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Keywords:

  • osteoporosis;
  • bone geometry;
  • femoral neck;
  • whole genome linkage scan

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

A genome-wide linkage scan was performed in a sample of 79 multiplex pedigrees to identify genomic regions linked to femoral neck cross-sectional geometry. Potential quantitative trait loci were detected at several genomic regions, such as 10q26, 20p12-q12, and chromosome X.

Introduction: Bone geometry is an important determinant of bone strength and osteoporotic fractures. Previous studies have shown that femoral neck cross-sectional geometric variables are under genetic controls. To identify genetic loci underlying variation in femoral neck cross-sectional geometry, we conducted a whole genome linkage scan for four femoral neck cross-sectional geometric variables in 79 multiplex white pedigrees.

Materials and Methods: A total of 1816 subjects from 79 pedigrees were genotyped with 451 microsatellite markers across the human genome. We performed linkage analyses on the entire data, as well as on men and women separately.

Results: Significant linkage evidence was identified at 10q26 for buckling ratio (LOD = 3.27) and Xp11 (LOD = 3.45) for cortical thickness. Chromosome region 20p12-q12 showed suggestive linkage with cross-sectional area (LOD = 2.33), cortical thickness (LOD = 2.09), and buckling ratio (LOD = 1.94). Sex-specific linkage analyses further supported the importance of 20p12-q12 for cortical thickness (LOD = 2.74 in females and LOD = 1.88 in males) and buckling ratio (LOD = 5.00 in females and LOD = 3.18 in males).

Conclusions: This study is the first genome-wide linkage scan searching for quantitative trait loci underlying femoral neck cross-sectional geometry in humans. The identification of the genes responsible for bone geometric variation will improve our knowledge of bone strength and aid in development of diagnostic approaches and interventions for osteoporotic fractures.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

OSTEOPOROSIS IS A major public health problem and results in >1.5 million osteoporotic fractures in the United States annually.(1,2) Among fractures at various skeletal sites, hip fractures are the most devastating because they are commonly associated with substantial pain, cost more to repair, and cause more morbidity as well as mortality than any other type of osteoporotic fractures.(3,4) Although low BMD has been commonly used as a risk factor for predicting fractures,(5–7) a growing body of evidence indicates that many factors other than BMD contribute importantly to bone strength and fracture risk.(8–11)

A number of studies have shown that the bone cross-sectional geometry is an important determinant of bone strength and risk of fracture.(12–15) From a biomechanical perspective, bone strength is correlated with the shape and distribution of bone.(16–18) It was suggested that biomechanical parameters derived from the bone cross-sectional geometric variables would be a better indicator of bone strength than BMD.(19,20) Furthermore, several studies have shown that these bone cross-sectional geometric parameters can predict the risk of fractures.(21–26)

Thus far, there have been relatively few genetic studies on femoral neck cross-sectional geometry. The few studies conducted in mice(27,28) and humans(29) have suggested that the variation of the femoral neck cross-sectional geometry is under strong genetic control. However, no genome-wide mapping for femoral neck cross-sectional geometry has been reported in humans. In this study, we estimated four femoral neck cross-sectional geometric variables in 1816 subjects from 79 white pedigrees and conducted the first whole genome linkage scan (WGS) for femoral neck cross-sectional geometry.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Subjects

The study was approved by the Creighton University Institutional Review Board. All the study subjects signed informed-consent documents before entering the project. A total of 1816 subjects from 79 pedigrees were included in the present WGS. All the study subjects are white and of European origin. These 79 pedigrees vary in size from 4 to 416 individuals, with a mean size of 32.

All these samples were initially recruited for studies on BMD.(30,31) Among the 79 pedigrees, 50 pedigrees were ascertained through probands with low BMD (ZBMD ≤ −1.28 at the hip or spine, belonging to the bottom 10% in the age-matched population), 25 pedigrees were recruited through probands having high BMD (ZBMD ≥ +1.28 at the hip or spine, belonging to top 10% in the age-matched population), and 4 pedigrees were recruited without regard to BMD. The exclusion criteria have been detailed previously.(32) Briefly, subjects with chronic diseases and conditions that may potentially affect bone mass were excluded. The exclusion criteria were assessed by nurse-administered questionnaires and/or medical records.

