BMC Medical Genetics

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Genome-wide association with bone mass and geometry in the
Framingham Heart Study
Douglas P Kiel*1, Serkalem Demissie2, Josée Dupuis2, Kathryn L Lunetta2,
Joanne M Murabito3,4 and David Karasik1
Address: 1Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA, USA, 2Department of Biostatistics, Boston
University School of Public Health, Boston, MA, USA, 3Section of General Internal Medicine, Boston University School of Medicine, Boston, MA,
USA and 4The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
Email: Douglas P Kiel* - kiel@hrca.harvard.edu; Serkalem Demissie - demissie@bu.edu; Josée Dupuis - dupuis@bu.edu;
Kathryn L Lunetta - klunetta@bu.edu; Joanne M Murabito - murabito@bu.edu; David Karasik - karasik@hrca.harvard.edu* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S14

doi:10.1186/1471-2350-8-S1-S14

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S14© 2007 Kiel et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Osteoporosis is characterized by low bone mass and compromised bone structure, heritable traits that
contribute to fracture risk. There have been no genome-wide association and linkage studies for these traits using highdensity genotyping platforms.
Methods: We used the Affymetrix 100K SNP GeneChip marker set in the Framingham Heart Study (FHS) to examine
genetic associations with ten primary quantitative traits: bone mineral density (BMD), calcaneal ultrasound, andgeometric indices of the hip. To test associations with multivariable-adjusted residual trait values, we used additive
generalized estimating equation (GEE) and family-based association tests (FBAT) models within each sex as well as sexes
combined. We evaluated 70,987 autosomal SNPs with genotypic call rates ≥80%, HWE p ≥ 0.001, and MAF ≥10% in up
to 1141 phenotyped individuals (495 men and 646 women, mean age 62.5 yrs). Variance component linkage analysis was
performed using 11,200 markers.
Results: Heritability estimates for all bone phenotypes were 30–66%. LOD scores ≥3.0 were found on chromosomes
15 (1.5 LOD confidence interval: 51,336,679–58,934,236 bp) and 22 (35,890,398–48,603,847 bp) for femoral shaft
section modulus. The ten primary phenotypes had 12 associations with 100K SNPs in GEE models at p < 0.000001 and2 associations in FBAT models at p < 0.000001. The 25 most significant p-values for GEE and FBAT were all less than 3.5
× 10-6 and 2.5 × 10-5, respectively. Of the 40 top SNPs with the greatest numbers of significantly associated BMD traits(including femoral neck, trochanter, and lumbar spine), one half to two-thirds were in or near genes that have notpreviously been studied for osteoporosis. Notably, pleiotropic associations between BMD and bone geometric traits
were uncommon. Evidence for association (FBAT or GEE p < 0.05) was observed for several SNPs in candidate genes
for osteoporosis, such as rs1801133 in MTHFR; rs1884052 and rs3778099 in ESR1; rs4988300 in LRP5; rs2189480 in VDR;
rs2075555 in COLIA1; rs10519297 and rs2008691 in CYP19, as well as SNPs in PPARG (rs10510418 and rs2938392) and
ANKH (rs2454873 and rs379016). All GEE, FBAT and linkage results are provided as an open-access results resource at
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: The FHS 100K SNP project offers an unbiased genome-wide strategy to identify new candidate loci and to
replicate previously suggested candidate genes for osteoporosis.

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Introduction
Osteoporosis is a skeletal disorder characterized by compromised bone strength leading to an increased risk offracture [1]. In the United States alone, there are over 1.5
million fractures each year, including 280,000 hip fractures and 500,000 vertebral fractures. According to therecent U.S. Surgeon General's Report on Skeletal Health,fractures remain a large and growing public health concern [2]. Presently, the gold standard for assessment of
fracture risk is measurement of bone mineral density(BMD, g/cm2) by dual-energy X-ray absorptiometry(DXA). Whereas low BMD is among the strongest risk factors for fracture [3,4], a number of clinical studies havedemonstrated that other measurements, such as quantitative ultrasound (QUS) and bone geometry, are importantfor fracture prediction and osteoporosis treatment monitoring [5-7]. Thus, QUS of the calcaneus is associated withhip fracture, largely independent of BMD [8,9]. A growing
body of evidence in recent years indicates that femoral
geometry also contributes importantly to hip fracture risk
[10,11].
BMD, QUS, and femoral bone geometry are approximately normally distributed, complex traits. A wealth ofstudies have documented BMD to be under strong genetic
control with 50–70% heritability [12,13]. Similarly, QUS[8,14,15] and hip geometry [16-19] are probably regulated by additive genetic factors. However, despite years of
research in the field of osteoporosis genetics, progress todate has been modest in successfully identifying major
genes determining BMD, QUS, and bone geometry in thegeneral population. The numbers of quantitative trait loci
(QTLs) and genes linked and/or associated with osteoporosis-related phenotypes continue to expand and thelist has become considerably more detailed and complex.
More than 20 genome linkage scans to date have revealed
multiple QTLs covering all chromosomes but the Y chromosome [20]. Moreover, the results from one study haveinconsistently been replicated in other samples. To overcome these obstacles, a collaborative meta-analysis of 9
genome-wide linkage searches of BMD was recently conducted, including data from 11,842 subjects, members of
3,045 families [13]. The meta-analysis suggested anumber of specific QTLs to be pursued further (1p13.3q23.3 and 1q32-q42.3, 3p25.3-p22.1, 11p12-q13.3,
12q24.31-qter, and 18p11-q12.3). An additional factor
that adds to the complexity of finding genes for osteoporosis-related traits is the notion (including our own[21,22]) that QTLs for bone density and geometry [23] areskeletal site-specific, age-group-, and sex-specific [24,25].
Numerous biological candidate genes for bone phenotypes have also been proposed, but few have been validated with large-scale evidence [13]. Recent meta-analyses
for estrogen receptor alpha [26], collagen type I alpha 1

http://www.biomedcentral.com/1471-2350/8/S1/S14[27], methylenetetrahydrofolate reductase, [28] and vitamin D receptor [29] genes demonstrated that the polymorphisms in these genes each explain a small percentageof the variation in BMD or fracture. With the near completion of the International HapMap Project and the rapid
improvements in high throughput genotyping technology, the ultimate understanding of the genetic basis ofosteoporosis may come from a genome-wide association
approach in which the whole human genome is surveyed
for common genetic variation in osteoporosis-related heritable quantitative traits such as BMD [19]. To further
advance the field of skeletal genetics, efforts are now turning toward studies that are able to cover the vast majorityof the genome with a dense set of SNPs, in order to identify the genes linked and/or associated with osteoporosisrelated traits. We thus performed genome-wide linkage
and association analyses using a dense genome scan inmembers of extended pedigrees from the Framingham
Heart Study (FHS).

