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Am J Hum Genet. May 2004; 74(5): 866–875.
Published online Apr 7, 2004.
PMCID: PMC1181981

Polymorphisms in the Low-Density Lipoprotein Receptor–Related Protein 5 (LRP5) Gene Are Associated with Variation in Vertebral Bone Mass, Vertebral Bone Size, and Stature in Whites


Stature, bone size, and bone mass are interrelated traits with high heritability, but the major genes that govern these phenotypes remain unknown. Independent genomewide quantitative-trait locus studies have suggested a locus for bone-mineral density and stature at chromosome 11q12-13, a region harboring the low-density lipoprotein receptor–related protein 5 (LRP5) gene. Mutations in the LRP5 gene were recently implicated in osteoporosis-pseudoglioma and “high-bone-mass” syndromes. To test whether polymorphisms in the LRP5 gene contribute to bone-mass determination in the general population, we studied a cross-sectional cohort of 889 healthy whites of both sexes. Significant associations were found for a missense substitution in exon 9 (c.2047G→A) with lumbar spine (LS)–bone-mineral content (BMC) (P=.0032), with bone area (P=.0014), and with stature (P=.0062). The associations were observed mainly in adult men, in whom LRP5 polymorphisms accounted for [less-than-or-eq, slant]15% of the traits’ variances. Results of haplotype analysis of five single-nucleotide polymorphisms in the LRP5 region suggest that additional genetic variation within the locus might also contribute to bone-mass and size determination. To confirm our results, we investigated whether LRP5 haplotypes were associated with 1-year gain in vertebral bone mass and size in 386 prepubertal children. Significant associations were observed for changes in BMC (P=.0348) and bone area (P=.0286) in males but not females, independently supporting our observations of a mostly male-specific effect, as seen in the adults. Together, these results suggest that LRP5 variants significantly contribute to LS–bone-mass and size determination in men by influencing vertebral bone growth during childhood.


The lifetime risk of suffering an osteoporosis-related fracture is >40% in women and >13% in men (Kanis 2002). This risk is largely influenced by the bone-mineral density and size achieved in young adults (Duan et al. 2001a, 2001b). Twin and parent-offspring studies have indicated that additive genetic effects account for 60%–80% of the population variance in areal bone-mineral density (aBMD), bone area, bone-mineral content (BMC), and stature (Ferrari et al. 1998b; Eisman 1999). As with other complex phenotypes, interindividual variation in these traits seems to be determined at multiple loci (Perola et al. 2001; Peacock et al. 2002). Many association studies with osteoporosis candidate genes have been performed to date, but, so far, only small and often inconsistent differences in aBMD between genotypes have been reported (Ferrari et al. 1999b; Peacock et al. 2002 [for review]). Therefore, allelic variants of genes with a major contribution to bone mass and size remain to be identified.

The low-density lipoprotein (LDL) receptor–related protein 5 (LRP5) gene, which, in humans, maps to chromosome 11q12-13, encodes a transmembrane protein of 1,615 amino acids that is a member of the LDL receptor–related family (Hey et al. 1998). These proteins mediate WNT signaling through the β-catenin pathway and are involved in Drosophila and mouse development (Wodarz and Nusse 1998; Tamai et al. 2000).

Recently, several lines of evidence have pointed to the LRP5 gene as a candidate susceptibility factor for osteoporosis in the general population. (1) Loss-of-function mutations in LRP5 are responsible for osteoporosis pseudoglioma (OPPG [MIM 259770]), a rare autosomal recessive disorder characterized by low bone mass, spontaneous fractures, and blindness (Gong et al. 2001), whereas LRP5 gain-of-function mutations cause high-bone-mass syndromes (HBM [MIM 601884]) (Boyden et al. 2002; Little et al. 2002; Van Wesenbeeck et al. 2003). (2) Mice with targeted disruption of Lrp5 have a deficit in bone formation and sustain spontaneous fractures (Kato et al. 2002). (3) A QTL for bone-mineral density in the general population was mapped to 11q12-13, the same chromosomal region where LRP5 is located (Koller et al. 1998; Carn et al. 2002; Livshits et al. 2002). Moreover, a QTL for stature has also been identified in this region (Hirschhorn et al. 2001; Perola et al. 2001), suggesting that the two traits may be influenced by allelic variation of the same gene.

To investigate whether allelic variants of the LRP5 gene contribute to bone-mass and stature determination in the general population (Patel and Karsenty 2002), we first studied the frequency and linkage disequilibrium (LD) of 13 previously reported SNPs in a subset of our sample. We then selected those polymorphisms that provided most of the genetic information to perform an association study in our entire cohort, comprising healthy adults, children, and adolescents of both sexes, for which detailed bone-mass and size measurements at the lumbar spine (LS) had been collected. To confirm and further dissect our results, we subsequently performed a 1-year longitudinal association study in a subgroup of 386 prepubertal children to assess the contribution of LRP5 genotypes to vertebral bone growth.