Measurement

Areal BMD (g/cm2) and bone size (cm2) of the femoral neck were measured by DXA with a Hologic 1000, 2000+, or 4500 scanner (Hologic, Bedford, MA, USA). All scanners are calibrated daily, and long-term precision is monitored with external phantoms. The CVs of femoral neck BMD and bone size measurements obtained on the Hologic 2000 + scanner are 1.87% and 1.94%, respectively. Similar CVs were obtained on Hologic 1000 and 4500 scanners.(33) BMD data obtained from different machines were transformed to a compatible measurement using the transformation formula described in Genant et al.(34) Areal bone size measurements by different scanners in our center are highly compatible with one another and are well within the precision limits.(33) In particular, members of the same pedigree were usually measured on the same type of machine, ensuring minimum or no effect on our linkage analyses caused by measurements by different scanners.

Using DXA-derived femoral neck BMD and bone size, we estimated four femoral neck cross-sectional geometric variables. The algorithm and the underlying assumptions regarding the geometry and structure of femoral neck have been detailed earlier.(18,22,35) Briefly, the method assumes that the bone within the femoral neck region has the configuration of a uniform right circular cylinder, 60% of the measured bone mass is cortical (i.e., fc = 0.6), and the effective density of bone mineral in fully mineralized bone tissue is 1.05 g/cm3 (i.e., ρm = 1.05 g/cm3).(22)

The four estimated femoral neck cross-sectional geometric variables are as follows: cross-sectional area (CSA), an indicator of bone axial compression strength; section modulus (Z), an index of bone bending strength; cortical thickness (CT); and buckling ratio (BR), an index of bone structural instability. They are computed as follows:

  • equation image

where W is the femoral neck periosteal diameter and can be approximated by dividing the areal bone size of femoral neck by the width of the region of interest (in Hologic DXA systems, the width of the femoral neck region is standardized at 1.5 cm).(21)

  • equation image

where

  • equation image

where

  • equation image

pt is the trabecular porosity

  • equation image

Genotyping

For each subject, DNA was extracted from peripheral blood using the Puregene DNA isolation kit (Gentra Systems, Minneapolis, MN, USA). All subjects were genotyped with 451 microsatellite (MS) markers, including 432 from autosomes and 19 from chromosome X. These markers are from ABI PRISM Linkage Mapping Sets Version 2.5 (Applied Biosystems, Foster City, CA, USA). The overall marker density is ∼8.1 cM per marker.

PCRs were performed on PE 9700 thermocyclers (Applied Biosystems) with cycling conditions suggested in the manual of ABI PRISM Linkage Mapping Sets Version 2.5. Marker allele identification and sizing were performed using ABI PRISM 3700 DNA Analyzer (Applied Biosystems) and GENESCAN Version 4.0 and GENOTYPER Version 4.0 software (Applied Biosystems). A genetic database management system (GenoDB)(36) was used to manage the genotype data. PedCheck(37) was used for checking the Mendelian inheritance pattern at all the marker loci and for confirming the alleged relationships of family members within pedigrees. After three rounds of data checking and regenotyping, the data that could still not pass the PedCheck or were missing were counted as the genotype missing or error data; its rate was ∼0.3%. All 451 markers were successfully genotyped. These markers have an average population heterozygosity of ∼0.79.

Statistical analyses

A variance component linkage analysis for quantitative traits(38–40) was performed. The program used was SOLAR (Sequential Oligogenic Linkage Analysis Routines),(38) available online (http://www.sfbr.org/sfbr/public/software/solar/solar.html). Two- and multipoint linkage analyses were performed for each femoral neck cross-sectional geometric variable in the 79 pedigrees.

Age, sex, height, weight, and sex-by-age interaction were tested for importance on femoral neck cross-sectional geometric phenotypes, and significant factors were adjusted as covariates in linkage analyses. In the 79 pedigrees, the kurtosis values of adjusted cross-sectional geometric variables ranged from 0.56 to 0.92. Although the variance component analyses implemented in SOLAR are quite robust to reasonable violations of normality of the data (kurtosis < 2.0),(41) to accommodate the observed deviation from normality in our bone size data, we estimated a correction constant for LOD scores using the procedure “lodadj” implemented in SOLAR.(42) This procedure simulated a fully informative marker, unlinked to phenotypes in 10,000 replicates. For each marker, IBDs (identity by descent) were calculated, and a LOD score was computed. For the four femoral neck cross-sectional geometric variables, the estimated correction constants for LOD scores ranged from 0.99 to 1.05. All LOD scores given in the text are empirically adjusted LOD scores. In addition, we calculated empirical pointwise p values for adjusted LOD scores using the “empp” command in SOLAR. When a putative quantitative trait loci (QTL) is suggested, the proportion of phenotypic variation attributable to this QTL can be estimated by SOLAR. The estimate is usually inflated and can be considered as the upper bound of the genetic effect because of the locus.(43)