Materials and methods
Study sample
The Framingham Osteoporosis Study is an ancillary studyof the Framingham Heart Study (see description of the
FHS sample in the Overview paper [30]). Beginning at
biennial examination 20 of the Original Cohort, bonemineral density (BMD) was obtained using single anddual photon absorptiometry. The Original Cohort participants underwent bone densitometry by DXA with a
Lunar DPX-L (Lunar Corp., Madison, WI, USA) during
their examination 22 (1992–1993). In order to maximize
the sample size, we used DXA scans from examination 24(in 1996–1997) for 31 Original Cohort members who did
not have DXAs at examination 22. The Offspring Cohort
was scanned with the same machine at their examination
cycle 6 or 7 (between 1996 and 2001). Right calcaneal
bone ultrasonography (QUS) was performed with a portable QUS device, the Sahara® bone sonometer (Hologic,
Inc., Waltham, MA), in members of the Offspring Cohort
at their examination cycle 6/7 and the Original Cohort
during their exam 24 assessment. There were 1141 phenotyped individuals, 245 from the Original Cohort (86 men
and 159 women, mean age 77.5 yrs) and 896 Offspring
Cohort participants (409 men and 487 women, mean age
58.5 yrs), belonging to 241 families.
Phenotype definition and residual creation
Original Cohort participants had bone measures performed at either exam 22 or 24. Offspring Cohort participants had these measures obtained during a period that
included part of the official Framingham exams 6 and 7.
The following traits were studied: Bone mineral density(including femoral neck, FNBMD; trochanter, TRBMD;
L2–L4 lumbar spine, LSBMD); quantitative ultrasound of
the calcaneus (broadband ultrasound attenuation, BUA;

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speed of sound, SOS), and geometric indices of the hipfrom DXA (femoral neck-shaft angle, NSA; femoral necklength, NeckLeng; section modulus, Z, and width, W, at
the "narrow neck", Neck, and the shaft, S, regions of thehip). In total, 10 traits are reported here. Details of themeasurements and phenotype definition [15,23,31] aswell as coefficients of variation for the different component variables were previously reported for BUA (5.3%),
SOS (0.4%) [32], LSBMD (0.9%), FNBMD (1.7%),
TRBMD (2.5%) [33], and for the geometric traits, ranged
from 3.3% (NeckW) to 9.1% (NeckLeng) [34].
Multivariate regression analysis was performed in each sex(men and women) and Cohort (Original and Offspring)in order to obtain normalized or ranked residual phenotypes, adjusted for covariates. Table 1 lists the covariates
used for each trait. The covariate measurements have been
described in previous work [22]. We used ranked residuals
in order to correct for the deviations from the normal kurtosis and skewness in some of the phenotypes, since variance component analysis (VCA) is sensitive to the highkurtosis [35].
Genotyping methods
We used the Affymetrix 100K SNP GeneChip marker set in
the Framingham Heart Study to examine genetic associations with the above phenotypes (see description of the
FHS 100K and Marshfield STRs in the Overview paper[30]). We evaluated autosomal SNPs with genotypic callrates ≥80%, HWE p ≥ 0.001, MAF ≥10% in up to 1141
phenotyped individuals (495 men and 646 women). Ultimately, 70,987 SNPs were analyzed.
Statistical analysis methods
We performed genome-wide association (GWA) analyses
by two approaches, using both family-based and population-based methods. (See description of the general statistical methods for GWA and linkage analyses in the
Overview paper [30]). Both sex-specific and combinedsexes analyses were performed. We used additive generalized estimating equation (GEE) and family-based association tests (FBAT) models to test associations of ourphenotypes. Family based association testing has a great
power to detect genetic variants of modest effect size
[30,36].

In order to prioritize SNPs potentially associated withmultiple phenotypes, we evaluated several phenotypic
subgroups: (1) clinically important bone mineral density
traits (FNBMD, TRBMD, LSBMD), (2) a combination of
BMDs with BUA, (3) hip geometry (NSA, NeckLeng,
NeckW and ShaftW), and (4) hip BMDs with hip geometry. We also focused on BMD in sex-specific subgroups.
For each SNP we calculated the number of traits significantly associated with this SNP at alpha <0.01 in both

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GEE and FBAT, assuring that the FBAT and GEE effects
were in the same direction. We then selected top SNPs
with highest numbers of nominally significantly associated traits. For identical proportions of nominally significant traits, SNPs were additionally ranked by the mean of
GEE p-values across traits (as well, GEE p-values were used
for SNP ranking in the sex-specific subgroups due to asmall sample size for FBAT analysis).
For SNPs that were selected by more than one strategy andthat pertained to a specific gene (according to the NCBI
Build 35), we perused Entrez Gene and also queried
PubMed using the gene name and "osteoporosis", "fracture" or "bone mass" terms, in order to identify biologicalplausibility or evidence relating these genes specifically tobone disease.
Variance component analyses (VCA) for all phenotypes
were performed on normalized or rank-normalized residuals using the computer package Sequential Oligogenic
Linkage Analysis Routines (SOLAR, SFBR/NIH, San Antonio, TX) version 2.0, available online http://
www.sfbr.org/solar/doc/00.contents.html.
Heritability(h2) of each phenotype was estimated as the proportion of
the total phenotypic variance attributable to the additiveeffects of genes. Subsequent linkage analyses were also
conducted using SOLAR. This method, described in detailelsewhere [37] (see description of the linkage methods in
the Overview paper [30]), entails specification of thegenetic covariance between arbitrary relatives as a function of the identity-by-descent (IBD) relationships at agiven marker locus and models the covariance matrix fora pedigree as the sum of the additive genetic covarianceattributable to the QTL, the additive genetic covariancedue to the effects of loci other than the QTL, and the variance due to unmeasured environmental factors. VCA linkage used IBDs values that were calculated from 11,200
markers.