Subjects and Methods


For the association study, 889 healthy children, adolescents, and adults of European descent were recruited from among volunteers drawn from the population living in Geneva, Switzerland. This cohort comprised 149 and 240 prepubertal girls and boys, respectively (Tanner’s pubertal stage 1), of whom 148 girls and 238 boys were re-evaluated after 1 year for the longitudinal study (Bonjour et al. 1997, 2001); 74 female and 68 male adolescents in Tanner’s pubertal stages 2–5 (Bonjour et al. 1991; Theintz et al. 1992), of whom 15 female and 5 male adolescents had reached peak bone mass and were thereafter classed as adults; 100 young men, students at Geneva University (Ferrari et al. 1999a); and 186 pre- and perimenopausal women and 72 men who were parents of some of the children and adolescents included in the study cohort (Ferrari et al. 1998b, 1998c). Most of these subjects have participated in a number of previous studies of bone mass at our institution, and their detailed inclusion/exclusion criteria have been reported elsewhere (see references above).

Clinical Measurements

The aBMD (g/cm2), BMC (g), and projected bone area (cm2) were measured at the LS (L2-L4 vertebrae) in anteroposterior view by dual X-ray absorptiometry (DXA) with Hologic QDR-1000, -2000, and -4500 instruments, as reported elsewhere (see references above). The coefficient of variation at L2-L4 was 1% for all measurements. Stature was evaluated by measuring standing height (without shoes) with a stapediometer (Holtain). Calcium intake was evaluated using validated food-frequency questionnaires completed under supervision of a trained dietician (Bonjour et al. 1997; Ferrari et al. 1998c, 1999a). The study was approved by the ethics committee of the University Hospitals of Geneva, and informed consent for genotyping of osteoporosis candidate genes was obtained from all participants and/or their parents.

SNP Genotyping

Genomic DNA was extracted from blood lymphocytes or mouth epithelial cells by use of the QIAmp DNA blood kit (Qiagen). SNPs were genotyped by the pyrosequencing method (Pyrosequencing), in which short regions including the SNP are automatically sequenced through a series of four enzymatic steps (Ronaghi 2001). We designed specific PCR primers for each SNP so that regions of ~200 bp spanning the polymorphism were amplified (dbSNP Database).

Data Analysis

Hardy-Weinberg equilibrium was tested for each SNP by use of the HW Exact Test, as implemented in the Genepop software (Garnier-Gere and Dillmann 1992). To assign inferred haplotypes to each individual, we used the HAPLOTYPER program and default parameters (Niu et al. 2002). Haplotype frequencies were confirmed by the Arlequin program (Excoffier and Slatkin 1995). LD for all SNP-pair combinations was calculated using the DnaSP software (Rozas and Rozas 1995). For LD measurement, we used the R2 statistic. To determine whether there was underlying population structure in our cohort that could potentially generate false-positive associations, we ran the program STRUCTURE (Pritchard et al. 2000), using genotype data of five markers located in different genomic regions (LRP5, IL-6, VDR, LEPR, and LEP).

For association analyses, stature, aBMD, BMC, and projected bone area were adjusted for age and sex and were expressed as standardized Z scores, by use of the study population mean and SD for age and sex as reference (at 1-year intervals for children and at 5-year intervals for adults). Z-score differences between LRP5 polymorphisms were tested by analysis of covariance (ANOVA), with weight (Z scores) as a continuous variable and with genotypes and sex as categorical variables. Sex was included as a covariate to account for potential interactions between sex and genotypes, since both genomewide screening in mice and association studies in humans indicate that some genes may govern bone mass in a sex-specific manner (Orwoll et al. 2001; Ferrari et al. 2004). To avoid biases, only unrelated individuals were analyzed at one time; that is, adults and children were analyzed separately. To correct for multiple comparisons, we used a modified Bonferroni method, as described by Sankoh et al. (1997), which takes into consideration the level of correlation between linked markers. For the haplotype associations, the classical Bonferroni correction was applied (hence P values [less-than-or-eq, slant].0125 were considered significant at α=0.05).

In prepubertal children, association of LRP5 polymorphisms with longitudinal changes in bone-mass measurements, calculated as the difference between values obtained at baseline and after 1 year, were tested by two-factor ANOVA, with genotypes and sex as independent variables. Subsequently, differences between LRP5 genotypes (and haplotypes) were tested by ANOVA within sexes. The percentage of the population variance for each trait explained by LRP5 genetic variation was estimated as the R2 value from multiple regression analysis. This included five LRP5 SNPs transformed into dummy variables (two homozygous for the rare allele, one heterozygous, and zero homozygous for the common allele) as independent and residuals of bone measurements—adjusted by age, sex, and weight—as dependent variables.