For the chromosome X, SOLAR can only handle two-point analyses. Other software, such as GENEHUNTER and MERLIN,(44) which can perform multipoint linkage analysis on chromosome X, unfortunately are not good at handling large pedigrees that make up most of our study sample. Therefore, we applied the program FASTER (Family Smart Eliminator, available at http://www.hoschl.cz/faster/), to break down those large pedigrees into smaller ones, by splitting families and/or deleting family members while keeping members with genotypes as many as possible. All pedigrees that would be used in multipoint analyses must meet the criteria 2NF ≤ 20, in which N is number of nonfounders (individuals with at least one parent), and F is the number of founders (individuals without parents). Eventually, multipoint linkage analyses on chromosome X were performed in 121 derivative pedigrees (including 60 intact pedigrees and 61 newly derived pedigrees) with 920 subjects using the variance component method implemented in Merlin.

Because gender-specific influences on bone geometry have been shown in epidemiological and genetic studies,(27,45,46) we also conducted linkage analyses for femoral neck cross-sectional geometric variables in men and women separately in the 79 pedigrees. In the sex-specific analyses, the phenotype values for individuals of the opposite sex were recorded as missing data. Also, age, height, and weight were tested, and significant factors were adjusted as covariates in the sex-specific linkage analyses.

To aid in interpretation of the linkage results, we performed pairwise correlation analyses between the femoral neck cross-sectional geometric variables. Moreover, for comparison between these results and our previous reported WGS results for hip BMD,(30,31) we also calculated the correlations between hip BMD and the femoral neck cross-sectional geometric variables. In addition, we performed a principal component analysis of the four cross-sectional geometric variables, using the statistical package SAS (SAS v.6.12; SAS Institute, Cary, NC, USA). Individual scores of the first two principal components (PC1 and PC2) were used as additional phenotypes in the linkage analyses.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

The basic characteristics of the four femoral neck cross-sectional geometric variables in the study subjects stratified by age and sex are summarized in Table 1. The variable dynamics with gender and aging in the 79 pedigrees are consistent with pervious reports.(22,46–49) Males generally have larger geometric variables than females. In both sexes, CSA, CT, and Z decrease with the advancing of age, whereas BR increases with aging. The estimated h2 (SE) for BR, CSA, CT, and Z are 0.59 (0.04), 0.50 (0.04), 0.62 (0.04), and 0.37 (0.04), respectively. As expected,(21) the CSA, CT, and BR are highly correlated with hip BMD, whereas the correlation between hip BMD and Z is relatively modest (Table 2). The first two principal components (PC1 and PC2) explained 84% and 14%, respectively, of the total variation of the four cross-sectional geometric variables. The principle component loadings (i.e., the correlation coefficients between the variables and the factors; Table 2) suggest that PC1 is primarily a factor for CSA, and PC2 is mainly responsible for BR variation.

Table Table 1.. Basic Characteristics of the Femoral Neck Cross-Sectional Geometric Variables in the Study Subjects Stratified by Age and Sex
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Table Table 2.. Phenotypic Correlation Between Femoral Neck Cross-Sectional Geometric Variables, Hip BMD, and Principal Components
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The multipoint linkage results for autosomes are summarized in Fig. 1 and Table 3. For autosomes showing at least suggestive linkage evidence (LOD ≥ 1.9) in multipoint linkage analyses for any one of the four femoral neck cross-sectional variables, we also plot the results in Fig. 2. Because a two-point LOD score of 3.45 was achieved at marker DXS991 for CT, we also plotted the two-point LOD score results for the chromosome X and present it in Fig. 2.

Table Table 3.. Genomic Regions with LOD > 1.5 for Femoral Neck Cross-Sectional Geometry
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Figure FIG. 1.. Multipoint linkage results on autosomes for femoral neck cross-sectional geometric variables.