Results
Bone-related quantitative traits and derived phenotypes
analyzed in the FHS 100K SNP resource are listed in Table
1. Each trait was analyzed as multivariable-adjusted residuals from cohort- and sex-specific models; in addition,geometric traits were adjusted for age, age2 only. Amongthese, the following ten multivariable-adjusted residuals
are the primary phenotypes presented here: BMD of thefemoral neck (FNBMD; n with data = 1141), trochanter(TRBMD; n = 1141) and the second to fourth vertebral
bodies of the lumbar spine (LSBMD; n = 1127); a quantitative ultrasound measure of the calcaneus, broadband
ultrasound attenuation (BUA; n = 1105); femoral geometry measures at the "narrow neck" region of the hip andthe femoral shaft including narrow neck section modulus(NeckZr; n = 1107) and width (NeckWr; n = 1106), fem-

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Table 1: Phenotypes included in the study
Trait

Abbreviation

Bone mineral density (BMD)
Femoral BMD**

Trochanter BMD**

Lumbar spine BMD**

Quantitative ultrasound (QUS)
Broadband ultrasound attenuation**
Speed of Sound
Hip geometry
Neck-shaft angle**

Femoral neck length**

Neck width**

Neck section modulus**

Neck Cross-Sectional Moment of Inertia

Neck Average Buckling Ratio

Shaft width**

Shaft Section modulus**

Shaft Cross-Sectional Moment of Inertia

Shaft Average Buckling Ratio

Inter-Trochanteric Average Buckling Ratio

Gender (N)Cohort, exam cycle

Adjustment*

FNBMD

Combined (1141)

Original, ex. 22–24, Offspring,ex. 6–7

1) multivariable-adjusted residuals (age, age2, height,
BMI, smoking, physical activity, estrogen therapy)

FNBMDfFNBMDmTRBMD
TRBMDf
TRBMDmLSBMD
LSBMDfLSBMDmFemales (646)Males (495)Same as above

Same as above

Same as above

Combined (1117)
Females (641)Males (486)Same as above

Same as above

BUA

Combined (1105)

SOS

Combined (1104)

Original, ex. 24, Offspring, ex.
6–7
Same as above

1) multivariable-adjusted residuals (age, age2, height,
BMI, smoking, physical activity, estrogen therapy)
Same as above

NSA1

Combined (1096)

Original, ex. 22–24, Offspring,ex. 6–7

1) age, age22) multivariable-adjusted ranked residuals (age, age2,
height, BMI)

NSAf
NSAm
NeckLeng1
NeckLengf
NeckLengm
NeckW1r
NeckW1rf
NeckW1rm
NeckZ1r
NeckZ1rf
NeckZ1rm
NeckCSMI1r
NeckCSMI1rf
NeckCSMI1rm
NeckAvgBR1r
NeckAvgBR1rf
NeckAvgBR1rm
ShaftW1
ShaftW1f
ShaftW1m
ShaftZ1r
ShaftZ1rf
ShaftZ1rm
ShaftCSMIr
ShaftCSMIrf
ShaftCSMIrm
ShaftAvgBR1r
ShaftAvgBR1rf
ShaftAvgBR1rm
ITAvgBR1r
ITAvgBR1rf
ITAvgBR1rm

Females (622)Males (474)
Combined (1090)
Females (619)Males (471)
Combined (1095)
Females (618)Males (477)
Combined (1096)
Females (618)Males (478)
Combined (1094)
Females (618)Males (476)
Combined (1096)
Females (618)Males (478)
Combined (1028)
Females (599)
Males (429)
Combined (1028)
Females (599)
Males (429)
Combined (1026)
Females (599)
Males (427)
Combined (1028)
Females (599)
Males (429)
Combined (1024)
Females (596)Males (428)Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above

Same as above*adjustment performed in each Cohort (generation) and gender**primary traits reported here

oral shaft section modulus (ShaftZr; n = 1037) and width(ShaftW; n = 1037), femoral neck-shaft angle (NSA; n =1107), and femoral neck length (NeckLeng; n = 1101).
Additional bone-related quantitative phenotypes areavailable in the FHS 100K SNP resource (web posted at
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007) including calcaneal speed ofsound; several hip geometry measures including neckcross-sectional moment of inertia; neck, shaft and inter-trochanteric buckling ratio, and shaft cross sectional
moment of inertia from these same examinations.
Heritability estimates for all bone phenotypes ranged
from 30–66%. We consider here only multivariableadjusted, residual phenotypes derived from ten primary
traits defined above. There were 12 associations with
100K SNPs in GEE models at p < 0.000001 and 2 associations in FBAT models with p < 0.000001. The 25 SNPs

