Mitchell 2015 genetics of bone mass in childhood and adolescence effects of sex and maturation interactions

Genetics of Bone Mass in Childhood and Adolescence: Effects
of Sex and Maturation Interactions
Jonathan A Mitchell1, Alessandra Chesi2, Okan Elci3, Shana E McCormack4,5, Heidi J
Kalkwarf6, Joan M Lappe7, Vicente Gilsanz8, Sharon E Oberfield9, John A Shepherd10,
Andrea Kelly4,5, Babette S Zemel4,11,*, and Struan FA Grant2,4,5,*
1Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, PA, USA
2Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
3Biostatistics and Data Management Core, Children’s Hospital of Philadelphia, Philadelphia, PA,
USA
4Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA
5Division of Endocrinology and Diabetes, The Children’s Hospital of Philadelphia, Philadelphia
6Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center,
Cincinnati, OH, USA
7Division of Endocrinology, Department of Medicine, Creighton University, Omaha, NE, USA
8Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
9Division of Pediatric Endocrinology, Diabetes, and Metabolism, Department of Pediatrics,
Columbia University Medical Center, New York, NY, USA
10Department of Radiology, University of California San Francisco, San Francisco, CA, USA
11Division of Gastroenterology, Hepatology and Nutrition, The Children’s Hospital of Philadelphia,
Philadelphia, PA, USA
Abstract
We aimed to determine if adult bone mineral density (BMD) susceptibility loci were associated
with pediatric bone mass and density, and if sex and pubertal stage influenced any association. We
Address correspondence to: Struan FA Grant, PhD, Division of Human Genetics and Endocrinology, The Children’s Hospital of
Philadelphia, 3615 Civic Center Boulevard, Room 1102D, Philadelphia, PA 19104. grants@chop.edu.
*BSZ and SFAG contributed equally to this work.
Disclosures
All authors state that they have no conflicts of interest.
Authors’ roles: Study conception and design: SFAG and BSZ. Acquisition of data: SFAG, BSZ, HJK, JML, VG, SEO, and JAS. Data
analysis: JAM, BSZ, and OE. Interpretation of data: JAM, AC, OE, SEM, HJK, JML, VG, SEO, JAS, AK, BSZ, and SFAG. Drafting
manuscript: JAM, BSZ, and SFAG. Revising manuscript content: AC, OE, SEM, HJK, JML, VG, SEO, JAS, and AK. Approving final
version of manuscript: JAM, AC, OE, SEM, HJK, JML, VG, SEO, JAS, AK, BSZ, and SFAG. JAM takes full responsibility for the
integrity of the data analysis.
Additional Supporting Information may be found in the online version of this article.
HHS Public Access
Author manuscript
J Bone Miner Res. Author manuscript; available in PMC 2016 September 01.
Published in final edited form as:
J Bone Miner Res. 2015 September ; 30(9): 1676–1683. doi:10.1002/jbmr.2508.
AuthorManuscriptAuthorManuscriptAuthorManuscriptAuthorManuscript

analyzed prospective areal BMD (aBMD) and bone mineral content (BMC) data from the Bone
Mineral Density in Childhood Study (n =603, European ancestry, 54% female). Linear mixed
models were used to assess if 77 single-nucleotide polymorphisms (SNPs) near known adult BMD
susceptibility loci interacted with sex and pubertal stage to influence the aBMD/BMC; adjusting
for age, BMI, physical activity, and dietary calcium. The strongest main association was observed
between an SNP near C7orf58 and distal radius aBMD. However, this association had a significant
sex•SNP interaction, revealing a significant association only in females (b =−0.32, p =1.8 × 10−6).
Furthermore, the C12orf23 locus had significant interactions with both sex and pubertal stage,
revealing associations in females during Tanner stage I for total hip aBMD (b =0.24, p =0.001) and
femoral neck aBMD (b =0.27, p =3.0 × 10−5). In contrast, the sex•SNP interactions for loci near
LRP5 and WNT16 uncovered associations that were only in males for total body less head BMC
(b =0.22, p =4.4 × 10−4) and distal radius aBMD (b =0.27, p =0.001), respectively. Furthermore,
the LRP5 locus interacted with both sex and pubertal stage, demonstrating associations that were
exclusively in males during Tanner V for total hip aBMD (b =0.29, p =0.003). In total, significant
sex•SNP interactions were found at 15 loci; pubertal stage•SNP interactions at 23 loci and 19 loci
interacted with both sex and pubertal stage. In conclusion, variants originally associated with adult
BMD influence bone mass in children of European ancestry, highlighting the fact that many of
these loci operate early in life. However, the direction and magnitude of associations for a large
number of SNPs only became evident when accounting for sex and maturation.
Keywords
DXA; GENETIC RESEARCH; GENERAL POPULATION STUDIES; CHILDHOOD;
PUBERTY
Introduction
Optimizing peak bone mass (PBM) in early adulthood is one of the most important factors in
preventing osteoporosis(1) and fracture later in life.(2,3) Epidemiological studies suggest that
a 10% increase in PBM at the population level would decrease the risk of fracture later in
life by 50%.(4) Lifestyle factors influence the accumulation and loss of bone over the life
course.(5) However, there is strong evidence for a genetic component in the predisposition of
osteoporosis, with an estimated 60% to 80% of the variability in the risk explained by
heritable factors.(6–8) Twin studies also suggest that genetic predisposition determines up to
80% of PBM.(9) Thus, understanding the genetic contribution to PBM is critical to
developing effective intervention strategies for the prevention of osteoporosis and
fractures.(10)
In 2008, OPG (TNFRSF11B) and LRP5 were the first adult bone mineral density (BMD)
susceptibility loci identified in a genome-wide association study (GWAS).(11) To date, 56
adult BMD associated loci and 14 fracture risk–associated loci have been identified.(12)
However, the extent to which these loci impact bone accrual in early life prior to PBM
versus affecting bone loss post-PBM is not known. A systematic investigation to assess
whether adult BMD susceptibility loci are associated with bone density and content during
childhood and adolescence is needed.
Mitchell et al. Page 2

