Polymorphism in the insulin-like growth factor 1 gene is
associated with age at menarche in caucasian females
Jian Zhao1*, Dong-Hai Xiong2*, Yan Guo1*, Tie-Lin Yang1, Robert R Recker2
and Hong-Wen Deng1,2,3,4
1The 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, People’s Republic of China;2Osteoporosis Research Center and
Department of Biomedical Sciences, Creighton University, Omaha, NE 68131, USA;3Department of Orthopedic Surgery, School of
Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA
4To whom correspondence should be addressed at: School of Medicine, Department of Basic Medical Science, University of Missouri/
Kansas City, Room: M3-CO3, 2411 Holmes Street, Kansas City, MO 64108-2792, USA. Tel: þ816-235-5354, Fax: þ816-235-6517,
BACKGROUND: The insulin-like growth factor 1 (IGF1) gene, which plays a crucial role in hypothalamic–
pituitary–ovarian hormone-controlled metabolic processes, may influence the onset of menarche. Our study aimed
to test association between IGF1 polymorphisms with the variation of age at menarche (AAM) in Caucasian
females. METHODS: We recruited a sample of 1048 females from 354 Caucasian nuclear families and genotyped
19 single-nucleotide polymorphisms (SNPs) spanning the entire IGF1 gene. Pairwise linkage disequilibrium among
SNPs was measured, and the haplotype blocks were inferred. Both single SNP markers and haplotypes were tested
for association with AAM using the quantitative transmission disequilibrium test. RESULTS: Significant association
(P 5 0.0153) between AAM and SNP3 (rs6214) in block1 was detected. CONCLUSIONS: Our results suggested a
potential effect of SNP3 in the IGF1 gene on AAM variation in Caucasian women for the first time. However,
further independent studies are needed to confirm our findings.
Keywords: association; insulin-like growth factor 1; age at menarche; haplotype
Menarche, the beginning of menstruation, is the cornerstone in
women’s lives (Ku et al., 2006). Early menarche is associated
with elevated risk of ovarian tumors and breast cancer
(Geschwind and Galaburda, 1985; Trichopoulos, 1990).
Delayed menarche may increase the risk of osteoporosis
(Eastell, 2005). Thus, it is valuable and necessary to explore
the potential factors influencing the variation of age at menarche
AAM is determined by different environmental and genetic
factors(van Noord et al., 1997; Worda et al., 2004). It was
reported that 53–74% of the variation in AAM could be due
to genetic effects (Kaprio et al., 1995; Sharma, 2002). For
example genetic linkage and association studies have
suggested several quantitative trait loci (Guo et al., 2006)
and candidate genes for AAM (Gorai et al., 2003; Xita et al.,
2005). However, these findings only partly explain the
genetic architecture underlying AAM variation, and more
AAM-influencing genetic factors wait to be found.
Insulin-like growth factor 1 (IGF1) plays a crucial role in
hypothalamic–pituitary–ovarian hormone-controlled metabolic
processes (Jones and Clemmons, 1995) and is the major
effector of bone growth (Khosla, 1997; Kawai et al., 1999). It
is known that GnRH is the key regulator of the reproductive
system, directly regulating the release of LH and FSH that are
essential for the normal function of the gonads (Chandrashekar
and Bartke, 2003). Hypothalamic GnRH mRNA expression
increased at 3.5 months and declined by 6 months of age
during puberty in the gilt (Barb et al., 2006). The increment in
IGF1 might be involved in the stimulation of GnRH activity
more, previous studies have shown that IGF1 is a metabolic
signal capable of activating the GnRH/LH-releasing system at
the time of puberty in rats (Hiney et al., 1991) and IGF1 is
capable of enhancing FSH-stimulated LH receptor expression
(Adashi et al., 1985c). IGF1 and FSH can synergize to stimulate
progesterone production in primary cultures of maturing human
granulosa cells (LaVoie et al., 1999). IGF1 also enhances
FSH-mediated steroidogenesis, including estradiol (E2) and pro-
gesterone production (Bergh et al., 1991 Adashi et al., 1985a;
*These authors contributed equally to this work.