Allele Frequencies and Haplotype Structure

We initially studied 13 SNPs, 10 of which were reported elsewhere (Okubo et al. 2002; Van Wesenbeeck et al. 2003) (table A [online only]), that were located in the coding region or intron/exon junctions of the LRP5 gene. As a first step, SNPs were genotyped for a sample of 88 unrelated individuals randomly selected from among 889 subjects in our cohort. Eight of the 13 SNPs were found to have a minor-allele frequency of [gt-or-equal, slanted]2% in our population (fig. 1), and the genotype frequencies of these 8 SNPs did not significantly deviate from Hardy-Weinberg equilibrium. On the basis of these polymorphisms, we inferred that 11 different haplotypes were present in our study population, using a likelihood method based on a Baysesian algorithm (Niu et al. 2002). The most common haplotype had a frequency of 50%, and four haplotypes accounted for 85% of the total sample (table B [online only]). Significant LD was observed across the gene (fig. 1), although LD was not correlated with intermarker distance (R2=0.11; P=.588).

Figure  1
Schematic diagram of SNP localization in LRP5. Vertical bars represent the 23 exons of LRP5, and arrows indicate the positions of the eight validated SNPs with a minimum allele frequency of 2%. SNPs encoding missense substitutions are boxed. Leading asterisks ...
Table A
Table B
Frequency of LRP5 Haplotypes for All Available SNPs

To reduce the number of tests for association, we discarded SNPs with a minor-allele frequency <5% and selected only one SNP for each pair of markers in nearly complete LD (R2[gt-or-equal, slanted]0.9). Five SNPs, which we call the “informative SNPs,” fulfilled these criteria; two of them alter the amino acid sequence of the protein: missense substitutions exon 9, c.2047G→A (p.V667M), and exon 18, c.4037C→T (p.A1330V) (fig. 1).

To rule out potential population stratification in our study cohort, we ran the program STRUCTURE (Pritchard et al. 2000), using data from markers on five different independent loci that had been genotyped in 221 individuals from our sample (Ferrari et al. 1998a, 2003; Quinton et al. 2001; Nieters et al. 2002). The highest likelihood was obtained under the assumption of one population (K=1), and, for higher values of K, none of the individuals was assigned to a particular population, thereby indicating that an unknown population substructure was unlikely to be present in our cohort.

Cross-Sectional, Population-Based Association Study in Adults

Among 889 subjects who were genotyped for these five SNPs, 877 with complete data sets could eventually be analyzed. To account for the age distribution of the cohort, subjects were subdivided into “adults,” whom we assumed had reached peak bone mass and size—that is, the maximal amount of bone accumulated by the end of the pubertal growth period (in this case, by the age of 17.0 years for females and 18.0 years for males) (Rizzoli and Bonjour 1999)—and growing “children/adolescents” (table 1). Association analyses were performed using bone measurements and stature adjusted for age and sex (Z scores) within these groups. Because of the small number of genotype AA at c.1980 and c.2047 and genotype TT at c.4037 (n=3, 4, and 13, respectively), these subjects were grouped with heterozygotes for each specific SNP.

Table 1
Characteristics of Subjects in Cross-Sectional Study

In adults, the most significant and consistent associations with all LS bone measurements, as well as with stature, occurred with the missense SNP in exon 9, c.2047G→A (p.V667M) (table 2). After adjustment for multiple comparisons, associations remained significant for LS BMC and area and for stature (P values [less-than-or-eq, slant].006 are significant at α=0.05 after correction, given that 20 tests were performed with an average marker R2=0.3). Associations with vertebral BMC and bone area were driven mainly by men, in whom average differences between carriers and noncarriers of the exon 9 c.2047A rare allele were [less-than-or-eq, slant]0.67 Z scores (fig. 2). In contrast, differences in stature between exon 9 c.2047G→A genotypes were similar for both sexes (fig. 2), corresponding to an average 2.0 cm lower adult height in carriers of the rare A allele. To our knowledge, this finding constitutes the first genetic variant to be linked to stature in humans, although it explained <5% of the population-based variance for the trait.

Figure  2
Differences in adult LS bone measurements and stature, according to LRP5 exon 9 missense SNP and sex. Stature and aBMD, BMC, and bone area at the LS were measured cross-sectionally in 364 adult subjects of both sexes. Results are mean Z scores ± ...
Table 2
P Values for Association of LRP5 Polymorphisms with Vertebral Bone Mass and Stature in Adults[Note]

Although no significant associations were observed with the other four “informative SNPs” (table 2), single SNPs may fail to capture all of the contribution of a locus to a particular trait. We therefore tested association, using inferred haplotypes assigned to each individual. Reconstruction of haplotypes with the five informative SNPs resulted in a number of haplotypes with low probability of correct assignment. Hence, we built haplotypes using four informative SNPs (c.1980G→A, c.2047G→A, c.3405A→G, and c.4037C→T), which yielded all haplotypes with >.98 probability of correct assignment (table 3).

Table 3
Haplotypes of “Informative SNPs” in the Cross-Sectional Cohort

The results of the haplotype association revealed significant Z-score differences in LS BMC and area among adults of different haplotype groups, which remained statistically significant after correction for multiple comparisons (table 2; fig. 3A). Stature, on the other hand, showed no significant differences with the haplotype analysis, suggesting that only the c.2047 variant is likely to play a role in the determination of this trait.

