 38 (CYP19: 31; CYP17: 7) were genotyped successfully using the high-throughput BeadArray SNP genotyping technology of Illumina Inc. (San Diego, CA, USA) and 35 were analyzed subsequently (three rare SNPs for CYP19 were abandoned because of insufficient power to detect their effects). The average rate of missing genotype data was reported by Illumina to be ~0.05%. The average genotyping error rate estimated through blind duplicating was reported to be less than ~0.01%. PedCheck (36) was used to verify Mendelian consistency of SNP genotype data, and any incompatibility for that family was removed. Then the error checking option embedded in Merlin (37) was run to identify and disregard the genotypes flanking excessive recombinants, thus further reducing genotyping errors. Allele frequencies for each SNP were calculated by allele counting, and the Hardy–Weinberg equilibrium was tested in 703 unrelated parents (see the following ‘LD and Haplotype Analyses’) using the PEDSTATS procedure embedded in Merlin.

LD and haplotype analyses
Our LD and haplotype analyses for CYP17 and CYP19 focused on the 703 unrelated parents from the 405 nuclear families. The chosen parental group consisted of 340 males and 363 females, ranging in age from 40.7 to 87.9 when recruited. The greater sample size we adopted allowed for more confidence in the dissected LD structures. Population haplotypes and their frequencies were inferred using PHASE v2.1.1 software. GOLD program (http://www.sph.umich.edu/csg/abecasis/GOLD/) was used to depict the graphic overviews of LD structure and to chart the pairwise standardized disequilibrium coefficient (D') derived from haplotype data. HaploBlockFinder (http://cgi.uc.edu/cgi-bin/kzhang/haploBlockFinder.cgi/) was used to identify block structures and select htSNPs. To infer haplotypes defined by the tagging SNPs within each block of each gene for all of the subjects among 405 families, we adopted the algorithm of integer linear programming implemented in PedPhase V2.0 (http://www.cs.ucr.edu/~jili/haplotyping.html), which is based on LD assumption and able to recover phase information at each marker locus with great speed and accuracy even in the presence of 20% missing data (38).

Bioinformatic analysis
For CYP19 and CYP17, human and mouse genomic sequences (CYP19: >130 kb, CYP17: >23 kb, from the Celera database) were analyzed using the Vista program (http://www-gsd.lbl.gov/VISTA/index.shtml) to visualize the pairwise percentage identity as calculated for every 100 bp. Potential TFBSs were queried for these two genes by use of a recently developed web-based Bioinformatics tool (Mapper, http://snpper.chip.org/bio/mapper-enter).

Association analyses
The software QTDT (http://www.sph.umich.edu/csg/abecasis/QTDT/) was utilized for family-based association analyses based on both single-SNP and haplotype markers with estimated frequencies greater than 5%. QTDT incorporates the variance components method (39) in the analysis of family data and includes exact estimation of P-values. QTDT also provides a test for detecting population stratification (40). If there is no evidence for population stratification, the test results of total association in QTDT should be more reliable.

For population-based association analyses, one daughter from each of the 354 families was randomly selected to generate an unrelated testing sample. Normality test was conducted before analyses, and the final random sample consisted of 348 female subjects after excluding six female subjects with either no data or extremely abnormal AAM values. ANOVA was performed for both single-SNP and haplotype markers versus AAM in Minitab software. The independent variable was the genotype, which divided into three levels corresponding to the three genotypes observed for each SNP (1,1; 1,2; 2,2;). Furthermore, ANOVA analysis was repeated on a second random sample consisting of 357 independent females not selected for the initial random sample.

All P-values <0.05 in our study were considered nominally significant, which were further subject to Bonferroni correction to account for multiple comparisons. The conservative significance threshold for a single test was assessed at a type I error rate of 0.05/N. N was the number of tested markers for each gene.


   ACKNOWLEDGEMENTS
 
Investigators of this work were partially supported by grants from NIH (R01 AR050496, K01 AR02170-01, R01 AR45349-01, and R01 GM60402-01A1) and an LB595 grant from the State of Nebraska. The study also benefited from grants from National Science Foundation of China, Huo Ying Dong Education Foundation, HuNan Province, Xi'an Jiaotong University and the Ministry of Education of China.

Conflict of Interest statement. The authors know of no financial interests or connections, direct or indirect, or other situations that might raise the question of bias in the work reported or the conclusions, implications or opinions stated.


   FOOTNOTES
 
{dagger} The first two authors contributed equally to this work. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

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