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with the lowest p-values in GEE (p < 3.5 × 10-6) and FBAT
models (p < 2.5 × 10-5), ordered by chromosomal position, are shown in Tables 2a and 2b, respectively. Of these
SNPs, 10 (GEE) and 19 (FBAT) were within 60 kb of aknown gene. Several SNPs associated with phenotypes
were in high pairwise LD (r2 ≥ 0.8). When this occurred we
displayed results for the higher ranking SNP based on a
combination of FBAT and GEE p-values. Of note, some
SNPs were associated with more than one trait: thus,
rs10514345 on chromosome 5 was associated (GEE) with
NeckZ1 (combined sample) and LSBMD (females) andrs1209921 on chromosome 21 was associated (FBAT)with TRBMD (combined sample) and FNBMD (females)(Tables 2a and 2b, respectively).
In Table 2c we present results with LOD scores above 2.0
from genome-wide linkage analyses with their corresponding 1.5-LOD confidence interval. There were linkage peaks observed for the majority of chromosomes, andin general there were a greater number of linkages withbone geometry traits than with either BMD or ultrasoundmeasures. LOD scores ≥3.0 were found on chromosomes
15 (1.5 LOD support interval, 51,336,679–58,934,236
bp) and 22 (35,890,398–48,603,847 bp), with femoral
shaft section modulus. There were no linkage regions ofoverlap with markers that were found to be associatedwith bone phenotypes in the FBAT and GEE analyses.
Notably, when we restricted our sample to members of
the Offspring Cohort, LOD scores for BMD phenotypes
increased; for example, for LSBMD on chr. 7 (63,211,216bp) from 1.25 to 2.43; for FNBMD on chr. 9 (104,660,212bp) from 1.79 to 2.65, and on chr. 17 (63,038,023 bp)
from 1.83 to 2.43 (not shown in the Table 2c).
In order to prioritize SNPs potentially associated withmultiple phenotypes, we evaluated four phenotypic subgroups by both population- and family-based tests. The
first subgroup was BMD (femoral neck, trochanter andspine), the second was BMD + bone ultrasound (BUA),
the third was bone geometry (femoral neck-shaft angle,
femoral neck length, femoral neck width, and shaft
width), and the fourth, hip BMDs with hip geometry. For
each SNP we calculated the number of traits in the subgroup that were significantly associated with the SNP at p
< 0.01 in both GEE and FBAT, and then ranked SNPs withhighest numbers of nominally significantly associatedtraits in which the direction of association was the same.
In cases where SNPs had the same proportions of nominally significant traits, SNPs were additionally ranked by
the mean of GEE p-values across traits.
In Table 3a we present only the SNPs selected based on
associations with p-value of both GEE and FBAT < 0.01
with the first subgroup (BMD phenotypes only), for a
combined sample of men and women. Using the annota-

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tion by UCSC genome browser tables http://
genome.ucsc.edu/, of the top 20 SNPs for the BMD subgroup, 9 (45%) were identified in genes or within 60 kb
of a known gene, and none were coding (Table 3a). The
nominally significant intragenic SNPs were found in several genes, including cadherin 9 type 2 (CDH9) on 5p14
and gene deleted in colorectal carcinoma (DCC) on
18q21. It is also noteworthy that the association of
TRBMD with rs1209926 on chr. 21 (39,078,994 bp)
occurred in the region identified in our original genomewide linkage study in the FHS [22] SNP rs2938392 in theperoxisome proliferator-activated receptor gamma(PPARG) was also associated with the BMD traits in GEE
tests and less strongly in FBAT analyses (not shown in
Table 3a).
We also examined men and women separately within thefirst phenotypic subgroup and confirmed the associationsof BMD phenotypes, observed in the combined sample,
with rs922028 (chromosome 4, no known gene),rs1479679 (in CDH9, chr. 5), and rs768207 (in DCC, chr.
18), all in women (Table 3b). There were also associations
in one sex that were neither observed in the other sex nor
in the combined sample, and men and women did notshare many polymorphisms associated with BMD.
We also looked for evidence of pleiotropy by evaluating
association results within the other three pre-specified
phenotype subgroups, BMD + BUA and hip geometry, as
well as combinations of hip BMD with hip geometry(fourth subgroup). Notably, pleiotropic associations
between BMD and other traits were uncommon (notshown in a table). When considering BMD and BUA
together (phenotype subgroup 2), there were nominallysignificant associations (p < 0.0005) with a SNP rs953934
in the interleukin 1 receptor-like 1 gene (IL1RL1) on 2q12
(not shown). Of the top SNPs for the Hip Geometry subgroup (NSA, NeckLeng, NeckW1r and ShaftW1), 50%
were identified in genes or within 60 kb of a known gene;none were coding. The nominally significant intragenic
SNPs were found in several genes, including rs7151976 inheat shock 70 kD protein 2 (HSPA2) on 14q24 andrs2834719 in runt-related transcription factor 1 (RUNX1,
or CBFA2) on 21q22. Significant associations of SNPs
with BMD phenotypes did not overlap with geometricphenotypes, and within the hip geometry subgroup, SNPs
significant for one geometric phenotype did not overlap
with other phenotypes within this subgroup (data notshown).
We further queried the FHS 100K array results, to verify, insilico, whether previously reported candidate genes forosteoporosis were associated with BMD, QUS and bonegeometry in our sample. Thus Table 4 shows SNPs in thesepre-defined candidate genes that were associated at p ≤Page 5 of 13(page number not for citation purposes)

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Table 2: Top genetic associations with bone density and geometry based on the lowest p-value for GEE test (2a), FBAT (2b), andLinkage (2c) analyses

2a: Top 25 associations with bone density and geometry based on the lowest p-value of the GEE test
Phenotype

SNP

Chrom.