Figure  3
Differences in adult LS measurements, according to LRP5 haplotypes. aBMD, BMC, and bone area at the LS were measured cross-sectionally in men and women and were expressed as standardized Z scores (±SE). Haplotypes were based on SNPs c.1980, c.2047, ...

For all three LS bone parameters (aBMD, BMC, and area) there was a marked trend for lower Z scores in individuals carrying haplotype 4. Since haplotype 4 is the sole haplotype that carries the c.2047A allele, which was strongly associated with lower bone mass and size when analyzed independently, this finding is consistent with the genotype data. In addition, there was a trend for higher Z scores in individuals with haplotype combinations 3,1; 3,2; and 3,3. However, since these groups are scarcely populated (n[less-than-or-eq, slant]5 subjects/group), and the 0,3 group (n=39) has Z scores close to zero, this analysis did not support a contribution of haplotype 3 to vertebral bone mass and size phenotypes.

To refine the haplotype analysis, we (1) performed a sex-specific comparison, since the genotype data had shown a mostly male-driven association, and (2) reduced the number of classes by considering only SNPs at positions c.2047 and c.4037, the two missense polymorphisms, which are sufficient to define the most interesting haplotypes. This resulted in only three haplotypes: haplotype 0′—which combines haplotypes 0, 1, and 2 (c.2047G-c.4037C)—and haplotypes 3 and 4, which are the same as those defined in table 3.

The results of this analysis (fig. 3B) confirm a strong sex-specific effect (as with the genotype data), since, whereas in the women, there are no significant differences between the haplotypic groups, in men, significant differences are present ([less-than-or-eq, slant]0.8 Z scores), mainly driven by haplotype 4. The increased Z scores observed for individuals carrying haplotype 3 suggest that this haplotype could also contribute to the vertebral bone traits in men and deserves further investigation.

Altogether, LRP5 polymorphisms accounted for 4.0% and 3.8% of the adult population variance in LS BMC and area (independent of age and weight), respectively. It is remarkable that, in men, the contribution of LRP5 alleles reached 15.4% and 12.7% for BMC and area, respectively.

Cross-Sectional and Longitudinal Association Study in Childhood

Similar association studies with genotypes and haplotypes were performed independently in the children and adolescent group of our cohort, but only marginal associations with aBMD and BMC were observed for the exon 9 c.2047 SNP (data not shown), and no associations were observed with the haplotypes (table C [online only]).

Table C
Baseline Measurements According to LRP5 Haplotypes in Children[Note]

These observations led us to hypothesize that LRP5 variants might influence vertebral bone size and mass gain during growth, in which case, association of LRP5 alleles with the cross-sectional bone measurements would not be fully expressed in children. To test this hypothesis, we studied the association of LRP5 variants with longitudinal changes in LS parameters in 386 children from the original cohort who were remeasured after 1 year (148 girls and 238 boys). Since the polymorphism at position c.2047 and the two-SNP haplotypes (c.2047 and c.4037) were the most informative in the adults, we focused on those markers for the longitudinal analysis. As shown in figure 4A, the GA/AA genotypes were indeed associated with smaller gain in bone area in the boys. This was not observed in the growing girls (see below), consistent with the adult data, in which most of the association with vertebral Z scores was driven by men (fig. 2).

Figure  4
Changes in LS bone size and mass during childhood, according to LRP5 exon 9 c.2047 genotypes and haplotypes. Mean changes (±SEM) in LS BMC and projected area were evaluated between baseline and 1 year in prepubertal males (M) and females (F). ...

We then looked at haplotype association with changes in vertebral bone mass and size, using the reduced number of haplotypes already defined in the adults, namely 0′, 3, and 4. Age, weight, and calcium intake at baseline and during follow-up, all factors known to influence bone-mass gain in childhood, were similar among haplotypic groups (table C [online only]).

Significant differences between the haplotype groups were observed only in the growing boys (fig. 4B), with carriers of haplotype 4 having lower BMC and area gains, particularly when compared with carriers of haplotype 3, who showed a slight trend for higher gains. The pattern in the growing boys resembled that of the adults (fig. 3B), suggesting a consistent sex-related effect of LRP5 to LS bone-mass and size acquisition.

In the girls, a nonsignificant trend for higher gains was observed for carriers of the c.2047A variant and haplotype 4, opposite to what was seen in the boys. But since this effect was not present in the adults, the biological relevance of this observation is uncertain.