To investigate the potential role of adult bone loci on pediatric bone mass accrual, sex and
pubertal stage need to be taken into account. During their lifetime women lose about 30% to
50% of PBM, whereas men lose 20% to 30% of PBM;(13) the risk of osteoporosis is greater
in women relative to men. Further, 26% of PBM is gained during the 2 years of peak bone
accretion during adolescence(14) and this pattern suggests that the regulation of bone
accretion varies across maturational stages. Using a longitudinal study design and subjects
of European ancestry, we aimed to determine if adult bone loci were associated with BMD
or bone mineral content (BMC) during childhood, and if consideration of sex and pubertal
stage provided new insights into such associations.
Subjects and Methods
Study sample
Participants from the Bone Mineral Density in Childhood Study (BMDCS) were invited to
donate a blood or saliva sample at their final study visit. The BMDCS was a multicenter
longitudinal study to establish norms for BMC and areal-BMD (aBMD) for children 5 to 20
years of age in the United States. Children were recruited from Children’s Hospital of Los
Angeles (Los Angeles, CA), Cincinnati Children’s Hospital Medical Center (Cincinnati,
OH), Creighton University (Omaha, NE), Children’s Hospital of Philadelphia (CHOP)
(Philadelphia, PA), and Columbia University (New York, NY).(15,16) Females aged 6 to 15
years and males aged 6 to 16 years were enrolled in 2002–2003 and were measured annually
for 6 years (up to 7 visits). Additional older (age 19 years) and younger (age 5 years)
subjects were enrolled in 2006–2007 and evaluated annually for 2 years (up to 3 visits) to
extend the reference percentiles from ages 5 to 20 years.
Criteria for BMDCS entry were selected in order to enroll normally developing children
with healthy bones. Key criteria included term birth (≥37 weeks’ gestation), birth weight
>2.3 kg, no evidence of precocious or delayed puberty, and height, weight, or BMI within
the 3rd to the 97th percentiles for age. Children were excluded for multiple fractures (more
than two fractures if age <10 years or more than three fractures if age >10 years), current or
previous medication use or medical condition known to affect bone health, and extended bed
rest. Same sex siblings were excluded, but opposite sex siblings were not excluded from
participation in BMDCS, because separate aBMD references curves were generated for
males and females.
Written informed consent was obtained from the study participants 18 years and older. For
participants less than 18 years of age, consent was obtained from the parent or guardian and
assent was obtained from participants. The protocol was approved by the Institutional
Review Board of each Clinical Center.
Genotyping
We performed high-throughput genomewide SNP genotyping, using the Illumina Infinium II
OMNI Express plus Exome BeadChip technology (Illumina, San Diego, CA, USA), at
CHOP’s Center for Applied Genomics, as described.(17) The SNPs analyzed survived the
filtering of the genome wide dataset for SNPs with call rates <95%, minor allele frequency