Physical Position

GEE p-value

FBAT p-value

Gene region*

NeckZ1rf

rs6600671

1

120,812,532

5.85 × 10-7

3.18 × 10-3

NeckZ1

rs2053506

3

19,350,795

3.74 × 10-6

2.68 × 10-2

KCNH829,828,407

2.83 × 10-6

2.91 × 10-2

RBMS3

2.36 × 10-3

ERC2

TRBMDmrs10510628

3

NeckW1rf

rs2054989

356,505,749

6.06 × 10-7

ShaftW1f

rs922948

3

69,525,327

1.75 × 10-6

3.47 × 10-3

ShaftW1

rs9912583

103,256,733

5.32 × 10-7

3.12 × 10-2

BUA

rs9291683

4

10,000,429

2.07 × 10-6

7.44 × 10-4

NeckZ1rf

rs2548003

528,783,080

2.40 × 10-7

1.30 × 10-1
5.97 × 10-3

ZNF366
GPR98

NeckZ1rf

rs10515148

571,752,684

6.10 × 10-7

LSBMDfrs10514345

590,460,035

2.15 × 10-6

9.85 × 10-2

NeckZ1Same as above

1.81 × 10-7

3.03 × 10-2

NeckW1rf

rs4715166

6

13,324,037

2.69 × 10-6

9.97 × 10-3

PHACTR1

BUA

rs2214681

7147,140,340

2.72 × 10-6

2.26 × 10-2

CNTNAP2

ShaftZ1R

rs10503887

8

31,752,989

2.23 × 10-7

1.77 × 10-3

NRG1

6.71 × 10-2

FNBMDmrs2165468

10

3,506,105

1.07 × 10-6

ShaftW1f

rs1452928

11

84,499,969

7.10 × 10-7

7.43 × 10-3

NeckZ1rf

rs63888211

115,269,255

3.50 × 10-6

6.33 × 10-3
2.17 × 10-3

ShaftZ1rf

rs10492096

126,450,843

3.35 × 10-6

TRBMD

rs10506701

12

72,872,477

1.40 × 10-6

1.25 × 10-1

ShaftW1f

rs10506821

12

78,999,391

2.03 × 10-7

1.80 × 10-1
2.21 × 10-2

NeckZ1rm

rs1590305

13

36,933,189

3.15 × 10-6

FNBMDmrs9317284

13

62,532,351

2.44 × 10-7

2.86 × 10-1
6.18 × 10-3

VAMP1

TRBMDfrs4087296

16

80,935,282

3.11 × 10-7

NSAm

rs4131805

18

24,313,110

2.07 × 10-6

1.87 × 10-3

TRBMDfrs4811196

20

35,903,108

1.13 × 10-6

7.03 × 10-4

CTNNBL1

GEE p-value

FBAT p-value

Gene region

162,045,085

2.34 × 10-3

1.22 × 10-6

LMX1A

2.80 × 10-6

DPT

2b: Top 25 associations with bone density and geometry based on the lowest p value of the FBAT test

Phenotype

NeckW1rf

SNP

rs1395548

Chrom.

1

Physical Position

FNBMD

rs1538173

1

165,373,720

6.77 × 10-2

TRBMD

rs3762397

1

196,821,876

5.96 × 10-3

2.78 × 10-7

NR5A2

ShaftZ1rf

rs9287234

1

236,892,414

9.71\ × 10-3

7.90 × 10-6

FMN2

8.79 × 10-6

TRBMD

rs914951

1

239,278,119

1.82 × 10-2

NeckW1

rs4143244

35,385,364

1.07 × 10-2

7.05 × 10-7

NeckW1rf

rs1349205

3

54,093,157

2.22 × 10-2

1.42 × 10-5

CACNA2D3

1.32 × 10-5

IFT57/HHLA2

2.00 × 10-5

FSTL1

ShaftW1

rs1870007

3

109,458,022

1.71 × 10-1

FNBMDfrs950649

3

121,567,065

7.63 × 10-4

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Table 2: Top genetic associations with bone density and geometry based on the lowest p-value for GEE test (2a), FBAT (2b), andLinkage (2c) analyses (Continued)
NSA1

rs432446

4

127,906,277

2.84 × 10-2

5.19 × 10-6
1.53 × 10-5

FNBMD

rs922028

4

181,162,903

9.55 × 10-3

FNBMD

rs1823926

5

119,882,620

2.62 × 10-1

1.27 × 10-5

PRR16

NSA1

rs10503733

823,589,963

4.29 × 10-4

1.79 × 10-5

NKX3-11.32 × 10-5

NSAm

rs10503953

8

33,917,325

1.86 × 10-3

NSAf

rs4876377

8

118,556,170

4.56 × 10-2

1.93 × 10-5

THRAP6

NSAf

rs10505600

8

133,882,045

2.48 × 10-1

1.36 × 10-5

PHF20L1

8.87 × 10-6

FREM1

NSA1

rs1552896

914,831,387

4.02 × 10-1

NeckZ1rm

rs7857590

918,979,893

1.36 × 10-1

8.08 × 10-6

C9orf138

BUA

rs700760

9

86,164,178

3.82 × 10-2

1.42 × 10-5

ZCCHC6

1.21 × 10-5

FNBMDf

rs2378731

987,175,111

5.53 × 10-4

LSBMDmrs10512315

9103,334,877

1.70 × 10-1

2.28 × 10-5

NeckW1rf

rs2048741

9117,716,816

5.49 × 10-3

2.40 × 10-5
1.05 × 10-5

C9orf126

ShaftZ1rm

rs6744

9

124,988,623

9.58 × 10-2

NeckLengf

rs773985

10

34,822,758

3.18 × 10-2

1.05 × 10-5

PARD3

124,204,438

1.11 × 10-1

1.80 × 10-5

PLEKHA1/HTRA1

4.35 × 10-6

FGF14

NeckLengf

rs10490924

10

FNBMDmrs10508076

13

101,270,253

5.41 × 10-3

TRBMD

rs1865968

16

57,535,751

1.15 × 10-1

1.72 × 10-5

TRBMD

rs1209921

21

39,078,145

2.51 × 10-4

2.41 × 10-5

5.32 × 10-5

6.65 × 10-3

FNBMDfSame as above

C21orf24/ETS2

2c. Magnitude and Location of Peak LOD scores >2.5 for regions in the bone density and geometry phenotype group

Phenotypemarker

Chrom.

physical location

maxLOD

1.5 LOD confidence
interval

NeckLeng1

rs1759687

1

112,663,861

2.80

102,547,964–
119,889,867

ShaftW1

rs2320625

2

95,284,610

2.85

85,198,107–
105,339,005

NSA1

rs1304771

2

123,061,290

2.80

108,949,962–
129,987,436

BUA

rs842288

3

16,291,319

2.08

8,499,321–40,075,261

NeckZ1

rs1386389

4

112,310,874

2.33

102,722,423–
131,391,829

NeckW1

rs2094541

10

130,314,425

2.56

128,465,158–
135,165,518

LSBMD

rs10492028

12

111,799,883

2.10

103,438,302–
117,318,152

ShaftZ1

rs10518923

15

55,573,538

3.02

51,336,679–58,934,236

NeckW1

AFM049xd2

16

22,945,152

2.34

16,856,153–24,665,428

BUA

rs10492922

16

27,519,162

2.20

26,434,120–50,596,704

NeckZ1

rs6502901

17

5,864,832

2.37

451,209–8,829,083

BUA

rs8081154

17

46,196,038

2.12

37,926,265–62,642,504

NeckW1

rs750160

20

649,6312.10

161,523–4,335,440

ShaftZ1

rs7290139

22

44,039,280

3.01

35,890,398–48,603,847* SNP in a gene(s) or near (within 60 kb)

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Table 3: Combination of FBAT and GEE, for grouped BMD phenotypes*

3a: Combination of FBAT and GEE, for grouped BMD phenotypes in men and women combined
Chrom

