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The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 9 3534-3537
Copyright © 2006 by The Endocrine Society

Weight-Adjusted Genome Scan Analysis for Mapping Quantitative Trait Loci for Menarchal Age

Anya Rothenbuhler1, Delphine Fradin1, Simon Heath, Hervé Lefevre, Claire Bouvattier, Marc Lathrop and Pierre Bougnères

Department of Pediatric Endocrinology and U561 (A.R., D.F., H.L., C.B., P.B.), Paris 5 René Descartes University, Institut National de la Santé et de la Recherche Médicale, Hôpital Saint-Vincent de Paul, 75014 Paris, France; and Centre National de Génotypage (S.H., M.L.), 91057 Evry, France

Address all correspondence and requests for reprints to: Pierre Bougnères, Department of Pediatric Endocrinology and U56, Institut National de la Santé et de la Recherche Médicale, Hôpital Saint-Vincent de Paul, 75014 Paris, France. E-mail: pierre.bougneres{at}wanadoo.fr.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Discussion
 References
 
Context: Twin and family studies indicate that genetic factors contribute to the variability of age at menarche (AAM), a multifactorial trait of major importance to human reproductive success. Individual variability of premenarcheal fatness is known to be an important determinant of AAM.

Objective: The objective of the study was mapping quantitative trait loci (QTLs) for AAM.

Design and Methods: AAM was assessed in 98 sister pairs of recent European ancestry whose growth charts were available. There was a negative correlation between menarcheal body weight SD score (SDS) and AAM (r = 0.47, P < 0.0001). We designed a genome scan approach and used the variance components model implemented in Merlin for quantitative traits to evaluate linkage of AAM and AAM adjusted for menarcheal weight SDS to 418 genome-wide microsatellites.

Results: Multipoint linkage analysis for AAM revealed nominal QTLs defined by LOD scores between 1.06 and 1.69 on chromosomes 1p, 1q, 7p, 8q, 16p, 19q, and 20q. The genome scan for AAM adjusted for menarcheal weight SDS revealed several QTLs with strongly suggestive LOD scores in 16q21 (LOD = 3.33), 16q12 (LOD = 3.12), and 8p12 (LOD = 2.18) and a number of other nominally significant QTLs yet viewed as hypothetical.

Conclusions: We found several regions that may contain determinants of AAM, but there is still a long series of steps to confirm these QTLs and identify the genomic polymorphisms implicated in AAM variability.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Discussion
 References
 
MENARCHE, THE HALLMARK maturational event of female adolescence, inaugurates reproductive life in humans. Throughout the evolutionary history of our species, it is likely that age at menarche (AAM) has been a central component of the net reproduction rate and thus of the Darwinian fitness of humans during their limited life span (1). It can thus be hypothesized that genotypes issued from natural selection could affect the age-specific probability of women bearing these genotypes to reproduce. The timing of menarche is strongly influenced by environmental factors. Within the last 30 yr, studies in various parts of the world have shown that the AAM has fallen as child health has improved (2, 3). Evidence for body fatness as a regulator of fecundity is conflicting (4) but is best documented for age at puberty or menarche. There is ample evidence of delayed puberty being associated with poorer childhood nutrition (5, 6). In particular, early onset of puberty is reported in children of populations who migrated from developing to developed regions (5) or countries (7) or belong to different socioeconomic classes of the same population. Higher relative weight is consistently associated with increased likelihood of reaching menarche (8). On the other side, many studies indicate a significant genetic contribution to the timing of the onset of menstruation (9) and suggest that approximately half of the phenotypic variation among girls from developed countries in the timing of menarche is due to genetic factors. The importance of such genetic factors is suggested by the effect of ethnicity on pubertal maturation (10) by twin studies, which attribute 53–74% of AAM variation to genetic effects (11, 12, 13, 14, 15); the Fels Longitudinal Study, which estimated AAM heritability to be close to 50% (16, 17); or the strong correlation between AAM of mothers and daughters (15, 18). Constitutional delay of sexual maturation, the extreme end of normal pubertal timing, clusters in families and has a strong genetic component (19, 20). The specific genetic determinants of AAM yet remain unknown.

The current study investigates the quantitative genetics of pubertal timing. A few months ago, the first genome-wide linkage study on the onset of puberty identified several suggestive and nominal quantitative trait loci (QTLs) in a large Caucasian sample recruited in the United States (21). The current study takes a complementary approach in women whose weight kinetics at adolescence were known to us, with the aim to identify specific QTLs contributing to the variation of AAM. Here we report results from this genome scan approach.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Discussion
 References
 
Patients

The study population included 150 women of recent European ancestry from 69 families; 59 pedigrees of two, seven pedigrees of three, and three pedigrees of four sisters leading to a total of 98 sister pairs. All participants were healthy volunteers and signed informed consent documents before entering the study. The geographic origin of the subjects was assessed through family history, analysis of patronymic names, and grandparents’ birthplace.