<1%, missing rate per person >2%, and Hardy-Weinberg equilibrium p < 1 × 10−5. We used
previously reported SNPs or the best one or two surrogate SNPs available based on the CEU
HapMap. In our study, we assessed 77 SNPs in or near known adult bone mass loci that
were investigated using additive models.(11,12,18,19)
Bone phenotypes
DXA scans were obtained using Hologic, Inc. (Bedford, MA, USA) bone densitometers
(QDR4500A, QDR4500W, Delphi A, and Discovery models). Scans were obtained
following manufacturer guidelines for patient positioning at the lumbar spine, proximal
femur, forearm, and total body by trained research technicians using standardized protocols.
Scans were centrally analyzed by the DXA Core Laboratory (University of California, San
Francisco) using Hologic software version 12.3 for baseline scans and Apex 2.1 for follow-
up scan analysis using the “compare” feature. aBMD values for the spine, total hip, femoral
neck, and distal 1/3 radius, and BMC of the total body less head (TBLH) were adjusted
based on the cross-calibration of DXA devices and longitudinal calibration stability using
anthropomorphic spine and hip phantoms, and the Hologic whole-body phantom. BMC
(rather than aBMD) of the TBLH was used because it is the preferred measure of bone status
for the total body when adjusted for body size.(20,21) aBMD/BMC Z-scores were calculated
using the BMDCS reference values to account for the known increases and sex differences
in aBMD/BMC during growth and development.(16) aBMD/BMC Z-scores were adjusted for
height-for-age Z-scores as described to minimize potential confounding by skeletal size.(22)
Additional measures
Weight was measured on a digital scale and height was measured using a stadiometer.(15,16)
Height, weight, and BMI Z-scores were calculated using the CDC 2000 growth charts.(23)
Race and ethnicity were self-identified by each participant using the National Institutes of
Health and the U.S. Bureau of the Census classifications. Population ancestry was also
confirmed using the participant’s genetic information using principal components analysis.
Only participants of European ancestry by both criteria were included in our study.
Pubertal stage was assigned based on a physical examination by an experienced physician or
nurse skilled in pediatric endocrinology. The participants were categorized as prepubertal
(Tanner I), pubertal (Tanner II–IV), or postpubertal (Tanner V). Pubertal stage categorization
in the females was based on breast development and Tanner criteria,(24,25) and in the males
was based on testicular volume measured by a Prader ochidometer.(24)
Dietary calcium intake was assessed using a semiquantitative food frequency questionnaire
(FFQ) (Block Dietary Data Systems, Berkeley, CA, USA). The FFQ consisted of 45 food
and beverage items; the reported frequency and amount of intake in the last week was used
to estimate calcium intake (mg/day) using an automated computer analysis program.
Physical activity (hours/day) was estimated using an expanded version of the questionnaire
originally validated by Slemenda and colleagues.(26) Over 40 different physical and
sedentary activities were queried. Responses were tabulated to estimate hours of physical
activity per week.

Statistical methods
The number of visits per subject varied from one to seven in our longitudinal study (64%
had 7 visits). We treated the subjects as a random sample from a larger population to which
we wished to draw inferences. Using mixed-effect linear regression we modeled the
between-subject variability as a random effect (random intercept term at the subject level),
accounting for correlations arising from repeated-measures taken from each subject. Mixed-
effects models allow use of all data under assumption of missing at random. We fitted
random intercept models using the method of maximum likelihood (ML) estimation and
employed the Huber-White approach to construct robust standard errors that give valid
inferences for large sized samples. Briefly, letting “i” denote the ith subject and “j” denote
the jth visit on that subject, for a given aBMD/BMC Z-score, we specified the following
regression model examining the relationship of aBMD/BMC Z-score with time-invariant
variables (ie, variables that do not change from visit-to-visit: SNP and sex) as well as with
time-dependent variables (ie, variables that do change from visit-to-visit: age, puberty stage,
BMI Z-score, physical activity, and dietary calcium):
with and independently
The fixed portion of the model included age, BMI Z-score, physical activity, dietary
calcium, Tanner stage, sex, and SNP, providing an overall regression line representing the
population average. The random effect (ui) serves to shift this regression line up or down
according to each subject. The xtmixed procedure in Stata 12.0 (StataCorp LP, College
Station, TX, USA) was used to perform the statistical analysis. Each SNP was tested
individually as an additive trait (ie, coded 0, 1, and 2) to assess the association between each
SNP and each bone outcome adjusting for the covariates. We then included in the fixed
portion of the model a series of interaction terms. Three-way interaction effects of SNP, sex,
and puberty stage were used to determine whether any association varied as a function of
both sex and maturation stage. Two-way interaction effects of SNP by sex were used to
determine whether any association between a SNP and bone Z-scores varied as a function of
sex; and two-way interaction effects of SNP by puberty stage were used to determine
whether any association varied as a function of maturation stage. For the models that
included three-way interactions, the lower order interactions were also included. The overall
interaction p values were extracted using a “contrast” statement in Stata after fitting the
models and the sex and/or puberty stage specific beta coefficients and p values were
extracted using a “margins” statement from the same fitted model. If we observed an
interaction term with a p <0.05, we extracted the sex and/or Tanner stage-specific SNP beta
coefficients, standard errors, and p values from the model. The strata-specific SNP
associations were considered significant at p <0.05, because all loci being tested are known
to associate with adult bone density.(27,28) However, to address the issue of multiple testing,