We performed a cross-sectional association study to analyze the contribution of LRP5 polymorphisms to variation in LS bone mass and size in a large sample of white Europeans. To this end, we studied the LD structure of the gene and selected five SNPs that provide most of the genetic information for this locus, which we genotyped in 364 healthy adults and 513 children and adolescents. We identified a missense substitution in exon 9 of the gene (c.2047G→A) that, in adults, is significantly associated with LS bone mass (BMC), projected bone area, and stature and is marginally associated with LS aBMD. Our findings are therefore consistent with previous reports of QTLs for bone-mineral density and stature in humans, both mapping to 11q12-13 (Koller et al. 1998; Hirschhorn et al. 2001; Perola et al. 2001; Carn et al. 2002; Livshits et al. 2002), the genomic region where LRP5 is located. Together with variants in the vitamin D–receptor gene (van der Sluis et al. 2003), LRP5 genetic variation is one of the first identified common genetic determinants of vertebral bone size and stature in whites.

To better describe the contribution of the locus to the determination of LS bone phenotypes, we studied the LRP5 genetic variants in the context of their haplotypes. Recent publications involving large cohorts, multiple SNPs, and haplotype analysis, such as for TNFRSF1B and ESR1 (Albagha et al. 2002; van Meurs et al. 2003), have indeed shown robust associations between haplotypes and bone phenotypes.

The haplotype associations revealed large differences in bone parameters between the different groups, which were as much as 2 SDs in some cases (fig. 3A). The main genetic determinant behind the genotype and haplotype associations was the c.2047G→A variant that defines haplotype 4. Nevertheless, the increase in Z scores observed for carriers of haplotype 3 in the males (fig. 3B) suggests that additional polymorphic sequences within this locus might contribute to the vertebral bone traits.

The associations observed in the adult group were clearly driven by the men, as seen in figures figures22 and and3B3B, suggesting that some sex-specific factors, such as gonadal steroids (which are known to play a major role in bone homeostasis [Rizzoli and Bonjour 1997; Khosla et al. 2001]) or sequences in the X or Y chromosomes, directly or indirectly affect the action of LRP5 on bone phenotypes. Similar findings have recently been reported for haplotypes in the ESR1 gene, which were found to be associated with vertebral bone area and fracture risk in females but not in males (van Meurs et al. 2003). These data support the notion that loci conferring risk for spine osteoporosis frequently do so in a sex-specific manner.

To confirm and further dissect the associations observed in the adults, we performed similar cross-sectional studies in the children, but no significant differences were observed at that stage of development. This might be explained by the fact that LRP5 gene variants influence the bone phenotypes during growth; hence, children have not had time to express these differences. To directly test this hypothesis, we performed a 1-year longitudinal study of 386 children from the original cohort.

We observed significant differences in bone-mass and size gain in growing boys that were consistent with the pattern of Z-score distribution observed in the men, in that boys carrying haplotype 4 had significantly smaller gains when compared with the other groups, in particular with those individuals carrying haplotype 3 (fig. 4B). As with the adults, the significant P value was mainly driven by haplotype 4; however, the fact that haplotype 3 carriers also showed a mild effect is interesting. In contrast, growing girls carrying haplotype 4 appeared to have a marginally higher bone-mass acquisition compared with the other groups. Since these differences did not translate into significant Z-score differences in women, they are unlikely to be biologically relevant.

It is also interesting to compare haplotype-specific gain in bone mass across sexes, since, although the overall amount of gain (for both phenotypes) is roughly the same in both sexes, its distribution among haplotype groups is very different. Thus, there would seem to be clear interactions between haplotypes 3 and 4 and sex (P interaction = .0008 for BMC and .026 for area).

Overall, the results of the longitudinal study independently confirmed the association of haplotype 4 to lower Z scores (for BMC and area) in the men and suggest that the main effect of LRP5 on bone-phenotype variation is during growth, when it affects bone mass at the spine, mainly through the determination of vertebral-bone size (Seeman 2002).

An important aspect of our study that deserves further investigation concerns the functional role of the c.2407G→A SNP that was found to be associated with LS bone traits. Although it results in an amino acid substitution (p.V667M), it is not clear whether and how it affects the function or expression of the LRP5 protein. The localization of the p.V667M substitution at the top of the third propeller module in the receptor extracellular domain, similar to most missense mutations causing the OPPG and HBM syndromes, is intriguing (Gong et al. 2001; Boyden et al. 2002; Little et al. 2002). However, in vitro assays to functionally test the consequences of this polymorphism are necessary to understand whether this SNP is causative or is in LD with some other genetic variation, outside the coding sequence, that might have a regulatory effect (Twells et al. 2003). In addition, further characterization of haplotype 3, either by functional tests or by additional population- or family-based studies, should help clarify that haplotype's potential functional involvement.

In summary, our findings indicate that LRP5 allelic variation contributes significantly to the determination of vertebral bone mass and size, mostly in white males. Taken together with the accumulating strong evidence of a major role of LRP5 in bone metabolism, LRP5 variants appear as potentially important genetic susceptibility factors for osteoporosis and vertebral fractures, particularly in men.