# The Author 2007. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology.
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Human Reproduction pp. 1–6, 2007
Hum. Reprod. Advance Access published March 21, 2007
by guest on June 3, 2013
Veldhuis and Demers, 1985; Veldhuis and Rodgers, 1987;
Erickson et al., 1989, 1991). All of these findings suggest that
the IGF1 may play significant roles in pubertal development,
menarche, the menstrual cycle, fertility and reproduction
system (Hiney et al., 1996; Huang et al., 1998; Spiliotis,
2003). Menarche depends on the maturation of female reproduc-
tive system. Early Menarche is associated with early onset of
ovulatory cycles (Hickey and Balen, 2003). It has been reported
that hypothalamic–pituitary–ovarian axis controls the age of
first ovulation and occurrence of menarche (Beitins, 1981;
Hickey and Balen, 2003; Loucks, 2006). It is therefore possible
that IGF1 may have an effect on the onset of menarche.
Based on this hypothesis, we tested IGF1 gene single
nucleotide polymorphisms (SNPs) comprehensively for an
association with AAM variation using the quantitative trans-
mission disequilibrium test (QTDT) in a large sample of
Materials and methods
This study was approved by the Creighton University Institutional
Review Board. Signed informed-consent documents were obtained
from all participants before they entered the study. Women with
chronic diseases and conditions that might potentially affect bone
mass, structure, or metabolism were excluded. These diseases/con-
ditions included chronic disorders involving vital organs (heart,
lung, liver, kidney and brain), serious metabolic diseases (diabetes,
hypo- and hyper-parathyroidism, hyperthyroidism, etc.), other skeletal
diseases (Paget disease, osteogenesis imperfecta, rheumatoid arthritis,
etc.), chronic use of drugs affecting bone metabolism (hormone repla-
cement therapy, corticosteroid therapy and anti-convulsant drugs) and
malnutrition conditions (such as chronic diarrhea, chronic ulcerative
colitis, etc.). Initially, all of the 1873 participants from 405 nuclear
families were US Caucasians of European origin recruited for
complex diseases or traits research. For menarche study, we selected
families that contain at least two informative female subjects (?1
daughters) with AAM data. Therefore, 50 families having only male
offspring and 1 family without enough AAM data for daughters
were excluded. At last, 1051 female subjects from 354 nuclear
families were studied, with the ages ranging from 19 to 85 years old.
For each study subject, the detailed medical information including
menstrual history was recorded by nurse-administered questionnaire.
AAM was calculated as the date of menarche following the onset of
menses minus the date of birth (in years rounded to the first decimal
place. After excluding three outliers whose AAM were 18 years or
older (diagnosed as primary amenorrhea), our AAM data followed
normal distribution as verified by the Kolmogorov–Smirnov test
implemented in the software Minitab (Minitab, Inc., State College,
PA, USA). The final sample used in the association analysis consisted
of 1048 females and their AAM ranged from 8.5 to 17.0 years with a
mean of 13.0 (SD ¼ 1.4).
SNP selection and genotyping
A total of 19 SNPs in and around IGF1 gene were selected on the basis
of the following criteria: (i) validation status, namely, SNPs we
selected exist in Caucasian, (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 and
(v) reported to SNP database by various sources. Genomic DNA
was extracted from whole blood using a commercial isolation kit
(Gentra Systems, Minneapolis, MN, USA) following the procedure
detailed in the kit. DNA concentration was assessed by a DU530
UV/VIS spectrophotometer (Beckman Coulter, Inc., Fullerton, CA,
USA). All the subjects were genotyped, yielding a large set of geno-
type data that are further subject to Mendelian inheritance checking,
allele frequency estimation and haplotype reconstruction. Nineteen
SNPs were successfully genotyped using the high-throughput Bead-
Array SNP genotyping technology of Illumina Inc (San Diego, CA,
USA). The average rate of missing genotype data was reported by Illu-
mina to be ?0.05%. The average genotyping error rate estimated
through blind duplicating was reported to be less than ?0.01%. The
nineteen SNPs were spaced ?85 kb apart on average and covered
the full transcript length of the IGF1 gene.
PedCheck(O’Connell and Weeks, 1998) was used to check Mendelian
removed. Then the error checking option embedded in Merlin (Abeca-
sis et al., 2002) was run to identify and disregard the genotypes flank-
ing excessive recombinants, thus further reducing genotyping errors.