we applied the Benjamini and Hochberg false discovery rate (FDR) method to derive
corrected p values by accounting for the number of independent loci and the number of
skeletal sites investigated for each interaction analysis.(29) Finally, we performed a series of
sensitivity analyses to assess the confidence of our finding by: (1) using restricted maximum
likelihood (REML) in place of ML; (2) removing age as a covariate; and (3) restricting our
analyses to those with more than three study visits.
Results
The final European ancestry cohort with complete data included 603 individuals at baseline
(Supporting Table 1). A total of 20 SNPs yielded at least nominal significant evidence of
association with aBMD/BMC at one or more skeletal sites (p <0.05) (Supporting Table 2).
The two loci most strongly associated with bone mass were C7orf58 and LIN7C (Table 1).
The rs13245690 effect allele (C7orf58) was associated with lower distal radius aBMD (b =
−0.20, p =1.8 × 10−4) whereas the rs7104230 effect allele (LIN7C) was associated with
lower femoral neck aBMD (b =−0.14, p = 0.005), TBLH BMC (b = −0.08, p =0.033), and
total hip aBMD (b =−0.14, p =0.010).
We then tested whether any sex•SNP interactions were associated with aBMD/BMC, and
observed 15 such interactions (p <0.05, Supporting Tables 3 and 4). Eleven of these SNPs
yielded sex-specific associations (Table 2). The previous association with C7orf58 and lower
distal radius aBMD was driven by females (b =−0.32, p =1.8 × 10−6), and the same variant
was associated with lower TBLH-BMC in females (b =−0.11, p =0.034). In contrast,
C7orf58 was associated with higher spine aBMD (b =0.19, p =0.034) and higher total hip
aBMD (b =0.17, p =0.033) in males (Table 2). Other loci associated with bone outcomes in
females included TXNDC3 (femoral neck aBMD: b =0.18, p =0.022; TBLH BMD: b =0.14,
p =0.030), MPP7 (distal radius aBMD: b =−0.24, p =0.001), and C12orf23 (TBLH BMC: b
=0.19, p =1.3 × 10−4; spine aBMD: b =0.21, p =0.002). Other loci associated with bone
outcomes in males included LRP5 (TBLH BMC: b =0.22, p =4.4 × 10−4), KLHDC5/
PTHLH (radius aBMD: b =0.29, p =0.002; spine aBMD: b =0.20, p =0.048; and TBLH
BMC: b =0.14, p =0.046), WNT16 (radius aBMD: b =0.27, p =0.001), DNM3 (femoral neck
aBMD: b =0.21, p =0.013), ESR1 (distal radius aBMD: b =−0.21, p = 0.027), and RANKL/
AKAP11 (TBLH BMD: b =−0.15, p =0.008) (Table 2). Correcting for multiple testing, the
following loci remained statistically significantly associated with aBMD/BMC in males or
females (Table 2): C7orf58 (female, distal radius), WNT16 (male, distal radius), MPP7
(female, distal radius), LRP5 (male, TBLH BMC), and C12orf23 (female, TBLH BMC).
We then tested whether any SNPs had significant interactions with pubertal stage to
influence aBMD/BMC, and observed 23 such interactions (p <0.05, Supporting Tables 3 and
5). Nine of these loci yielded maturation-specific associations (Table 3). The following loci
were particularly associated with bone mass at one or more skeletal sites before Tanner stage
V was reached: WNT4, MHC, and TNFRSF11B (Table 3). For example, the rs6993813
(TNFRSF11B) effect allele was associated with lower spine aBMD (b =−0.16, p =0.002)
and lower TBLH BMC (b =−0.12, p =0.002) during Tanner stage I. The following loci were
particularly associated with bone mass once Tanner stage V was reached: SPTNB1, LIN7C,
SOX9, and GPATCH1 (Table 3). For example, the rs7104230 (LIN7C) effect allele was

associated with lower total hip aBMD (b =−0.18, p =0.002) during Tanner stage V.
Correcting for multiple testing, the following loci remained statistically significantly
associated with aBMD/BMC in specific Tanner stages (Table 3): TNFRSF11B (Tanner I and
II–IV, spine; and Tanner I, TBLH BMC) and GPATCH1 (Tanner V, TBLH BMC).
We then tested whether any SNP had significant interactions with both sex and pubertal
stage to influence bone outcomes, and observed 19 such interactions (Supporting Tables 3
and 6). Twelve of these loci yielded sex-and-maturation-specific associations: GALNT3,
PKDCC, CTNNB1, ABCF2, TXNDC3, TNFRSF11B, CPN1, MBL2, LRP5, C12orf23,
RANKL/AKAP11, and MARK3 (Table 4). For example, specific to females during Tanner
stage I the rs12185748 (GALNT3) effect allele was associated with lower spine aBMD (b =
−0.26, p =0.001), total hip aBMD (b =−0.16, p =0.020), and TBLH BMC (b =−0.14, p
=0.015). Also specific to females, the rs1053051 (C12orf23) effect allele was associated
with higher total hip aBMD and femoral neck aBMD and the strongest associations were
observed during Tanner stage I (total hip: b =0.24, p =0.001; femoral neck: b =0.27, p =3.0 ×
10−5). Specific to males once Tanner stage V was reached, the rs9594738 (RANKL/
AKAP11) effect allele was associated with lower distal radius aBMD (b =−0.29, p =0.001)
and the rs3781586 (LRP5) effect allele was associated with higher total hip aBMD (b =0.29,
p =0.003). The C12orf23 locus interaction with sex and pubertal stage for femoral neck
aBMD is shown in Fig. 1 to illustrate a SNP interaction with both sex and maturation.
Further, Supporting Table 7 compares the direction of the SNP associations we observed
with those reported in adult GWAS. Correcting for multiple testing, the following loci
remained statistically significantly associated with aBMD/BMC in males or females at
specific Tanner stages (Table 4): GALNT3 (female/Tanner I, spine) and C12orf23 (female/
Tanner I, femoral neck).
Our findings did not change when we used REML instead of ML (data not shown).
Similarly, our findings did not change when we removed age as a covariate or when we
restricted our analysis to those with more than three study visits (data not shown).
Discussion
C7orf58 and LIN7C were the loci most strongly associated with aBMD/BMC in our
pediatric sample, before any interactions were considered. As such, the initial interpretation
was that only a small subset of bone-related loci uncovered in adult GWA studies operated in
childhood. However, these two loci and several other bone density loci first identified in
adults were subsequently found to have statistically significant interactions with sex and/or
pubertal stage to influence pediatric bone mass. Our interaction analyses revealed
associations, or stronger associations, that would have otherwise gone undetected. Indeed,
the C7orf58 locus was specifically associated with higher spine and total hip aBMD in males
and with lower distal radius aBMD and TBLH BMC in females, whereas the LIN7C locus
was most strongly associated with lower total hip aBMD during late puberty. Other notable
loci that had significant sex and/or pubertal stage interactions included genes involved in
Wnt signaling (eg, LRP5 and WNT16), RANK-OPG-RANKL signaling, and phosphate
regulation (GALNT3); and genes with unknown function with respect to bone development
(eg, C12orf23). This is the first longitudinal study to elucidate the role of adult bone density