We thank Dr. Matthew L. Warman, at Case Western Reserve University, for providing SNP location and sequence; Dr. Daniel Slosman and collaborators, at the Division of Nuclear Medicine, Geneva University Hospital, for DXA measurements; Dr. Kristina Allen, at Genome Therapeutics Corp., and Dr. Paul Yaworski, at Wyeth Research, both in Cambridge, MA, for their critical insights into this work. This study was supported by a research and development grant from the University Hospitals of Geneva (S.L.F.) and by grants from the Swiss National Science Foundation (J.P.B. and R.R.) and the National Center for Competence in Research Frontiers in Genetics (S.E.A.).

Electronic-Database Information

Accession numbers and URLs for data presented herein are as follows:

dbSNP Database, http://www.ncbi.nlm.nih.gov/SNP/(for SNPs IVS4-4T→C [accession number rs314776], exon 9 c.1980 G→A [accession number rs2277268], exon 9 c.2047 G→A [accession number rs4988321], exon 10 c.2268 C→T [accession number rs2306862], IVS10 +6T→C [accession number rs4988322], exon 15 c.3405 A→G [accession number rs556442], exon 18 c.4037 C→T [accession number rs3736228], and exon 19 c.4137 C→T [accession number rs3736229])
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/(for OPPG and HBM)