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Figure FIG. 2.. Genomic regions showing at least suggestive linkage (LOD ≥ 1.9) for femoral neck cross-sectional geometric variables. The variables are indicated as BR (solid black line), CSA (dotted line), CT (dashed line), and Z (gray line). (A-C) Multipoint linkage results on chromosomes 8, 10, and 20, respectively. (D) Two-point linkage results on chromosome X.

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The most significant linkage result, a two-point LOD score of 3.45 (pointwise p < 0.0001) was achieved on Xp11 at marker DXS991 for CT (Table 3; Fig. 2D). This putative QTL may account for ∼9.6% of CT variation (after adjusting for age, height, weight, sex, and sex-by-age interaction). In addition, several other markers on chromosome X also showed suggestive linkage for CT (Table 3; Fig. 2D). The two-point LOD scores for other cross-sectional geometric variables exhibited similar patterns as that of CT, but none exceeded the threshold of suggestive linkage (Fig. 2D). All the multipoint LOD scores calculated on chromosome X by Merlin in derived pedigrees are <0.5 (data not shown).

Chromosome region on 10q26 near marker D10S212 showed significant linkage evidence for BR (LOD = 3.27, pointwise p < 0.0001) and PC2 (LOD = 3.13, pointwise p < 0.0001; Fig. 2B; Table 3). Approximately 17.6% of BR variation (after adjusting for age, height, weight, sex, and sex-by-age interaction) may be attributable to this locus. We also found suggestive evidence of linkage on chromosomes 8 and 20 (Figs. 2A and 2C; Table 3). The former achieved a LOD score of 1.88 (pointwise p = 0.0013) at 8q24 near marker D8S284 for CSA (Fig. 2A). The results on chromosome 20 are interesting, because a relatively broad genomic region showed suggestive linkage to several variables, including CSA (LOD = 2.33, pointwise p = 0.0004), CT (LOD = 2.09, pointwise p = 0.0007), BR (LOD = 1.94, pointwise p = 0.0015), and PC1 (LOD = 2.16, pointwise p = 0.0008; Fig. 2C; Table 3). The linkage peaks of the three variables substantially overlapped (Fig. 2C), encompassing a ∼40-cM region on 20p12-q12. For parameter Z, linkage analyses in the 79 pedigrees did not indicate any significant or suggestive linkage regions across the entire human genome.

The major results of sex-specific linkage analyses are summarized in Fig. 3. The most interesting results were observed on chromosome 20, where both males and females showed significant or suggestive linkage for BR and CT, respectively (Figs. 3C and 3G). For instance, a significant linkage evidence was detected for BR on 20q12 near marker D20S107 in females (LOD = 5.00), whereas a nearly significant linkage evidence was also obtained in males for BR at 20p12 near marker D20S186 (LOD = 3.18; Fig. 3C). Both of the two sex-specific linkage peaks largely overlapped with the linkage peak detected in the entire 79 pedigrees (Fig. 3C). A very similar situation occurred for CT on chromosome 20, where suggestive linkage was detected in females near marker D20S107 (LOD = 2.74) and in males near marker D20S186 (LOD = 1.88; Fig. 3G). In addition, we also detected suggestive linkage evidence on several other genomic regions in females (Fig. 3), including 2q37 (LOD = 2.39), 10p15 (LOD = 2.19), and 10q26 (LOD = 1.92) for BR, as well as 8q24 (LOD = 2.27) and 16q12 (LOD = 2.03) for CT. Similarly, potential male-specific QTLs were identified on 15q21 (LOD = 2.87) for CT and 4p16 (LOD = 2.86) for Z (Fig. 3).

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Figure FIG. 3.. Genomic regions with LOD ≥ 1.9 in sex-specific linkage analyses for cross-sectional geometric variables. The samples were indicated as combined 79 pedigrees (solid line), females in 79 pedigrees (gray line), and males in 79 pedigrees (dashed line). (A-C) Multipoint linkage results for BR on chromosomes 2, 10, and 20, respectively. (D-G) Multipoint linkage results for CT on chromosomes 8, 15, 16, and 20, respectively. (H) Z on chromosome 4.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

It has long been accepted that bone geometric structure is an important determinant of bone strength and fracture resistance.(13,14,23,50) Hip geometric structures have been reported in association with hip fracture incidence, largely independent of the effect of BMD.(22–26) Bone geometry has been suggested to be influenced by genetic factors(27,28,51); however, few genetic study has been conducted for mapping these genetic factors. In this study, using a method described previously,(18,22) we estimated four femoral neck cross-sectional geometric variables from the DXA-derived data and conducted the first genome-wide linkage mapping for femoral neck geometric structure in 79 multigenerational pedigrees.