loci on aBMD/BMC in childhood while also incorporating sex and pubertal stage
interactions. The findings support our hypothesis that genetic determinants of bone accretion
are not constant during the development of the growing skeleton and vary by sex.
Our study has several key strengths. The bone phenotypes we modeled were derived from a
highly standardized protocol that included centralized, expert analysis of DXA scans. To
accurately account for the age and sex-related patterns of bone acquisition and the known
effects of height status, we used aBMD/BMC Z-scores adjusted for height-for-age Z-scores
as outcomes. Pediatric endocrinologists or nurse specialists performed pubertal evaluations.
Our longitudinal models adjusted for growth, pubertal timing, physical activity, and diet to
assure that any associations were not likely to be related to these known influences on
pediatric bone acquisition. We performed a series of sensitivity analyses to assess the
confidence of our findings. Importantly, our findings remain consistent when we: (1) used
restricted expected maximum likelihood (REML); (2) restricted our sample to the initial
cohort with more extensive follow-up; and (3) removed age as a covariate as there was
concern of overfitting. However, our study also has weaknesses. For some of our statistical
significant interactions, no SNP associations were observed for specific sex and/or pubertal
stage categories. We may have been able to observe associations with a larger sample size;
alternatively, these loci may not play a major role in skeletal biology in childhood. We used
DXA to estimate aBMD/BMC, and, in the future, replication of our findings using
volumetric BMD and cortical and trabecular bone density outcomes will be important.
A cross-sectional study recently associated the 7q31.31 region, which includes C7orf58 and
WNT16, with TBLH BMD in children aged 6 and 10 years.(30) We repeatedly measured
aBMD/BMC bone phenotypes across all maturational stages and observed a sex interaction
with C7orf58, documenting for the first time that this gene may function differently in males
and females in influencing pediatric bone accrual. The function of C7orf58 is not known. In
contrast, its neighboring gene, WNT16, encodes a ligand for the Wnt signaling pathway
known to have a key role in bone homeostasis.(31) We observed a sex interaction with
WNT16 at the distal radius and also observed sex and/or pubertal stage interactions with
other genes involved in the Wnt signaling pathway. In particular, LRP5 (a co-receptor in
Wnt signaling) interacted with sex and was strongly associated with higher TBLH BMC and
total hip aBMD in males in our study. LRP5 was one of the earliest adult bone density loci
identified in the general population,(11,32) and mutations in LRP5 have been identified in two
rare forms of pediatric osteoporosis (juvenile primary osteoporosis and osteoporosis-
pseudoglioma syndrome).(33,34) However, this locus has not been extensively associated
with pediatric bone mass in the general population.(35) The lack of LRP5 associations with
pediatric bone mass in the general population may be masked in analyses that combine
males and females. RANK-OPG-RANKL signaling is another well-established pathway
involved in bone homeostasis,(36) and variants in this pathway have been associated with
cortical volumetric BMD at the tibia in 15-year-old children.(37–39) Although direct
comparisons cannot be made with our aBMD/BMC phenotypes, it is interesting to note that
in one of these studies a sex-RANKL interaction was tested and there was evidence that the
association was stronger in males.(37) We observed sex and/or pubertal stage interactions
with RANKL/AKAP11 and TNFRSF11B (OPG). The RANKL/AKAP11 locus was
associated with lower TBLH BMC in the males and with lower distal radius aBMD in the