Albagha OM, Tasker PN, McGuigan FE, Reid DM, Ralston SH (2002) Linkage disequilibrium between polymorphisms in the human TNFRSF1Bgene and their association with bone mass in perimenopausal women. Hum Mol Genet 11:2289–2295. [PubMed] [Cross Ref]10.1093/hmg/11.19.2289
Bonjour JP, Carrie AL, Ferrari S, Clavien H, Slosman D, Theintz G, Rizzoli R (1997) Calcium-enriched foods and bone mass growth in prepubertal girls: a randomized, double-blind, placebo-controlled trial. J Clin Invest 99:1287–1294. [PMC free article] [PubMed]
Bonjour JP, Chevalley T, Ammann P, Slosman D, Rizzoli R (2001) Gain in bone mineral mass in prepubertal girls 3.5 years after discontinuation of calcium supplementation: a follow-up study. Lancet 358:1208–1212. [PubMed] [Cross Ref]10.1016/S0140-6736(01)06342-5
Bonjour JP, Theintz G, Buchs B, Slosman D, Rizzoli R (1991) Critical years and stages of puberty for spinal and femoral bone mass accumulation during adolescence. J Clin Endocrinol Metab 73:555–563. [PubMed]
Boyden LM, Mao J, Belsky J, Mitzner L, Farhi A, Mitnick MA, Wu D, Insogna K, Lifton RP (2002) High bone density due to a mutation in LDL-receptor-related protein 5. N Engl J Med 346:1513–1521. [PubMed] [Cross Ref]10.1056/NEJMoa013444
Carn G, Koller DL, Peacock M, Hui SL, Evans WE, Conneally PM, Johnston CC Jr, Foroud T, Econs MJ (2002) Sibling pair linkage and association studies between peak bone mineral density and the gene locus for the osteoclast-specific subunit (OC116) of the vacuolar proton pump on chromosome 11p12-13. J Clin Endocrinol Metab 87:3819–3824. [PubMed] [Cross Ref]10.1210/jc.87.8.3819
Duan Y, Seeman E, Turner CH (2001a) The biomechanical basis of vertebral body fragility in men and women. J Bone Miner Res 16:2276–2283. [PubMed]
Duan Y, Turner CH, Kim BT, Seeman E (2001b) Sexual dimorphism in vertebral fragility is more the result of gender differences in age-related bone gain than bone loss. J Bone Miner Res 16:2267–2275. [PubMed]
Eisman JA (1999) Genetics of osteoporosis. Endocr Rev 20:788–804. [PubMed] [Cross Ref]10.1210/er.20.6.788
Excoffier L, Slatkin M (1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol 12:921–927. [PubMed]
Ferrari S, Karasik D, Liu J, Karamohamed S, Herbert AG, Cupples LA, Kiel DP (2004) Interactions of interleukin-6 promoter polymorphisms with dietary and lifestyle factors and their association with bone mass in men and women from the Framingham Osteoporosis Study. J Bone Miner Res 19:552–559. [PubMed]
Ferrari S, Manen D, Bonjour JP, Slosman D, Rizzoli R (1999a) Bone mineral mass and calcium and phosphate metabolism in young men: relationships with vitamin D receptor allelic polymorphisms. J Clin Endocrinol Metab 84:2043–2048. [PubMed] [Cross Ref]10.1210/jc.84.6.2043
Ferrari S, Rizzoli R, Bonjour JP (1999b) Genetic aspects of osteoporosis. Curr Opin Rheumatol 11:294–300. [PubMed] [Cross Ref]10.1097/00002281-199907000-00013
Ferrari S, Rizzoli R, Manen D, Slosman D, Bonjour JP (1998a) Vitamin D receptor gene start codon polymorphisms (FokI) and bone mineral density: interaction with age, dietary calcium, and 3′-end region polymorphisms. J Bone Miner Res 13:925–930. [PubMed]
Ferrari S, Rizzoli R, Slosman D, Bonjour JP (1998b) Familial resemblance for bone mineral mass is expressed before puberty. J Clin Endocrinol Metab 83:358–361. [PubMed] [Cross Ref]10.1210/jc.83.2.358
Ferrari SL, Ahn-Luong L, Garnero P, Humphries SE, Greenspan SL (2003) Two promoter polymorphisms regulating interleukin-6 gene expression are associated with circulating levels of C-reactive protein and markers of bone resorption in postmenopausal women. J Clin Endocrinol Metab 88:255–259. [PubMed] [Cross Ref]10.1210/jc.2002-020092
Ferrari SL, Rizzoli R, Slosman DO, Bonjour JP (1998c) Do dietary calcium and age explain the controversy surrounding the relationship between bone mineral density and vitamin D receptor gene polymorphisms? J Bone Miner Res 13:363–370. [PubMed]
Garnier-Gere P, Dillmann C (1992) A computer program for testing pairwise linkage disequilibria in subdivided populations. J Hered 83:239. [PubMed]
Gong Y, Slee RB, Fukai N, Rawadi G, Roman-Roman S, Reginato AM, Wang H, et al (2001) LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell 107:513–523. [PubMed]
Hey PJ, Twells RC, Phillips MS, Yusuke N, Brown SD, Kawaguchi Y, Cox R, Guochun X, Dugan V, Hammond H, Metzker ML, Todd JA, Hess JF (1998) Cloning of a novel member of the low-density lipoprotein receptor family. Gene 216:103–111. [PubMed] [Cross Ref]10.1016/S0378-1119(98)00311-4
Hirschhorn JN, Lindgren CM, Daly MJ, Kirby A, Schaffner SF, Burtt NP, Altshuler D, Parker A, Rioux JD, Platko J, Gaudet D, Hudson TJ, Groop LC, Lander ES (2001) Genomewide linkage analysis of stature in multiple populations reveals several regions with evidence of linkage to adult height. Am J Hum Genet 69:106–116. [PMC free article] [PubMed]
Kanis JA (2002) Diagnosis of osteoporosis and assessment of fracture risk. Lancet 359:1929–1936. [PubMed] [Cross Ref]10.1016/S0140-6736(02)08761-5
Kato M, Patel MS, Levasseur R, Lobov I, Chang BH, Glass DA 2nd, Hartmann C, Li L, Hwang TH, Brayton CF, Lang RA, Karsenty G, Chan L (2002) Cbfa1-independent decrease in osteoblast proliferation, osteopenia, and persistent embryonic eye vascularization in mice deficient in Lrp5, a Wnt coreceptor. J Cell Biol 157:303–314. [PMC free article] [PubMed] [Cross Ref]10.1083/jcb.200201089
Khosla S, Melton LJ 3rd, Atkinson EJ, O’Fallon WM (2001) Relationship of serum sex steroid levels to longitudinal changes in bone density in young versus elderly men. J Clin Endocrinol Metab 86:3555–3561. [PubMed] [Cross Ref]10.1210/jc.86.8.3555