Although the 79 pedigrees were mainly recruited through probands having extreme BMD values and most of the estimated femoral neck cross-sectional geometric parameters are highly correlated with hip BMD, accounting for the ascertainment scheme in linkage analyses did not yield much difference in our results (data not shown). This is probably because only one subject (i.e., the proband) in each pedigree was ascertained for extreme values, and thus the effect of such sampling scheme diminished drastically with many large and complex pedigrees in our sample.

Significant linkages were identified on chromosome X for CT. Although the multipoint linkage analyses (by Merlin) detected no significant or suggestive linkage on chromosome X for CT, this lack of support could be caused by dramatically reduced linkage power in the derived pedigrees. For instance, there are only 603 siblings contained in the derivative pedigrees compared with 3846 siblings in the 79 pedigrees.(31) In addition, previous studies in mice have also reported a strong linkage between femoral midshaft CSA and mouse chromosome X,(27) which is highly homologous to human chromosome X. In view of the high correlation between CSA and CT, this evidence further supports that chromosome X may contain QTL(s) underlying variation of bone cross-sectional geometry. Potential candidate genes include genes for tissue inhibitor of metalloproteinase 1 (TIMP1) and type IV collagen α 5 (COL4A5).

Another interesting finding was on chromosome 20p12-q12, which showed suggestive linkage for CSA, BR, CT, and PC1. The linkage peaks at this region for these variables are relatively broad and complex, although largely overlapped, suggesting two or more QTLs may reside in this area. Subsequent sex-specific linkage analyses substantiated this speculation. In fact, comparing with the linkage peaks observed in the entire sample, the signals obtained for BR and CT in the sex-specific analyses are generally much stronger and with clearer patterns. In females, both the linkage peaks of BR and CT shifted to the region of 20q12, whereas in males, the peaks shifted to the region of 20p12. In addition, we have detected suggestive linkage evidence (LOD = 2.33) at 20p12 for hip BMD in our previous WGS.(31) The apparent pleiotropic effects of the QTL(s) on various cross-sectional geometric variables and hip BMD are not unexpected, given these cross-sectional geometric variables are all derived from the femoral neck BMD and periosteal diameter. The fact that PC1 and PC2 together can explain 98% of the total variation of the four cross-sectional geometric variables also suggests that these variables are highly correlated. Interestingly, previous findings in Icelandic population have shown that this region is significantly linked to osteoporotic fractures, and a gene located in this region, BMP2, can partially account for this linkage.(52) This evidence makes region 20p12-q12 a very promising candidate for identifying genes of bone geometric structures. Assessing the importance of BMP2 on bone geometry and searching for other genetic determinants at this region may be worth pursuing.

On 8q24, we detected a suggestive linkage evidence for CSA in the 79 pedigrees. Whereas sex-specific linkage analyses for CSA did not provide suggestive linkage evidence in females or males (data not shown), a suggestive linkage signal was identified at the same genomic region for CT in females. Previously, Volkman et al.(28) reported a significant association (p < 0.001) between the mice femoral midshaft CSA and a mouse genetic marker, D15Mit100, which is located at the region homologous to human 8q24. This region harbors a prominent candidate gene, osteoprotegerin (OPG). OPG is a member of the TNF receptor (TNFR) superfamily. It acts as a decoy receptor that inhibits the effect of RANK-RANKL on the differentiation and activation of osteoclasts.(53) OPG can also suppress the activity of mature osteoclasts and induce apoptosis of osteoclasts.(54) Serum levels of OPG and polymorphisms in the OPG gene have been associated with osteoporotic fractures, independent of BMD.(55,56)

Gender differences in a variety of indices related to hip geometry and structure have been reported.(27,46,57,58) In this study, sex-specific linkage analyses revealed several potential sex-specific QTLs, such 2q37 for BR and 16q12 for CT in females and 4p16 for Z and 15q21 for CT in males. At these genomic regions, the linkage signals detected in the combined sample and the sex-specific linkage analyses exhibited similar patterns, but the former is much lower than the latter. This may reflect the increased efficiency in QTL mapping in gender-stratified linkage analyses caused by increased sample homogeneity. On the other hand, these results might also be treated with caution because of the inflated false-positive and/or false-negative rates caused by increased multiple comparisons and insufficient power in individual subgroups.