males during late maturation, whereas the TNFRSF11B locus was most strongly associated
with lower distal radius aBMD, spine aBMD, and TBLH BMC during early maturation.
No established mechanisms explain why any gene would affect pediatric bone mass or
density differently by sex and pubertal stage. However, one possible explanation for the
interactions involving Wnt signaling loci is related to the sensitivity of bone to mechanical
loading. Osteocytes embedded within the bone matrix function as mechanosensors and Wnt
signaling is important for osteocyte mechanosensation.(31) Compared to wild-type mice,
Lrp5 knockout mice experience lower gains in bone mass, whereas mice with the Lrp5
G171V high bone mass mutation experience greater gains in bone mass in response to
mechanical loading.(40) In childhood, males have greater lean muscle mass and tend to gain
more lean mass during maturation;(41) males are also more likely to engage in higher-
intensity physical activities and experience less decline in physical activity during
maturation.(42,43) Such differences translate to a male skeleton exposed to greater
mechanical loading and may explain why we observed associations between LRP5, and
possibly other Wnt signaling pathway genes, and higher bone mass in males, but not in
females. We envision that biomedical scientists and clinicians will collaborate to lead
translational research to further investigate sex and developmental specific strategies that
could help to optimize skeletal health across the lifespan. The National Institutes of Health is
developing policies to help balance the study of male and female animals to allow for the
investigation of sex differences;(44) such policies will help establish mechanisms underlying
the sex and maturation differences we observed.
In conclusion, our findings suggest that multiple variants originally associated with adult
BMD do indeed influence bone mass in childhood, especially when sex and pubertal stage
are taken into account. These sex- and maturation-specific effects were most notable for loci
involved in Wnt signaling and RANK-OPG-RANKL signaling and for C7orf58 and
C12orf23, which have unknown functions. These findings underscore the need to understand
the mechanisms by which genetic determinants of bone accretion are regulated across
puberty in males and females.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
The study was funded by R01 HD58886; the Eunice Kennedy Shriver National Institute of Child Health and
Human Development (NICHD) contracts (N01-HD-1-3228, -3329, -3330, -3331, -3332, -3333); and the CTSA
program Grant 8 UL1 TR000077. The funders had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and
decision to submit the manuscript for publication.
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Fig. 1.
Example of an SNP-sex-maturation interaction at the femoral neck. SNP rs1053051
interacted with sex and maturation (p interaction = 0.002) to influence FN aBMD. The
rs1053051 effect allele was associated with FN aBMD in the females and most strongly
during Tanner stage I (b =0.27, p =3.0 × 10−5). FN =femoral neck; aBMD =areal bone
mineral density.

Table1
AssociationBetweenKnownAdultBoneLociandPediatricBoneMass(fullresultsinSupportingTable2)
ChrSNPMinorMajorMAFNearestgeneSkeletalsiteBeta±SEapa
7rs13245690G*A0.37C7orf58RadaBMD−0.20±0.051.8×10−4
11rs7104230T*C0.47LIN7CFNaBMD−0.14±0.050.005
THaBMD−0.14±0.050.010
TBLHBMC−0.08±0.040.033
Chr=chromosome;SNP=single-nucleotidepolymorphism;MAF=minorallelefrequency;SE=standarderror;Rad=distalradius;aBMD=arealbonemineraldensity;FN=femoralneck;TH=totalhip;
TBLH=totalbodylesshead;BMC=bonemineralcontent.
a
Betacoefficients,SEs,andpvalueswerederivedfromlinearmixedmodelsthatwereadjustedforage(years),sex,Tannerstage,bodymassindex(Z-score),physicalactivity(hours/week),anddietary
calcium(g/day).AnadditivemodelwasusedandsothebetacoefficientsareinterpretedasthedifferenceinaBMD/BMCZ-scorepereffectallele(asindicatedby*intheminorormajorcolumn).

Table2
SNP-SexInteractionsandPediatricBoneMass(fullresultsinSupportingTable4)
ChrSNPMinorMajorMAFNearestgeneSkeletalsitepinteraction
MalesFemales
Beta±SEapaBeta±SEapa
1rs2586392C*T0.27DNM3FNaBMD0.0290.21±0.080.013−0.04±0.080.595
6rs7751941AG*0.23ESR1RadaBMD0.015−0.21±0.100.0270.10±0.080.244
7rs13245690G*A0.37C7orf58RadaBMD0.011−0.05±0.080.521−0.32±0.071.8×10−6
7rs13245690G*A0.37C7orf58SpaBMD0.0060.19±0.090.034−0.13±0.080.081
7rs13245690G*A0.37C7orf58TBLHBMC0.0090.10±0.060.105−0.11±0.050.034
7rs13245690G*A0.37C7orf58THaBMD0.0130.17±0.080.033−0.10±0.070.174
7rs10276139C*T0.16TXNDC3FNaBMD0.024−0.10±0.100.3030.18±0.080.022
7rs10276139C*T0.16TXNDC3RadaBMD0.026−0.19±0.080.0200.08±0.090.371
7rs10276139C*T0.16TXNDC3TBLHBMC0.014−0.10±0.080.1680.14±0.060.030
7rs3779381G*A0.26WNT16RadaBMD0.0260.27±0.080.0010.02±0.070.837
10rs4568902G*A0.22MPP7RadaBMD0.0040.09±0.090.300−0.24±0.080.001
11rs16921914AG*0.29DCDC5FNaBMD0.0100.18±0.080.018−0.09±0.070.214
11rs273592C*T0.33DCDC5FNaBMD0.033−0.07±0.070.3470.14±0.070.034
11rs16921914AG*0.29DCDC5THaBMD0.0050.21±0.080.010−0.11±0.080.176
11rs273592C*T0.33DCDC5THaBMD0.020−0.10±0.070.1840.13±0.070.047
11rs3781586AC*0.14LRP5TBLHBMC0.0040.22±0.064.4×10−4−0.05±0.070.471
12rs1053051TC*0.49C12orf23RadaBMD0.031−0.16±0.080.0490.07±0.070.310
12rs1053051TC*0.49C12orf23SpaBMD0.001−0.13±0.080.0880.21±0.070.002
12rs1053051TC*0.49C12orf23TBLHBMC0.001−0.07±0.060.2210.19±0.051.3×10−4
12rs7304170TC*0.18KLHDC5/PTHLHRadaBMD0.0080.29±0.090.002−0.03±0.080.668
12rs7304170TC*0.18KLHDC5/PTHLHSpaBMD0.0130.20±0.100.048−0.14±0.090.135
12rs7304170TC*0.18KLHDC5/PTHLHTBLHBMC0.0150.14±0.070.046−0.10±0.070.149
13rs9594738TC*0.47RANKL/AKAP11TBLHBMC0.017−0.15±0.060.0080.03±0.050.545
SNP=single-nucleotidepolymorphism;Chrn=chromosome;MAF=minorallelefrequency;SE=standarderror;FN=femoralneck;aBMD=arealbonemineraldensity;Rad=distalradius;Sp=spine;
TBLH=totalbodylesshead;BMC=bonemineralcontent;TH=totalhip.
a
Betacoefficients,SEs,andpvalueswerederivedfromlinearmixedmodelsthatwereadjustedforage(years),pubertalstage,bodymassindex(Z-score),physicalactivity(hours/week),anddietary
calcium(g/day).AnadditivemodelwasusedandsothebetacoefficientsareinterpretedasthedifferenceinaBMD/BMCZ-scorepereffectallele(asindicatedby*intheminorormajorcolumn).Valuesof
pinboldanditalicizedremainsignificantaftercorrectionformultipletestingusingtheBenjaminiandHochbergfalsediscoveryrateprocedure.