1
1
2
2
2
4
4
4
5
5
7
7
7
8
11
12
16
18
19
21

Physical Position

SNP

Min p-value by
GEE

Min p-value by
FBAT% Significant P-values by
GEE & FBAT**

Function

Gene symbol

177,489,285
231,181,475
6,719,916
15,963,228
79,078,098
177,742,191
181,139,118
181,162,903
26,917,842
53,527,854
9,739,152
11,301,740
18,183,021
114,092,330
95,219,586
103,526,872
76,268,225
48,132,968
42,607,072
39,078,994

rs7544774
rs7554650
rs7584788
rs2380707
rs1261226
rs9312601
rs10520437
rs922028
rs1479679
rs16882423
rs1557978
rs10486135
rs10486301
rs1156075
rs546809
rs10507180
rs8051539
rs768207
rs1465434
rs1209926

3.34 × 10-4
1.67 × 10-4
7.55 × 10-3
1.14 × 10-3
1.19 × 10-5
2.22 × 10-3
5.23 × 10-4
8.45 × 10-4
1.13 × 10-4
1.65 × 10-3
1.73 × 10-4
1.67 × 10-4
1.94 × 10-4
5.55 × 10-3
1.41 × 10-3
7.18 × 10-4
2.08 × 10-3
1.94 × 10-3
1.51 × 10-3
2.51 × 10-4

7.55 × 10-3
5.93 × 10-3
5.19 × 10-4
5.22 × 10-4
4.47 × 10-3
7.03 × 10-4
7.72 × 10-5
1.53 × 10-5
2.38 × 10-4
2.38 × 10-3
3.97 × 10-4
5.95 × 10-4
9.43 × 10-4
8.51 × 10-3
7.10 × 10-5
4.16 × 10-3
5.80 × 10-3
2.99 × 10-3
7.17 × 10-4
3.21 × 10-5

67
67
67
67
67
67
100
100
67
67
33
67
67
67
67
100
67
67
67
33

intronunknown
unknown
unknown
unknown
unknown
unknown
unknown
intronintronintronintronunknown
intronintronintronunknown
intronintronunknown

XPR1

DCC
ZNF569C21orf24/ETS2

Gene symbol

CDH9
ARL15

CSMD3
MTMR2
CHST113b: Sex-specific results for grouped BMD phenotypes†
Chrom

1
1
2
4
5
6
6
7
7
9
9
11
12
13
13
15
17
17
18
20

Physical Position

SNP

Sex

Min p-value by
GEE‡

Min p-value by FBAT‡

Function

48,261,693
200,641,877
125,809,045
181,162,903
26,917,842
47,657,085
83,666,784
33,440,554
35,084,096
20,592,188
88,679,511
96,720,041
20,059,354
52,997,850
107,573,163
55,974,260
11,054,857
53,546,007
48,132,968
18,310,297

rs560004
rs1935588
rs1215318
rs922028
rs1479679
rs9296566
rs9294266
rs10486541
rs10486661
rs7867710
rs1779308
rs10501872
rs10505843
rs9316628
rs1223978
rs10518945
rs2024134
rs181224
rs768207
rs2328293

Males
Females
Males
Females
Females
Females
Males
Males
Females
Females
Males
Females
Females
Females
Males
Females
Males
Males
Females
Females

8.91 × 10-4
6.14 × 10-4
1.23 × 10-4
1.61 × 10-4
4.36 × 10-5
2.27 × 10-4
5.32 × 10-4
1.54 × 10-5
9.34 × 10-6
6.96 × 10-4
3.09 × 10-3
4.03 × 10-4
2.02 × 10-4
2.51 × 10-3
3.42 × 10-4
6.19 × 10-5
4.75 × 10-3
2.15 × 10-5
1.35 × 10-4
7.74 × 10-4

3.48 × 10-3
7.62 × 10-4
2.96 × 10-4
6.16 × 10-3
6.96 × 10-3
5.08 × 10-4
2.01 × 10-3
1.95 × 10-3
9.98 × 10-5
2.97 × 10-3
4.94 × 10-3
1.02 × 10-3
7.38 × 10-3
5.22 × 10-4
5.07 × 10-4
6.15 × 10-4
6.31 × 10-5
2.90 × 10-3
2.57 × 10-4
4.35 × 10-3

unknown
unknown
unknown
unknown
intronintronintronunknown
unknown
intronunknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
intron
locus

CDH9
CD2AP
C6orf157
TBX20
MLLT3

ALDH1A2
Dlc2/OR4D1/OR4D2
DCC
C20orf12* Femoral neck, Trochanter, Lumbar spine BMD** Percentage of traits nominally significantly associated with the SNP at p < 0.01 in both GEE and FBAT
†Subsamples with N < 400 excluded.
‡Percentage of traits nominally significantly associated at p < 0.01 in both GEE and FBAT was 67% for all SNPs.

0.05 in either GEE or FBAT models. We selected the following six previously reported genes that had been extensively studied: COL1A1, CYP19, ESR1, LRP5, MTHFR, and
VDR. For the MTHFR gene, we found 1 non-synonymous
coding SNP (rs1801133) that was associated with femoral
shaft section modulus and neck-shaft angle by GEE and
FBAT, respectively. We [38] have previously reported an
association of this SNP with BMD in unrelated participants in the Framingham Osteoporosis Study and others

have confirmed this association [28,39]. None of thethree 100K SNPs in the ESR1 gene (rs1884052, rs3778099
and rs3866461) with nominally significant associations
with bone phenotypes were in LD with widely published
SNPs (rs2234693 and rs9340799). A single intronic 100K
SNP from LRP5 (rs4988300) was associated with shaft
section modulus in males and with femoral neck BMD infemales. This 100K SNP was not in LD with publishedrs3736228 in LRP5 [40]. There were no LD data available

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Table 4: Associations of bone traits with selected candidate genes for osteoporosis with a FBAT or GEE p-value < 0.05
Chrom

Candidate
Gene

Published
SNP

Ref.