Intuitively, the bone cross-sectional geometry would be expected to be correlated with other bone size measurements (e.g., 2-D areal bone size) and height/stature. However, our results are largely inconsistent with previous findings on areal bone size(59,60) and height/stature,(61–71) except for a few regions on chromosome X.(67,69) This inconsistency reflects that bone cross-sectional geometry and areal bone size (and height/stature) may represent different aspects of bone properties. For instance, height/stature is more related to linear growth of the skeleton, which is dependent on the process of endochondral ossification in the growth plates of long bones. Indeed, the correlations between the bone cross-sectional geometric variables and areal bone size (and height/stature) are quite low (data not shown).

There are some potential limitations in this study. The cross-sectional geometric variables used in this study are not directly measured from true cross-sectional images (e.g., QCT) but estimated from areal BMD and bone size based on a few assumptions. However, this method has been used in a number of previous epidemiological or genetic studies.(22,35,72–74) The findings of these studies are consistent with other reports using a variety of methods, including radiography(23,24,75) and QCT.(25,46,47) Therefore, this approximation method indeed represents reasonable estimation of the femoral neck cross-sectional structure. With the advancement in imaging technology, direct and accurate measurement of true femoral neck cross-sectional structure may become more applicable to large samples and thus would be favored in future genetic studies. Another potential limitation is that we used different DXA scanners for phenotype measurement across the 79 pedigrees. However, different scanners were cross-calibrated and most members of the same pedigree were measured on the same scanner. In addition, when we performed linkage analyses after treating scanner type as a covariate or censoring phenotypic data from those individuals scanned on different scanners than the remainder of their families, the results (data not shown) were essentially the same as that of linkage analyses presented here. Therefore, combined analyses of the pedigrees measured by different scanners are warranted. It also should be kept in mind that, because of limitations of the current linkage analysis approaches, even a large sample such as the present one is still underpowered for QTL(s) with small to modest effects.

In summary, we conducted the first genome-wide linkage scan for femoral neck cross-sectional geometry in 79 white pedigrees. Four genomic regions on 8q24, 10q26, 20p12-q12, and chromosome X may harbor QTLs influencing femoral neck cross-sectional geometry. In addition, several additional chromosomal regions showed suggestive linkage for cross-sectional geometry in a gender-specific manner. Because of the complexity of the inheritance pattern of bone cross-sectional geometry and the difficulty in genetic dissection of complex traits, evaluation of our findings in other sufficiently powered samples may be necessary. Once linkage to a genomic region is confirmed, subsequent saturation linkage mapping followed by linkage disequilibrium analyses with dense SNP markers within positively identified regions can confine the QTL to small genomic regions, which is amenable for positional cloning. Successful examples using similar strategies in identifying predisposing genes for complex human diseases have emerged and are growing.(52,76–78) In addition, DNA microarray, proteomics, and other functional studies may complement the genetic mapping studies to eventually identify and confirm causal variants.(79,80)