Anthropometric and pubertal development data were collected based on growth charts and medical interviews. Age at menarche was recalled from women using both a status quo method and a direct AAM recall method (2), which have both been validated by epidemiological studies (22, 23). The status quo method is based on the tracking year by year of the onset of menarche, starting at age 9 yr, during precise interviews of the women participating to this study, whereas the recall method is simply asking the women when they menstruated for the first time. Menarcheal weight and height were derived from the analysis of each sister’s growth curve: it represented the height and weight in kilograms and SD score (SDS) at the time of occurrence of menarche. Clinical data of the cohort are presented in Table 1Go. The median age of participants was 29.3 ± 10.5 yr and the median age at menarche 13.1 ± 1.4 yr, a normal value in Western European countries (2).


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  		TABLE 1. Clinical characteristics of the studied sisters

 
Genotyping

Whole blood obtained from each family participant was frozen and stored at –20 C. Genomic DNA was extracted from the blood samples with an extraction kit (Gentra, Minneapolis, MN). A genome scan with a total of 420 microsatellites, at an average spacing of 10 cM, on all 22 autosomes and the X chromosome was realized by the Centre National de Génotypage (Evry, France).

Statistical analyses

We used the variance components model (24) implemented in Merlin (25) for quantitative traits to evaluate linkage to the 418 microsatellites taken predominantly from ABI (Applied Biosystems, Foster City, CA). Full details on these markers and the genotyping procedures can be found elsewhere (https://products.appliedbiosystems.com/ab/en/US/adirect/ab?cmd=catNavigate2&catID=600776&tab=Literature). Thevariance component methods ignore detailed aspects of any model underlying the trait mode of inheritance and is based on the inference of the correlation between relatives’ similarity with respect to the trait and their similarity with respect to one or more markers. P values accompanying LOD score values in this manuscript reflect the significance of linkage to each microsatellite marker. Criteria for assessment of overall statistical significance at the genome-wide level, considering the testing of multiple microsatellites, are controversial in genetic linkage studies of complex traits. The choice of statistical criteria for a significant linkage depends on the balance between the risks of false positivity and false negativity that is decided by the investigators. Lander and Kruglyak (26) calculated that a relatively stringent threshold of Z>3.6 is needed to guarantee a genome-wide P < 0.05, whereas LOD scores between 2.2 and 3.6 indicate only suggestive linkage and LOD scores between 0.6 and 2.2 are nominal linkage. Although other investigators have proposed the use of more liberal thresholds for significance, we kept the Lander and Kruglyak criteria in the present study with the aim to reduce the risk of false positivity.


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Discussion
 References
 
We found that AAM and weight SDS at onset of menarche were negatively correlated (Fig. 1Go), supporting the existence of a direct age-independent relationship between relative body weight and onset of menses. The higher the weight SDS of adolescents, by reference to the healthy French population, the earlier was the onset of menstruation.


Figure 1
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  		FIG. 1. Relationship between AAM and menarcheal body weight expressed as SDS in the studied sisters. The regression between AAM and weight SDS is described by the equation y = 13.51 – 0.414 x (r = 0.47). The higher the weight SDS of adolescents, the earlier was the onset of menarche.

 
We first examined AAM unadjusted to individual body weight. Modest linkage scores were found in chromosome 1 regions, which showed only nominally significant LOD scores: 1p21-p13 and 1q23-q32 (Table 2Go). A nominal linkage for AAM was also found on chromosomes 7p, 8q, 16p, 19q, and 20q (Table 2Go).


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  		TABLE 2. Regions displaying multipoint LOD scores of 1.00 or more in the studied sisters for AAM

 
After adjustment for weight SDS at menarche, the strongest linkage signals were found on chromosome 16q (Table 3Go): on 16q21, one peak had a LOD score of 3.33 and spanned approximately 20 cM, and on 16q12 another peak had a LOD score of 3.12 and spanned approximately 10 cM. According to Lander and Kruglyak criteria, several other regions showed nominal evidence for linkage: 1p, 2q, 4q, 7q, 8p, 9q, 10p, 15q, 16p, and 22q (Table 3Go).


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  		TABLE 3. Regions displaying multipoint LOD scores of 1.00 or more in the studied sisters for AAM adjusted for menarcheal weight SDS

 

   Discussion
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
References
 
The genetic control of the timing of puberty is of importance to human physiology and medicine (27). To be able to assess quantitative traits amenable to genetic analysis, we initially considered both age at onset of breast development and age at onset of menses (AAM). But we rapidly confirmed, as observed by others (18, 19), that AAM is the only reliable index of pubertal timing in a retrospective study of the general population due to inaccuracies in the recall of breast development.