Table3
SNP-MaturationInteractionsandPediatricBoneMass(fullresultsinSupportingTable5)
ChrSNPMinorMajorMAFNearestgeneSkeletalsitepinteraction
TannerITannerII–IVTannerV
Beta±SEapaBeta±SEapaBeta±SEapa
1rs17130546G*A0.08WLSFNaBMD0.015−0.21±0.100.042−0.26±0.100.010−0.08±0.110.443
1rs12042083AG*0.20WNT4SpaBMD0.0220.15±0.070.0430.03±0.070.6130.07±0.080.367
2rs11898505AG*0.39SPTBN1SpaBMD0.033−0.04±0.060.501−0.06±0.050.306−0.17±0.070.017
2rs6752877G*T0.40SPTBN1SpaBMD0.0160.07±0.060.2490.08±0.050.1240.21±0.070.003
6rs3130340C*T0.21MHCRadaBMD0.0100.15±0.070.0330.03±0.070.632−0.07±0.080.371
8rs13277230TC*0.46TNFRSF11BRadaBMD0.001−0.15±0.060.015−0.14±0.050.0100.03±0.060.595
8rs6993813TC*0.48TNFRSF11BRadaBMD0.010−0.17±0.060.007−0.17±0.050.002−0.04±0.060.579
8rs6993813TC*0.48TNFRSF11BSpaBMD0.017−0.16±0.050.002−0.16±0.050.001−0.05±0.060.411
8rs13277230TC*0.46TNFRSF11BTBLHBMC0.004−0.08±0.040.028−0.05±0.040.1990.04±0.040.326
8rs6993813TC*0.48TNFRSF11BTBLHBMC0.025−0.12±0.040.002−0.06±0.040.106−0.01±0.040.853
11rs7104230T*C0.47LIN7CTHaBMD0.008−0.12±0.060.057−0.08±0.060.152−0.18±0.060.002
17rs12937692AG*0.22C17orf53/HDAC5RadaBMD0.040−0.05±0.070.494−0.14±0.060.034−0.03±0.080.736
17rs7217932AG*0.48SOX9TBLHBMC0.0360.02±0.040.6020.03±0.040.4500.10±0.050.031
19rs2287679C*T0.28GPATCH1TBLHBMC0.0410.03±0.050.5510.08±0.040.0480.14±0.050.004
SNP=single-nucleotidepolymorphism;Chr=chromosome;MAF=minorallelefrequency;SE=standarderror;FN=femoralneck;aBMD=arealbonemineraldensity;Sp=spine;Rad=distalradius;TBLH
=totalbodylesshead;BMC=bonemineralcontent;TH=totalhip.
a
Betacoefficients,SEs,andpvalueswerederivedfromlinearmixedmodelsthatwereadjustedforage(years),sex,bodymassindex(Z-score),physicalactivity(hours/week),anddietarycalcium(g/day).
AnadditivemodelwasusedandsothebetacoefficientsareinterpretedasthedifferenceinaBMD/BMCZ-scorepereffectallele(asindicatedby*intheminorormajorcolumn).Valuesofpinboldand
italicizedremainsignificantaftercorrectionformultipletestingusingtheBenjaminiandHochbergfalsediscoveryrateprocedure.