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Investigators of this work were partially supported by grants from NIH, Health Future Foundation, State of Nebraska, and U.S. DOE. The study also benefited from grant support from CNSF, Huo Ying Dong Education Foundation, Hunan Province, the Ministry of Education of China, and 211 project funding through Xi'an Jiaotong University.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  • 1
    Ray NF, Chan JK, Thamer M, Melton LJ III 1997 Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: Report from the National Osteoporosis Foundation. J Bone Miner Res 12: 2435.
  • 2
    Melton LJ III 2003 Adverse outcomes of osteoporotic fractures in the general population. J Bone Miner Res 18: 11391141.
  • 3
    Cummings SR, Melton LJ 2002 Epidemiology and outcomes of osteoporotic fractures. Lancet 359: 17611767.
  • 4
    Lau EM 2001 Epidemiology of osteoporosis. Best Pract Res Clin Rheumatol 15: 335344.
  • 5
    Cummings SR, Black DM, Nevitt MC, Browner W, Cauley J, Ensrud K, Genant HK, Palermo L, Scott J, Vogt TM 1993 Bone density at various sites for prediction of hip fractures. The Study of Osteoporotic Fractures Research Group. Lancet 341: 7275.
  • 6
    Melton LJ III, Atkinson EJ, O'Fallon WM, Wahner HW, Riggs BL 1993 Long-term fracture prediction by bone mineral assessed at different skeletal sites. J Bone Miner Res 8: 12271233.
  • 7
    Ross PD, Davis JW, Epstein RS, Wasnich RD 1991 Pre-existing fractures and bone mass predict vertebral fracture incidence in women. Ann Intern Med 114: 919923.
  • 8
    Schuit SC, van der KM, Weel AE, De Laet CE, Burger H, Seeman E, Hofman A, Uitterlinden AG, van Leeuwen JP, Pols HA 2004 Fracture incidence and association with bone mineral density in elderly men and women: The Rotterdam Study. Bone 34: 195202.
  • 9
    Nielsen SP 2000 The fallacy of BMD: A critical review of the diagnostic use of dual X-ray absorptiometry. Clin Rheumatol 19: 174183.
  • 10
    McCreadie BR, Goldstein SA 2000 Biomechanics of fracture: Is bone mineral density sufficient to assess risk? J Bone Miner Res 15: 23052308.
  • 11
    Watts NB 2002 Bone quality: Getting closer to a definition. J Bone Miner Res 17: 11481150.
  • 12
    Yoshikawa T, Turner CH, Peacock M, Slemenda CW, Weaver CM, Teegarden D, Markwardt P, Burr DB 1994 Geometric structure of the femoral neck measured using dual-energy X-ray absorptiometry. J Bone Miner Res 9: 10531064.
  • 13
    Biggemann M, Hilweg D, Brinckmann P 1988 Prediction of the compressive strength of vertebral bodies of the lumbar spine by quantitative computed tomography. Skeletal Radiol 17: 264269.
  • 14
    Edmondston SJ, Singer KP, Day RE, Price RI, Breidahl PD 1997 Ex vivo estimation of thoracolumbar vertebral body compressive strength: The relative contributions of bone densitometry and vertebral morphometry. Osteoporos Int 7: 142148.
  • 15
    Cheng XG, Lowet G, Boonen S, Nicholson PH, Brys P, Nijs J, Dequeker J 1997 Assessment of the strength of proximal femur in vitro: Relationship to femoral bone mineral density and femoral geometry. Bone 20: 213218.
  • 16
    Yamauchi M, Sugimoto T, Chihara K 2004 Determinants of vertebral fragility: The participation of cortical bone factors. J Bone Miner Metab 22: 7985.
  • 17
    Turner CH, Burr DB 1993 Basic biomechanical measurements of bone: A tutorial. Bone 14: 595608.
  • 18
    Beck TJ 2003 Measuring the structural strength of bones with dual-energy X-ray absorptiometry: Principles, technical limitations, and future possibilities. Osteoporos Int 14: S81S88.
  • 19
    Myers ER, Hecker AT, Rooks DS, Hipp JA, Hayes WC 1993 Geometric variables from DXA of the radius predict forearm fracture load in vitro. Calcif Tissue Int 52: 199204.
  • 20
    Myburgh KH, Zhou LJ, Steele CR, Arnaud S, Marcus R 1992 In vivo assessment of forearm bone mass and ulnar bending stiffness in healthy men. J Bone Miner Res 7: 13451350.
  • 21
    Rivadeneira F, Houwing-Duistermaat JJ, Beck TJ, Janssen JA, Hofman A, Pols HA, Van Duijn CM, Uitterlinden AG 2004 The influence of an insulin-like growth factor I gene promoter polymorphism on hip bone geometry and the risk of nonvertebral fracture in the elderly: The Rotterdam Study. J Bone Miner Res 19: 12801290.
  • 22