100K SNP

r2*

PhysicalPosition

Lowest Pvalue by GEE

GEE
Phenotype

Lowest P-value
by FBAT1

MTHFR

rs1801133

rs1801133

self

11,790,644

0.033

ShaftZ1rm

0.042

NSA1

6

ESR1

rs2234693

[38]
[39]
[28]
[45]

rs3866461
rs1884052
rs3778099

0.223
0.004
0.002

152,238,835
152,383,480
152,510,689

0.014
0.002
0.003

NSAm
ShaftZ1R
BUA

0.003
0.023
0.019

NeckLengf
NeckW1rf
FNBMD

11

LRP5

0.038

ShaftZ1rm

0.025

FNBMDf

VDR

46,550,095

0.011

NeckZ1rm

0.021

LSBMD

15

CYP19

no datano data
0.030
0.049
no data
0.045
no datano data
0.902
0.239
0.58
0.82
no datano datano datano data

67,845,407

12

rs4988300
rs4988300
rs4988300
rs2189480
rs2189480
rs2189480
rs2189480
rs2189480
rs10519297
rs2008691
rs10519297
rs10519297
rs10519297
rs2075555
rs2075555
rs2075555

49,328,952
49,335,602

0.002
0.003

NSA1
NSA1

0.859
0.012

NSA1
NSA1

45,629,290

0.034

NeckW1rf

0.010

ShaftWm

17

COL1A1

rs9340799
rs4988321
rs627174
rs3736228
rs11568820
rs10735810
rs1544410
rs7975232
rs731236
rs700518
rs4775936
rs10046
rs11575899
rs1107946
rs11327935
rs1800012[45][40, 46][40, 41]
[29]
[29]
[29]
[29]
[29]
[47]
[48]
[49]

[50]
[51]
[52]

FBAT
Phenotype* r2 between the 100K SNP and a corresponding published SNP (column to the left)
p-values < 0.005 are shown in bold.

on the HapMap website for the SNPs widely studied byothers with respect to BMD (rs4988321 and rs627174).
For the VDR gene there was a single 100K SNP(rs2189480) nominally significantly associated with femoral neck section modulus and spine BMD; however,there were no data available on LD between this 100K
SNP and the 3 out of 5 SNPs reported in a recent metaanalysis of association studies for osteoporosis [29]. Two
SNPs reported in that meta-analysis (rs1544410 andrs11568820) were not in LD with the one 100K SNP studied here. There were two 100K SNPs in the CYP19 gene(rs10519297 and rs2008691) associated with femoral
neck-shaft angle that were in LD with at least one of thepreviously reported SNPs studied with respect to bonephenotypes (rs700518, rs4775936, rs10046, andrs11575899). Also, there was a single 100K SNP in the
COL1A1 gene (rs2075555) that was associated with femoral neck width in women and shaft width in men; however, there were no data at the HapMap website regarding
LD with the three other SNPs in this gene reported byother osteoporosis research groups (rs1107946,
rs11327935, rs1800012).

Discussion
To our knowledge, this is the first genome-wide association
study of many of the most commonly measured osteoporosisrelated phenotypes. This unbiased approach allowed simultaneous exploration of multiple genetic loci rather than focusing ona single-gene association. Using a strategy that combined
GEE and FBAT association tests within the subgroup of

BMD traits, 40 SNPs were each found to be associatedwith several bone phenotypes. More than half of thesewere in or near several genes, including cadherin 9 type 2(CDH9) on 5p14, nuclear receptor subfamily 5, group A
gene (NR5A2) at 1q32, gene deleted in colorectal carcinoma (DCC) on 18q21, and PPARG on 3p25. Some of thegenes found to be associated with one or several bonedensity phenotypes have not previously been studied forosteoporosis. Analyses stratified by sex confirmed association of BMD phenotypes with CDH9 and DCC inwomen. The sex specificity of our findings is fully in linewith the previous observations of both sex-specific linkage[13,21] and sex-specific genetic association with BMD[25,41].
Our phenotypic subgroup combining BMD and the ultrasound trait, BUA, demonstrated that the interleukin 1
receptor-like 1 (IL1RL1) gene on 2q12 was associated withall bone mass traits. In contrast there was a notableabsence of pleiotropic associations between BMD and hip
geometry (femoral neck length and width, neck-shaft
angle, and shaft width). This is an important observationin light of the current debates about the role of femoral
bone geometry in fracture prediction above and beyond
BMD, as well as a criticism of DXA-derived bone geometric traits, which are perceived as partially captured in the
BMD measurement. We have clearly shown that BMD
traits (measured at the femoral neck, trochanter, and at
the lumbar spine) share associations with SNPs in several
genes, whereas there are no such SNPs that share associa-

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tions with both hip BMD and hip geometric traits. Despite
substantial shared genetic determinants between hip
BMD and geometric traits (preliminary unpublishedresults from our group), these phenotypes are at least inpart governed by distinct gene variants. Since genes contributing to variation in BMD do not always contribute toosteoporotic fractures [42], there is a necessity to genetically dissect geometric phenotypes along with BMD toderive a valid endophenotype of osteoporosis and to better encircle the complex heritability of this disease [31].
When we queried the FHS 100K association results, several SNPs in previously well-studied candidate genes forosteoporosis were identified (FBAT or GEE p < 0.05), suchas rs1801133 in MTHFR; rs1884052 and rs3778099 in
ESR1; rs4988300 in LRP5; rs2189480 in VDR; rs2075555
in COLIA1; rs10519297 and rs2008691 in CYP19. We
also examined SNPs in other candidate genes of interest to
our group such as SNPs in PPARG (rs10510418 andrs2938392) that were associated with BMD and ultrasound and SNPs in ANKH (rs2454873 and rs379016) thatwere associated with femoral neck section modulus.
We observed limited overlap between the results of theassociation and linkage analyses, which may, in part, be
attributed to a low power of the linkage analysis. The resolution of linkage peaks depends not only upon theextracted information content of the genotyped markers,
but also upon the useful recombination information contained in the sample (which is related to family structureand sample size). Despite low power for detecting modest
genetic effects in linkage analyses, we identified several
loci with a substantial indication of linkage, such as LOD
scores ≥3.0 on chromosomes 15 (around 55,573,538 bp)
and 22 (around 44,039,280 bp) for bone geometry phenotypes. Also, LOD scores for BMD phenotypes generally
increased after restricting our sample to the members of
the Offspring Cohort only, despite potential loss of power
due to smaller sample size. This observation indirectly
supports our previous finding of refined LOD scores insubgroups of age [21] and may be explained by theyounger age of the Offspring Cohort members. The BMD
phenotypes in that cohort might be less affected by agingrelated artifacts in DXA scans, such as osteophytes, ossification of ligaments, and abdominal aortic calcification.
We also observed little overlap between the results of thelinkage analysis reported above and our previous studiesof the same phenotypes [23,31], which may, in part, be
attributed to low power of these linkage analyses and limited overlap between the 100K sample and the relatively
large sample of FHS pedigrees published before [23,31].
To our knowledge, this is a first genome-wide association
study of many of the most commonly measured osteoporosis-related phenotypes. It is distinct in many