Table4
SNP-Sex-MaturationInteractionsandPediatricBoneMass(fullresultsinSupportingTable6)
ChrSNPMinorMajorMAFNearestgeneSkeletalsitepinteractionSex
2rs12185748TC*0.49GALNT3SpaBMD5.4×10−5Male0.02±0.080.792−0.11±0.080.180−0.12±0.100.206
Female−0.26±0.070.001−0.08±0.070.286−0.03(0.08)0.683
2rs12185748TC*0.49GALNT3TBLHBMC0.049Male−0.05±0.060.332−0.10±0.060.089−0.10(0.07)0.163
Female−0.14±0.060.015−0.06±0.050.257−0.02(0.06)0.762
2rs12185748TC*0.49GALNT3THaBMD0.003Male−0.11±0.080.185−0.14±0.080.077−0.13(0.09)0.127
Female−0.16±0.070.020−0.02±0.070.7130.00(0.07)0.976
2rs10205005C*T0.24PKDCCRadaBMD0.001Male0.02±0.090.832−0.04±0.090.639−0.23(0.11)0.034
Female−0.00±0.110.9650.03±0.090.7540.23(0.10)0.019
3rs430727TC*0.44CTNNB1SpaBMD0.016Male0.07±0.090.4370.20±0.080.0170.24(0.10)0.012
Female0.11±0.080.1850.04±0.070.5660.00(0.08)0.978
7rs6967282G*A0.23ABCF2TBLHBMC0.014Male−0.09±0.070.2100.01±0.070.911−0.08(0.09)0.375
Female0.13±0.070.0450.02±0.060.778−0.03(0.07)0.653
7rs10276139C*T0.16TXNDC3FNaBMD0.047Male−0.05±0.100.587−0.07±0.100.483−0.16(0.11)0.166
Female0.07±0.090.4640.18±0.090.0360.28(0.10)0.004
7rs10276139C*T0.16TXNDC3THaBMD0.021Male−0.12±0.100.227−0.09±0.110.385−0.21(0.12)0.067
Female0.09±0.090.3230.13±0.090.1420.24(0.10)0.012
8rs6993813TC*0.48TNFRSF11BSpaBMD0.050Male−0.20±0.080.009−0.13±0.070.0690.06(0.09)0.473
Female−0.14±0.080.073−0.19±0.070.005−0.14(0.08)0.079
10rs11599750TC*0.40CPN1FNaBMD0.015Male−0.19±0.080.017−0.05±0.080.5620.03(0.09)0.761
Female−0.10±0.070.174−0.13±0.080.096−0.16(0.08)0.044
10rs7898709G*T0.13MBL2FNaBMD0.040Male0.00±0.100.9710.11±0.110.327−0.02(0.12)0.899
Female−0.10±0.110.368−0.22±0.110.040−0.15(0.12)0.194
11rs3781586AC*0.14LRP5THaBMD0.043Male0.12±0.110.2620.18±0.110.0980.29(0.10)0.003
Female0.03±0.120.806−0.06±0.120.643−0.12(0.13)0.343
12rs1053051TC*0.49C12orf23FNaBMD0.002Male−0.04±0.080.5870.10±0.080.2130.06(0.08)0.505
Female0.27±0.073.0×10−50.17±0.070.0140.17(0.08)0.022
12rs1053051TC*0.49C12orf23THaBMD0.002Male−0.03±0.090.7360.09±0.080.2850.06(0.09)0.459

ChrSNPMinorMajorMAFNearestgeneSkeletalsitepinteractionSex
Female0.24±0.070.0010.15±0.070.0330.16(0.07)0.034
13rs9594738TC*0.47RANKL/AKAP11RadaBMD0.004Male0.05±0.090.550−0.11±0.080.183−0.29(0.09)0.001
Female0.07±0.090.4090.12±0.070.1010.14(0.08)0.086
14rs2010281AG*0.35MARK3RadaBMD0.015Male−0.03±0.110.7880.00±0.090.9960.28(0.11)0.009
Female−0.02±0.090.785−0.01±0.070.8860.01(0.07)0.876
14rs2273703G*C0.36MARK3RadaBMD0.015Male0.03±0.110.788−0.00±0.090.996−0.28(0.11)0.009
Female0.02±0.090.8260.01±0.070.872−0.01(0.07)0.879
14rs2010281AG*0.35MARK3THaBMD0.010Male−0.09±0.090.322−0.17±0.080.0360.02(0.09)0.799
Female−0.06±0.070.4470.01±0.070.8420.04(0.07)0.574
14rs2273703G*C0.36MARK3THaBMD0.025Male0.09±0.090.3220.17±0.080.036−0.02(0.09)0.799
Female0.03±0.070.680−0.02±0.070.820−0.05(0.07)0.526
SNP=single-nucleotidepolymorphism;Chr=chromosome;MAF=minorallelefrequency;SE=standarderror;Sp=spine;aBMD=arealbonemineraldensity;TBLH=totalbodylesshead;BMC=bone
mineralcontent;TH=totalhip;Rad=distalradius;FN=femoralneck.
a
Betacoefficients,SEs,andpvalueswerederivedfromlinearmixedmodelsthatwereadjustedforage(years),bodymassindex(Z-score),physicalactivity(hours/week),anddietarycalcium(g/day).An
additivemodelwasusedandsothebetacoefficientsareinterpretedasthedifferenceinaBMD/BMCZ-scorepereffectallele(asindicatedby*intheminorormajorcolumn).Valuesofpinboldand
italicizedremainsignificantaftercorrectionformultipletestingusingtheBenjaminiandHochbergfalsediscoveryrateprocedure.

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