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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2005-2179
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The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 3 1009-1014
Copyright © 2006 by The Endocrine Society

Genomewide Linkage Scan for Quantitative Trait Loci Underlying Variation in Age at Menarche

Yan Guo, Hui Shen, Peng Xiao, Dong-Hai Xiong, Tie-Lin Yang, Yan-Fang Guo, Ji-Rong Long, Robert R. Recker and Hong-Wen Deng

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 (Y.G., T.-L.Y., H.-W.D.), Xi’an 710049, People’s Republic of China; Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University (Y.G., P.X., D.-H.X., T.-L.Y., J.-R.L., R.R.R.), Omaha, Nebraska 68131; Department of Orthopedic Surgery, School of Medicine, University of Missouri-Kansas City (H.S., Y.-F.G., H.-W.D.), Kansas City, Missouri 64108; and Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University (Y.-F.G., H.-W.D.), Changsha, Hunan 410081, People’s Republic of China

Address all correspondence and requests for reprints to: Dr. Hong-Wen Deng, Department of Basic Medical Science, University of Missouri School of Medicine, Room M3-CO3, 2411 Holmes Street, Kansas City, Missouri 64108-2792. E-mail: dengh{at}umkc.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: Age at menarche (AAM) is an important anthropological variable that has major implications for a woman’s health later in life. Genetic influence has been shown to contribute greatly to AAM, but the specific genetic determinants are largely unknown.

Objective: The objective of this study was to identify the quantitative trait loci (QTL) underlying the variations in AAM.

Methods: We performed a large-scale, genomewide, linkage scan in 2461 Caucasian women from 402 pedigrees. All subjects were genotyped with 410 microsatellite markers spaced approximately 8.9 cM apart across the human genome. Using the variance component method, we conducted multipoint linkage analyses and two-locus tests for epistatic interaction.

Results: The strongest linkage signal was obtained at the genomic region of 22q13 (LOD, 3.70); the other two suggestive linkages were on 22q11 (LOD, 2.68) and 11q23 (LOD, 1.98), respectively. We also detected significant epistatic interaction between genomic regions 22q13 and 3q13.

Conclusions: The identification of QTL and epistatic interaction in a large female sample laid a foundation for independent replication and fine-mapping studies as well as positional and functional candidate gene studies aimed at finding the causal genetic variants and hidden mechanisms concerning the variations in AAM.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
MENARCHE IS ONE of the most significant cornerstones in a woman’s psychological and physiological well-being. An early onset of menarche is associated with elevated risk of breast cancer (1) and endometrial cancer (2). Late menarche increases the risk of Alzheimer’s disease (3) and osteoporosis (4), but decreases the incidence of coronary heart disease (3). Therefore, from a clinical point of view, understanding the potential factors responsible for the age at menarche (AAM) may shed light on the pathophysiology of these diseases.

AAM is a complex trait that is determined by multiple genetic and environmental factors (5, 6, 7, 8). Twin and family studies have demonstrated that genetic contribution can explain 53–74% of the variation in AAM (5, 6, 9). There are highly significant correlations between AAM in mothers and daughters (8). In addition, genetic studies using association or linkage approaches have suggested several candidate genes associated with the variations in AAM, such as estrogen receptor {alpha} gene (10, 11), SHBG gene (12), androgen receptor gene (13), and cytochrome P450 genes (CYP family) (14, 15). Recently, Rothenbuhler’s group (16) conducted a genomewide linkage study of the onset of puberty (including AAM) and identified several suggestive genomic regions. However, to date, the specific genetic determinants for AAM remain largely unclear.

To identify susceptibility quantitative trait loci (QTL) underlying variations in AAM, we performed a large-scale, genomewide, linkage scan for AAM in 2461 Caucasian women from 402 pedigrees.


   Subjects and Methods
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 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

This study was approved by the Creighton University institutional review board. All study subjects signed informed consent documents before entering the project. The subjects came from an expanding database being created for studies to search for genes underlying the risk of osteoporosis and obesity, which is underway in the Osteoporosis Research Center of Creighton University. The studied sample contained 2,461 females from 402 pedigrees, who were all Caucasians of European origin. The pedigree structure is shown in Table 1Go. Altogether there are 13,481 informative relative pairs for linkage analyses (Table 2Go), including 1,946 sister-sister pairs.


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  		TABLE 1. Distribution of pedigree size

 

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  		TABLE 2. Informative relative pairs contained in study sample

 
Data on AAM

For the study subjects, a detailed medical history, including menstrual history, such as age when menstruation began, was obtained by nurse-administered questionnaire. AAM was calculated as the date of menarche after the onset of menses minus the date of birth (in years rounded to the 10th decimal). The average age of the sample subjects was 46.8 yr old, and their AAM ranged from 7.0–18.0 with a mean of 12.9 yr (SD = 1.4).