Allele frequencies for each SNP were calculated by allele counting,
and the Hardy–Weinberg equilibrium was tested using the PED-
STATS procedure embedded in Merlin. Population haplotypes and
their frequencies were inferred for each of the nineteen genes using
PHASE v2.1.1 software among 703 unrelated parents. Linkage dise-
quilibrium (LD) structure for each gene was defined, using GOLD
(http://www.sph.umich.edu/csg/abecasis/GOLD/), to chart pair-
wise D’ statistics derived from haplotype data. HaploBlockFinder
(http://cgi.uc.edu/cgi-bin/kzhang/haploBlockFinder.cgi/) was used
to identify block structures and select haplotype-tagging SNPs
(htSNPs) of each candidate gene. To infer haplotypes defined by the
tagging SNPs within each block of the gene for all of the subjects
among 405 families, we adopted the algorithm of integer linear pro-
gramming 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 (Li and
Jiang, 2005). The QTDT software (http://www.sph.umich.edu/csg/
abecasis/QTDT/) (Allison, 1997; Abecasis et al., 2000) was used
to test each SNP and haplotype with estimated frequencies .5% for
association with AAM variation. Using QTDT, we also performed
1000 permutations to adjust for the potential multiple-testing problem.
Allele frequencies, LD and haplotype reconstruction
The information of all the IGF1 SNPs is summarized in
(Table 1). The MAFs of these SNPs ranged from 0.06 to
0.41. All the SNPs were in Hardy–Weinberg equilibrium
(P . 0.01). LD block structure of IGF1 is shown in Fig. 1.
We identified three significant LD blocks, with the sizes
of 13.6, 53.9 and 17.3 kb, respectively. Block 1 ranged from
the 30untranslated region to exon4 with SNP2 and SNP3
selected as htSNPs. Block 2 extended from intron3 to intron2
with SNP4 and SNP13 selected as htSNPs. Block 3 covered
from intron2 to the promoter region with SNP16, SNP17 and
SNP19 selected as htSNPs. SNP15 and rs5742629 had no
strong LD with any other SNPs and thus cannot be assigned
Zhao et al.
Page 2 of 6
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to any of the three blocks. The detailed information for blocks
is presented in (Table 2).
Table 3 summarizes association results of IGF1 gene with
AAM in nuclear families by QTDT. We tested all htSNPs
and haplotypes for association with AAM. We did not find
any evidence of population stratification for either single
SNP markers or haplotypes. For single locus analysis, we
detected significant association for SNP3 (P ¼ 0.0153),
which lies in block 1. We also detected evidence of association
with AAM for haplotype GA (P ¼ 0.0243) in block 1. These
two associations both reached the permutation-established
experiment-significance level of a ¼ 0.026. Thus the associ-
ation evidence matched at both the single SNP and haplotype
levels. We further assessed the effect of different haplotypes
in block 1 on AAM. The mean AAM for subjects carrying
the GA haplotype in block 1 is 13.13 years (SD ¼ 1.44), and
for the non-carriers it is 12.86 years (SD ¼ 1.39) (P ¼ 0.0243).
Our study is the first to suggest that IGF1 may associate with
the variation of AAM. The protein encoded by the IGF1
gene is involved in the biosynthesis of ovarian hormones and
Figure 1: LD pattern of the IGFI gene. Blocks with all pairwise jD0j
values .0.8 are illustrated and numbered 1–3. SNP identifications
correspond to those in Table 1. Haplotype-tagging SNPs were
labeled in bold. SNP15 had no LD with any other SNPs and cannot
be assigned to any of the blocks. UTR, untranslated region.
Table 1: SNP marker information for the IGF1 gene
aSNP identification in the SNP database (www.ncbi.nlm.nih.gov/SNP);
bchromosome position in SNP database (www.ncbi.nlm.nih.gov/SNP).
Table 2: Haplotype information
ahaplotypes were reconstructed by SNP2, SNP3;
bhaplotypes were reconstructed by SNP4, SNP13;
ccorresponding to SNP15;dhaplotypes were
reconstructed by SNP16, SNP17 and SNP19.
IGF1 gene and age at menarche
Page 3 of 6
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present in many tissues to control cell differentiation, prolifer-
ation and apoptosis. It has been proposed that IGF1 functions
as an autocrine/paracrine regulator of ovary development.
The IGF system plays a role in the formation of the ovary
and ovum and participates in the regulation of bone metabolism
(Libanati et al., 1999). In this study, we found the significant
associations with AAM for SNP3 and the haplotype GA recon-
structed by the htSNPs (SNP3 and SNP4). SNP3 (rs6214) is
located in exon4, and although itself does not cause any
amino acid change, it may be in strong LD with certain
functional variants influencing the mRNA splicing of IGF1,
or alternatively, with certain functional alleles of exon4
changing theamino acid
However, all of the above are just hypotheses and wait to be
tested by further functional analyses.