http://www.biomedcentral.com/1471-2350/8/S1/S14

respects. First, this is a population-based sample rather
than a study of affected families. Second, our study
included adults from two generations of the same families. The longitudinal nature of the FHS and ongoingrecruitment of the third generation (grandchildren of theOriginal Cohort) will enable us to validate the results ofthis study with an even larger sample of 3 generations.
Third, results of this collaborative effort (FHS 100K SNP
study) are publicly available on the web http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007, which makes it a unique
resource contributing to ongoing efforts of other groups.
One of the strengths of this study lies in the unbiased
approach that allowed simultaneous exploration of multiple genetic loci rather than focusing on a single-geneassociation. However osteoporosis is likely to be a polygenic condition with a strong environmental component.
Therefore, an obvious limitation of this study is theabsence of analyses testing gene-environment and geneby-gene interactions [43], which is a demanding elementof study design and statistical analyses. We did not formally test for associations of the SNPs with some known
environmental factors, such as smoking or estrogen
replacement therapy in women, for which FHS has welldocumented longitudinal data, because of sample size
considerations.
A second potential limitation of this study is that there
may be unmeasured factors that would affect the association or impact on generalizability of our results. Samplingof homogenous individuals is needed to minimize theseeffects. We attempted to approach this problem in part bystratifying our sample by gender; however, a more
involved stratification, such as younger men or postmenopausal women, was not performed due to sample size
restrictions. A third potential shortcoming of this study is
the limited coverage of the genome by the 100K SNP
array. These SNPs are undersampled from coding regions(both non-synonymous and synonymous) and oversampled from regions outside genes, relative to SNPs in theoverall HapMap database [44]. Current plans of the FHS
include the genotyping and analysis of over half a million
genetic variants in approximately 9,000 men and womenfrom three generations of the FHS. This higher density
genotyping will provide wider coverage of genomic variation and thus will clearly have advantages for GWA andlinkage studies in the future.
Fourth, at the present there is no clear-cut strategy for prioritization of the regions identified by our approach towhole genome analyses. We implemented one possible
SNP prioritization strategy based on a somewhat arbitrarygrouping of multiple phenotypes. Pleiotropy across different (but probably related) phenotypes may be an indica-

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BMC Medical Genetics 2007, 8(Suppl 1):S14

tion of genes that influence more than one biologicalpathway and thus may affect overall risk of osteoporosis.
Finally, we are hesitant to regard any result reported inthis paper as genome-wide significant. Although observing evidence for association at a significance level of .0001
in both the GEE and FBAT test is very encouraging, it does
not meet the standard of genome-wide significance. Not
only do we have a large number of statistical tests for eachphenotype, but we also have numerous phenotypes [30].
None of our findings should be considered true positive
results until they are replicated in independent samples
and/or functional studies are performed that provide themechanism of the SNP effect.

Conclusions and future directions
An unbiased genome-wide strategy to detect genetic associations with skeletal traits provides an opportunity toidentify novel pathways for replication in other humanpopulations. This study is thus hypothesis-generating andawaits replication in comparable samples. On one hand,
ample data on genetic markers and associated traits may
form the basis of new phenotype definitions, aimed atdefining phenotypic groupings that are characterized by
the sharing of genetic linkage and association. Such data
may uncover biological mechanisms not obvious at thephenotypic level [42]. On the other hand, a composite
genetic risk score that combines the effects of multiple locimay prove to be of more practical utility for a common
and complex chronic disease such as osteoporosis, [43]rather than results of single-gene association studies. Identification of such a molecular profile and creation of anarray for genetic screening will be a very useful tool for
prevention and management of osteoporosis.

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DPK drafted the manuscript and revised it critically forimportant intellectual content. All authors read andapproved the final manuscript.

Acknowledgements
From the Framingham Heart Study of the National Heart Lung and Blood
Institute of the National Institutes of Health and Boston University Schoolof Medicine. The Framingham Heart Study core examinations were supported by the National Heart, Lung and Blood Institute's (Contract No.
N01-HC-25195). Measurements of phenotypes were funded by the
National Institute of Arthritis, Musculoskeletal and Skin Diseases and the
National Institute on Aging (grants No. R01 AR/AG 41398 and R01
AR050066).
FHS 100K analyses were conducted using the Boston University. Linux
Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR (National
Center for Research Resources) Shared Instrumentation grant
1S10RR163736-01A1 http://www.bu.edu/dbin/sph/departments/biostatis
tics/linga_publications.php.
We gratefully acknowledge the Framingham Study members who participated in this study, as well as the study coordinators, who contributed tothe success of this work, as well as Yekyung (Kelly) Cho for her technicalassistance, and Dr. L. Adrienne Cupples for her contributions to the designof the study and the statistical analysis.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

References
1.

2.

Abbreviations

3.

BMD = bone mineral density; Chr = chromosome; DXA =dual-energy X-ray absorptiometry; FBAT = family based
association testing; GEE = generalized estimating equations; GWA = Genome wide association; HWE = HardyWeinberg equilibrium; IBD = identity-by-descent; LD =linkage disequilibrium; LOD = logarithm of the odds;
MAF = Minor allele frequency; QUS = bone quantitative
ultrasound; SNP = single nucleotide polymorphism; VCA= variance component analysis.

4.

Competing interests

7.

5.

6.

The authors declare that they have no competing interests.

Authors' contributions
All authors have made substantial contributions to conception and design or acquisition of phenotypic data. JD
participated in the design of the study and performed statistical analyses; DPK, DEK, SD, JMM, and KLL contributed to the analysis and interpretation of data. DK and

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