Genotyping

DNA was extracted from peripheral blood leukocytes using the Puregene DNA isolation kit (Gentra Systems, Inc., Minneapolis, MN). Males in these 402 pedigrees were also included for genotyping to assist genotype identification and Mendelian inheritance checking. Each subject was genotyped with 410 microsatellite markers (including 17 markers on chromosome X) from the Marshfield screening set 14 by the Marshfield Center for Medical Genetics (Marshfield, WI). These markers had an average population heterozygosity of 0.75 and were spaced, on the average, 8.9 cM apart. The detailed genotyping protocol is available at http://research.marshfieldclinic.org/genetics/Lab_Methods/methods.html. PedCheck (17) was performed to ensure that the genotype data conformed to the Mendelian inheritance pattern at all the marker loci. In addition, the program MERLIN (18) was used to detect genotyping errors of unlikely recombination (e.g. double recombination) in our sample. The overall genotyping error rate was approximately 0.03%.

Statistical analyses

The distribution of phenotype data was verified by the Kolmogorov-Smirnov test and was close to normal. Variance component linkage analyses (19, 20, 21), as implemented in SOLAR (Sequential Oligogenic Linkage Analysis Routines, www.sfbr.org/solar) (19), was used to perform the genomewide multipoint linkage scan for AAM. Simulation studies were conducted in SOLAR to assess the significance and robustness of our results (22). After 10,000 simulations, we obtained a correction constant of 0.95. All LOD scores given in the text have been empirically adjusted by multiplying this constant. Empirical pointwise P values for adjusted LOD scores were also calculated in SOLAR.

For chromosome X, we only performed two-point linkage analyses. However, other software, such as GENEHUNTER (23), which is capable of multipoint analysis, cannot handle large pedigrees that made up the major part of our sample. Breaking down large pedigrees into smaller ones would result in some loss of statistical power.

Two-locus tests for epistatic interaction were performed between the chromosomal region showing the highest linkage signal and other regions with an LOD score of 1.0 or greater. We limited the analyses to these regions for two reasons. First, these regions may have impact on AAM; second, we wanted to control the overall type I error by limiting the number of statistical comparisons. This test used an extension of the variance component model where interactions between two markers or two chromosomal regions are considered simultaneously. Two levels of modeling in addition to single-locus modeling were performed: 1) two-locus models with only additive effects for each pair of loci, and 2) two-locus models with additive effects as well as an epistatic term for interaction for both loci. The increase in LOD scores under the epistatic model over that under the additive model suggests the potential interaction between these two regions. One-tailed P values were generated using a {chi}2 test with 1 df for all hypotheses tested (24). Significance for a single test of interaction was assessed at a type I error rate of 0.05/N according to Bonferroni adjustment for multiple comparisons. N was the number of independent tests.


   Results
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 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Single-locus linkage analysis

According to SOLAR, the heritability of AAM in our sample was 0.59, with an SE of 0.05, indicating that a substantial proportion of the variation in AAM is attributable to genetic effects.

Figure 1sGohows the genomewide linkage signals (multipoint LOD scores) of autosomes for AAM. Figure 2Go and Table 3Go present the major findings of this study. The strongest linkage signal was detected at the genomic region of 22q13 (LOD score, 3.70; P < 0.0001). In addition, we identified suggestive linkage evidence on 22q11 (LOD, 2.68; P = 0.0003) and 11q23 (LOD, 1.98; P = 0.0015), respectively. The proportions of the total variance explained by the QTLs at these genomic regions (22q13, 22q11, and 11q23) were 20, 16, and 16%, respectively. We did not detect any notable results on chromosome X (all LOD scores, ≤0.5).


Figure 1
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  		FIG. 1. Multipoint linkage signals on autosomes for AAM.

 

Figure 2
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  		FIG. 2. Chromosome regions showing at least suggestive linkage (LOD, ≥1.9) for AAM.

 

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  		TABLE 3. Main results of multipoint linkage analyses for AAM

 
Epistatic interaction analysis

Table 4Go lists the results of the epistatic interaction analyses. With stringent criteria for statistical significance (P = 0.05/4 or 0.0125), only one significant epistatic interaction was achieved, which was between 22q13 and 3q13 (P = 0.005). Figure 3sGohows the linkage signals on chromosome 3 under the single-locus, two-locus additive, and two-locus epistatic models.


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  		TABLE 4. Testing for epistasis between 22q13 and loci with single-locus LOD ≥ 1.0

 

Figure 3
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  		FIG. 3. Analysis of epistatic interaction between 22q13 and 3q13 for AAM.

 

   Discussion
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 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In this study we conducted a large-scale, genomewide scan for QTLs that influence menarcheal age. Multipoint linkage analyses revealed significant evidence for 22q13 and suggestive linkage on 22q11 and 11q23.