Koller DL, Rodriguez LA, Christian JC, Slemenda CW, Econs MJ, Hui SL, Morin P, Conneally PM, Joslyn G, Curran ME, Peacock M, Johnston CC, Foroud T (1998) Linkage of a QTL contributing to normal variation in bone mineral density to chromosome 11q12-13. J Bone Miner Res 13:1903–1908. [PubMed]
Little RD, Carulli JP, Del Mastro RG, Dupuis J, Osborne M, Folz C, Manning SP, et al (2002) A mutation in the LDL receptor–related protein 5 gene results in the autosomal dominant high–bone-mass trait. Am J Hum Genet 70:11–19. [PMC free article] [PubMed]
Livshits G, Trofimov S, Malkin I, Kobyliansky E (2002) Transmission disequilibrium test for hand bone mineral density and 11q12-13 chromosomal segment. Osteoporos Int 13:461–467. [PubMed] [Cross Ref]10.1007/s001980200055
Nieters A, Becker N, Linseisen J (2002) Polymorphisms in candidate obesity genes and their interaction with dietary intake of n-6 polyunsaturated fatty acids affect obesity risk in a sub-sample of the EPIC-Heidelberg cohort. Eur J Nutr 41:210–221. [PubMed] [Cross Ref]10.1007/s00394-002-0378-y
Niu T, Qin ZS, Xu X, Liu JS (2002) Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am J Hum Genet 70:157–169. [PMC free article] [PubMed]
Okubo M, Horinishi A, Kim DH, Yamamoto TT, Murase T (2002) Seven novel sequence variants in the human low density lipoprotein receptor related protein 5 (LRP5) gene. Hum Mutat 19:186. [PubMed] [Cross Ref]10.1002/humu.9012
Orwoll ES, Belknap JK, Klein RF (2001) Gender specificity in the genetic determinants of peak bone mass. J Bone Miner Res 16:1962–1971. [PubMed]
Patel MS, Karsenty G (2002) Regulation of bone formation and vision by LRP5. N Engl J Med 346:1572–1574. [PubMed] [Cross Ref]10.1056/NEJM200205163462011
Peacock M, Turner CH, Econs MJ, Foroud T (2002) Genetics of osteoporosis. Endocr Rev 23:303–326. [PubMed] [Cross Ref]10.1210/er.23.3.303
Perola M, Ohman M, Hiekkalinna T, Leppävuori J, Pajukanta P, Wessman M, Koskenvuo M, Palotie A, Lange K, Kaprio J, Peltonen L (2001) Quantitative-trait-locus analysis of body-mass index and of stature, by combined analysis of genome scans of five Finnish study groups. Am J Hum Genet 69:117–123. [PMC free article] [PubMed]
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. [PMC free article] [PubMed]
Quinton ND, Lee AJ, Ross RJ, Eastell R, Blakemore AI (2001) A single nucleotide polymorphism (SNP) in the leptin receptor is associated with BMI, fat mass and leptin levels in postmenopausal Caucasian women. Hum Genet 108:233–236. [PubMed] [Cross Ref]10.1007/s004390100468
Rizzoli R, Bonjour JP (1999) Determinants of peak bone mass and mechanisms of bone loss. Osteoporos Int 9:S17–S23. [PubMed]
——— (1997) Hormones and bones. Lancet 349 Suppl 1:SI20–23.9057775 [PubMed] [Cross Ref]
Ronaghi M (2001) Pyrosequencing sheds light on DNA sequencing. Genome Res 11:3–11. [PubMed] [Cross Ref]10.1101/gr.11.1.3
Rozas J, Rozas R (1995) DnaSP, DNA sequence polymorphism: an interactive program for estimating population genetics parameters from DNA sequence data. Comput Appl Biosci 11:621–625. [PubMed]
Sankoh AJ, Huque MF, Dubey SD (1997) Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Stat Med 16:2529–2542. [PubMed] [Cross Ref]10.1002/(SICI)1097-0258(19971130)16:22<2529::AID-SIM692>3.0.CO;2-J
Seeman E (2002) Pathogenesis of bone fragility in women and men. Lancet 359:1841–1850. [PubMed] [Cross Ref]10.1016/S0140-6736(02)08706-8
Tamai K, Semenov M, Kato Y, Spokony R, Liu C, Katsuyama Y, Hess F, Saint-Jeannet JP, He X (2000) LDL-receptor-related proteins in Wnt signal transduction. Nature 407:530–535. [PubMed] [Cross Ref]10.1038/35035117
Theintz G, Buchs B, Rizzoli R, Slosman D, Clavien H, Sizonenko PC, Bonjour JP (1992) Longitudinal monitoring of bone mass accumulation in healthy adolescents: evidence for a marked reduction after 16 years of age at the levels of lumbar spine and femoral neck in female subjects. J Clin Endocrinol Metab 75:1060–1065. [PubMed] [Cross Ref]10.1210/jc.75.4.1060
Twells RC, Mein CA, Payne F, Veijola R, Gilbey M, Bright M, Timms A, Nakagawa Y, Snook H, Nutland S, Rance HE, Carr P, Dudbridge F, Cordell HJ, Cooper J, Tuomilehto-Wolf E, Tuomilehto J, Phillips M, Metzker M, Hess JF, Todd JA (2003) Linkage and association mapping of the LRP5 locus on chromosome 11q13 in type 1 diabetes. Hum Genet 113:99–105. [PubMed]
van der Sluis IM, de Muinck Keizer-Schrama SM, Krenning EP, Pols HA, Uitterlinden AG (2003) Vitamin D receptor gene polymorphism predicts height and bone size, rather than bone density in children and young adults. Calcif Tissue Int 73:332–338. [PubMed] [Cross Ref]12874698
van Meurs JB, Schuit SC, Weel AE, van der Klift M, Bergink AP, Arp PP, Colin EM, Fang Y, Hofman A, van Duijn CM, van Leeuwen JP, Pols HA, Uitterlinden AG (2003) Association of 5′ estrogen receptor alpha gene polymorphisms with bone mineral density, vertebral bone area and fracture risk. Hum Mol Genet 12:1745–1754. [PubMed] [Cross Ref]10.1093/hmg/ddg176
Van Wesenbeeck L, Cleiren E, Gram J, Beals RK, Bénichou O, Scopelliti D, Key L, Renton T, Bartels C, Gong Y, Warman ML, de Vernejoul M-C, Bollerslev J, Van Hul W (2003) Six novel missense mutations in the LDL Receptor-related protein 5 (LRP5) gene in different conditions with increased bone density. Am J Hum Genet 72:763–771. [PMC free article] [PubMed]
Wodarz A, Nusse R (1998) Mechanisms of Wnt signaling in development. Annu Rev Cell Dev Biol 14:59–88. [PubMed] [Cross Ref]10.1146/annurev.cellbio.14.1.59

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