According to the theory of the oligogenic control of a complex trait (28), the current genome scan analysis tried to identify chromosome regions that may contain significant QTLs underlying the variation of AAM. Multipoint linkage analysis revealed that none of the LOD scores allowed identification of a truly significant QTL because a LOD score of 3.6 is needed to be sure of the presence of a menarcheal gene in linkage with a tested microsatellite. We nevertheless found strongly suggestive evidence for two QTLs on chromosomes 16q21 and 16q12, with LOD scores of 3.33 and 3.12. This region contains more than 70 genes, none of which is known to be prominently implicated in the control of sexual maturation.

Our data suggest that several nominally significant QTLs influence AAM. Although the present study provides no positional information allowing to suspect specific candidate genes, we listed, as is often done in genome scan studies, the genes encompassed by the putative QTLs. For example, a LOD score of 1.69 was located on chromosome 1q in the region harboring KISS1 gene, an important regulator of puberty (29). The nominal linkage to 16p13 includes IGFALS (IGF binding protein, acid labile subunit), whose inactivation is associated with delayed puberty (30). A nominally significant QTL was also found on 8p12 (LOD 2.18), which harbors GNRH1 and LEPROTL1 (leptin receptor overlapping transcript-like 1). An extensive study of the GNRH1 locus by Sedlmeyer et al. (31) showed no association with puberty onset in male and female probands with late pubertal development or among a multiethnic cohort recruited in Hawaii and California. The human LEPROTL1 gene shares 67% homology with the leptin receptor (LEPR), but nothing is known yet about the function of this gene. Another QTL was located on 22q11-q13. This region has previously been found by Guo et al. (21) with a LOD score of 3.70 on 22q13 and 2.68 on 22q11. According to these authors, there are two potential candidate genes in this region: SREBF2 (sterol regulatory element binding transcription factor 2) and COMT (catechol-O-methyltransferase).

In the genome scan study performed by Guo et al. (21), several regions with a LOD greater than 1.0 were detected as QTLs for AAM. Except for 22q, the current study shows no evidence for linkage with any of these loci. This observation suggests that the loci found by Guo et al. reflect gene effects that are difficult to detect in a smaller sample of sister pairs. The smaller size of our cohort was dictated by the fact that we had difficulties finding sister pairs with growth charts containing reliable weight data until completion of puberty. It is more surprising that the genomic localizations reported in the present study were not found by Guo et al. This lack of replication could stem from our nominally significant localizations being false positives. It is possible, however, that our QTLs, especially those with LOD scores at 3.12 and 3.33, are involved in weight-independent variability of AAM that are masked by larger effects of weight variability in the study by Guo et al.

Instead of positional genome-wide random linkage, another strategy would have been to use association approaches (32) as attempted by other investigators. Genes necessary to pubertal development, such as GNRHR, GPR 54, or FGFR1, have been highlighted by human monogenic disorders (29, 33, 34, 35), but there is no indication that they are involved in the normal variation of pubertal timing. An extensive study of the GNRHR locus showed nominal associations between timing of menarche and three single-nucleotide polymorphisms (SNPs) of the GNRHR gene in 125 trios (male and female probands with late pubertal development and their parents) and, interestingly, with a different SNP of GNRHR among a multiethnic cohort recruited in Hawaii and California (31). We found, however, that the vast number of pathways and candidate genes that could be involved in the physiological timing of puberty (36, 37) was overwhelming. Another approach would have been to derive candidate QTL regions or candidate genes from genome scans in other mammalian species (38, 39), but this would be exposed to the risk that the variability of sexual maturation including AAM is physiologically and genetically specific of the human species.

In summary, we view the current genome scan approach as a first step to identify loci responsible for AAM variability. Our results suggest that there are QTLs for AAM on 16q21 and 16q12 in European women and possibly in a few other regions giving weaker linkage signals. Because attempts to identify QTLs for multifactorial traits on the human genome in outbred populations face a major risk of false positivity, our findings need to be replicated by other investigators in other cohorts of Caucasian or non-Caucasian women. Further refinement of the indicated genomic intervals requires the study of hundreds of SNPs and microsatellite markers within these regions, a fine mapping process that will hopefully allow identification of genes responsible for the variation of pubertal timing.


   Acknowledgments
 
We are grateful to an anonymous reviewer of this journal for improving the pertinence of our statistical analysis.


   Footnotes
 
This work was supported by the Alfred Jost Research Fellowship from Serono Laboratory (to D.F.).

Disclosure statement: The authors have nothing to disclose.

First Published Online June 27, 2006

1 A.R. and D.F. contributed equally to this work. Back

Abbreviations: AAM, Age at menarche; QTLs, quantitative trait loci; SDS, SD score; SNP, single-nucleotide polymorphism.

Received January 23, 2006.

Accepted June 19, 2006.


   References
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 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 

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