To our knowledge, candidate genes of AAM in our strongest linkage region, 22q13, have not been well characterized. A potential candidate gene in this region, sterol regulatory element binding transcription factor-2 (MIM 600481), can stimulate the transcription of sterol-regulated genes and control cholesterol synthesis (25). This gene may have some effect on AAM. Based on the significant linkage signal in our study, this interesting region needs additional investigation.

Evidence for the suggestive linkage to 22q11 includes catechol-O-methyltransferase (MIM 116790), an estrogen-metabolizing gene involved in the estrogen-initiating mechanism. The lower activity of catechol-O-methyltransferase may influence the timing of menarche through the effect of estrogen-metabolizing enzyme (14). A potential candidate gene located in the other suggestive linkage region, 11q23, is the progesterone receptor (PGR; MIM 607311) gene. PGR mediates the physiological effects of progesterone and plays a central role in the reproductive events associated with the establishment and maintenance of pregnancy (26). Therefore, from a functional aspect, PGR may have impact on the variation in AAM.

Another interesting finding of this study was the significant epistatic effect of 22q13 and 3q13. Although the linkage signal on 3q13 (LOD, 1.49) was modest with the one-locus model, when epistasis with region 22q13 was taken into account in statistic analyses, the signal (LOD, 2.88) was greatly enhanced. This may suggest that genes located on 3q13 do not have a strong effect on AAM alone, but significantly influence menarche through interaction with genes on 22q13. However, because these two regions contain many genes, it is difficult to determine which two genes interact. Additional studies are needed to confirm our results and characterize the biological mechanisms of the observed interaction.

The estrogen receptor {alpha} gene (6q25) has been reported in association with AAM in several groups (10, 11). Our study detected a peak in region 6q25, with a linkage signal LOD of 0.85 which translates into a P value of 0.025. Although this kind of LOD score and P value cannot be construed as significant or even suggestively significant in a whole genome scan, they actually constitute significant evidence of replication. This is because the P value for replication is much less stringent for replication that is specific hypothesis based than for whole genome scans (27, 28).

Our data did not support several previous association studies related to puberty or AAM with gene loci such as SHBG (17q13) (12), CYP17 (10q24) (14), and the KiSS-1 (1q32)/GPR54 (19p13) system (29, 30), which may arise from the specific variance of these loci maybe too low to be detected by linkage. Furthermore, the differences in sample size or ethnic origin, genetic heterogeneity, or assessing methods are likely to be the explanation. Our results suggest that the genetic susceptibility to AAM comprises a number of loci, each with a modest effect, and that alternative strategies are required for the detection of most of these loci.

Compared with previous studies, a clear advantage of our study is the exceptionally large sample, which contains a large number of informative relative pairs for linkage analysis. This may give our study higher statistical power than previous ones (31).

Phenotypic definition and assessment are critical issues for genetic investigation of human complex traits. AAM can be assessed only in retrospect, sometimes many decades after the events occurred, which may increase the likelihood of error. However, because menarche is a significant milestone in a woman’s life, a recent study found a correlation of 0.79 between the original AAM and the information recalled 30 yr later (32). Many previous studies have demonstrated that it was remembered accurately by most subjects independently of their chronological age, and it was reasonable and reliable to use the retrospective method to acquire the AAM (33, 34, 35).

In summary, we performed a large-scale, genomewide, linkage scan to identify loci responsible for the variability in AAM, in which several chromosomal regions were detected, including 22q13 with significant linkage and 22q11 and 11q23 with suggestive linkage. We also found a significant epistatic interaction effect between 22q13 and 3q13. All these findings require independent replication and confirmation. Once linkage to a genomic region is confirmed, subsequent saturation linkage mapping, followed by linkage disequilibrium analyses with dense single nucleotide polymorphism markers within positively identified regions can confine the QTL to small genomic regions, which is amenable to positional cloning.


   Footnotes
 
First Published Online January 4, 2006

Abbreviations: AAM, Age at menarche; PGR, progesterone receptor; QTL, quantitative trait loci.

This work was supported by National Institutes of Health Grants K01-AR-02170-01, R01-AR-050496-01, and R01-GM-60402-01A1 (to H.-W.D.) and grants from the National Science Foundation of China, the Huo Ying Dong Education Foundation, HuNan Province, Xi’an Jiaotong University, and the Ministry of Education of China. The genotyping experiment was performed by Marshfield Center for Medical Genetics and was supported by National Heart, Lung, and Blood Institute, Mammalian Genotyping Service (Contract HV48141).

Disclosure of Potential Conflicts of Interest: All of the authors have nothing to declare.

Received October 3, 2005.

Accepted December 27, 2005.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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