In females, different kinds of hormones, growth factors and
cytokines are involved in the onset of menarche (Tamura et al.,
1998; Behl and Kaul, 2002; Bornstein et al., 2004; Quirk et al.,
2004). Lots of evidence showed that the IGF1 system plays
a key role in ovarian folliculogenesis (Adashi et al., 1985b;
Giudice, 1992). In vivo levels of IGF1 (Cook et al., 1993)
and IGF1 binding protein (IGFBP3) are positively correlated
with the serum E2 concentration in girls during puberty
(Suter et al., 2000; Blogowska et al., 2003; Kanbur-Oksuz
et al., 2004; Kulik-Rechberger and Janiszewska, 2004). More
interestingly, there is a higher positive correlation between
plasma E2and IGF1/IGFBP3 ratio, an index of free IGF1.
There is a positive correlation between LH and FSH with
both IGF1 and IGFBP3, and FSH and LH stimulate some
isoform of hepatic IGF1 mRNA (Ruiz et al., 2001). Concurrent
treatment with increasing concentrations of IGF1 brought
about dose-dependent increases in FSH-induced LH receptor
mRNA, a response which was 2.5-fold greater than that
induced by FSH alone at the highest concentration in rat granu-
losa cells (Hirakawa et al., 1999). On average, the production
of E2was ?2.8-fold higher when polycystic ovary disease
granulosa cells were treated concomitantly with FSH and
IGF1 than that evoked only by FSH (Erickson et al., 1990).
In all, these biological and physiological studies lend support
to our finding that the IGF1 gene polymorphisms influence
the variation of AAM.
sequenceof IGF1 protein.
Our study has several notable strengths. First, our sample is
relatively large, which includes 1048 females from 354 Cauca-
sian nuclear families. Therefore, adequate statistical power is
guaranteed to find the genetic variants of modest effect sizes
on the AAM variation. Second, we selected 19 SNPs distributed
throughout the IGF1 gene to test for its association with AAM,
rather than just testing only a few SNPs in the gene. These SNPs
comprehensively covered the entire IGF gene. Third, we
adopted the family-based association analysis to obviate the
known population stratification problem in the European
American population. All of these approaches made this study
a robust and reliable one for the follow-up research.
We estimated the power of our study using the Program
Genetic Power Calculator (GPC, http://statgen.iop.kcl.ac.uk/
gpc/qtlassoc.html). Under the condition of the conservative
significance level of 0.001 and the strong LD of jD0j ¼ 0.9,
our sample can achieve 90% power to detect the causal variants
responsible for ?4% variation of AAM.
In general, AAM is acquired by recall and self-reports,
which may increase the likelihood of error. However, study
suggests a high correction of 0.79 between recalled and
actual AAM and it is also found that recall of AAM was
generally quite good, both in precision and accuracy (Must
et al., 2002). AAM was recalled to within 1 year of the
actual event by 84% of the females (Casey et al., 1991). The
accuracy of recall of AAM was confirmed further in other pre-
viously studies (Bergsten-Brucefors 1976; Bean et al., 1979;
Greif and Ulman, 1982; Koo and Rohan, 1997) In addition,
study found that important personal experiences can produce
clear memories of a person’s circumstance at the time of the
event, that is ‘flashbulb memories’ (Pillemer et al., 1987),
and menarche was such a significant developmental event
that almost all of the women remembered the date of their
first menstruation (Golub and Catalano, 1983). Therefore,
recalled measures and self-reports should be quite valid to
In conclusion, our present study does support the hypothesis
that IGF1 variants can influence the onset age of menarche in
Caucasians, especially in the European Americans. However,
the causal functional variants underlying the observed associa-
tions are still unknown. Replication of our results and func-
tional studies are necessary to unravel the true associated
variants and the corresponding molecular mechanisms.
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 Project 30570875 supported
by National Natural Science Foundation of China, Xi’an Jiaotong
University, and the Ministry of Education of China.
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Table 3: QTDT results for the associations between IGF1 polymorphisms
and AAM variation
aSNPs listed in this table are haplotype-tagging SNPs;bP , 0.05 are labeled
Zhao et al.
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on December 22, 2006; accepted on January 2, 2007
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