    Duan Y, Beck TJ, Wang XF, Seeman E 2003 Structural and biomechanical basis of sexual dimorphism in femoral neck fragility has its origins in growth and aging. J Bone Miner Res 18: 17661774.
  • 23
    Gluer CC, Cummings SR, Pressman A, Li J, Gluer K, Faulkner KG, Grampp S, Genant HK 1994 Prediction of hip fractures from pelvic radiographs: The study of osteoporotic fractures. The Study of Osteoporotic Fractures Research Group. J Bone Miner Res 9: 671677.
  • 24
    Partanen J, Jamsa T, Jalovaara P 2001 Influence of the upper femur and pelvic geometry on the risk and type of hip fractures. J Bone Miner Res 16: 15401546.
  • 25
    Augat P, Reeb H, Claes LE 1996 Prediction of fracture load at different skeletal sites by geometric properties of the cortical shell. J Bone Miner Res 11: 13561363.
  • 26
    Beck TJ, Ruff CB, Mourtada FA, Shaffer RA, Maxwell-Williams K, Kao GL, Sartoris DJ, Brodine S 1996 Dual-energy X-ray absorptiometry derived structural geometry for stress fracture prediction in male U.S. Marine Corps recruits. J Bone Miner Res 11: 645653.
  • 27
    Klein RF, Turner RJ, Skinner LD, Vartanian KA, Serang M, Carlos AS, Shea M, Belknap JK, Orwoll ES 2002 Mapping quantitative trait loci that influence femoral cross-sectional area in mice. J Bone Miner Res 17: 17521760.
  • 28
    Volkman SK, Galecki AT, Burke DT, Paczas MR, Moalli MR, Miller RA, Goldstein SA 2003 Quantitative trait loci for femoral size and shape in a genetically heterogeneous mouse population. J Bone Miner Res 18: 14971505.
  • 29
    Slemenda CW, Turner CH, Peacock M, Christian JC, Sorbel J, Hui SL, Johnston CC 1996 The genetics of proximal femur geometry, distribution of bone mass and bone mineral density. Osteoporos Int 6: 178182.
  • 30
    Deng HW, Xu FH, Huang QY, Shen H, Deng H, Conway T, Liu YJ, Liu YZ, Li JL, Zhang HT, Davies KM, Recker RR 2002 A whole-genome linkage scan suggests several genomic regions potentially containing quantitative trait loci for osteoporosis. J Clin Endocrinol Metab 87: 51515159.
  • 31
    Shen H, Zhang YY, Long JR, Xu FH, Liu YZ, Xiao P, Zhao LJ, Xiong DH, Liu YJ, Dvornyk V, Araujo S, Liu PY, Li JL, Conway T, Davies KM, Recker RR, Deng HW 2004 A genome-wide linkage scan for bone mineral density in an extended sample: Evidence for linkage on 11q23 and Xq27. J Med Genet 41: 743751.
  • 32
    Deng HW, Deng H, Liu YJ, Liu YZ, Xu FH, Shen H, Conway T, Li JL, Huang QY, Davies KM, Recker RR 2002 A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet 70: 11381151.
  • 33
    Deng HW, Deng XT, Conway T, Xu FH, Heaney R, Recker RR 2002 Determination of bone size of hip, spine, and wrist in human pedigrees by genetic and lifestyle factors. J Clin Densitom 5: 4556.
  • 34
    Genant HK, Grampp S, Gluer CC, Faulkner KG, Jergas M, Engelke K, Hagiwara S, Van Kuijk C 1994 Universal standardization for dual X-ray absorptiometry: Patient and phantom cross-calibration results. J Bone Miner Res 9: 15031514.
  • 35
    Rivadeneira F, Houwing-Duistermaat JJ, Vaessen N, Vergeer-Drop JM, Hofman A, Pols HA, Van Duijn CM, Uitterlinden AG 2003 Association between an insulin-like growth factor I gene promoter polymorphism and bone mineral density in the elderly: The Rotterdam Study. J Clin Endocrinol Metab 88: 38783884.
  • 36
    Li JL, Deng H, Lai DB, Xu F, Chen J, Gao G, Recker RR, Deng HW 2001 Toward high-throughput genotyping: Dynamic and automatic software for manipulating large-scale genotype data using fluorescently labeled dinucleotide markers. Genome Res 11: 13041314.
  • 37
    O'Connell JR, Weeks DE 1998 PedCheck: A program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 63: 259266.
  • 38
    Almasy L, Blangero J 1998 Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62: 11981211.
  • 39
    Amos CI 1994 Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54: 535543.
  • 40
    Amos CI, Zhu DK, Boerwinkle E 1996 Assessing genetic linkage and association with robust components of variance approaches. Ann Hum Genet 60: 143160.
  • 41
    Allison DB, Neale MC, Zannolli R, Schork NJ, Amos CI, Blangero J 1999 Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure. Am J Hum Genet 65: 531544.
  • 42
    Blangero J, Williams JT, Almasy L 2000 Robust LOD scores for variance component-based linkage analysis. Genet Epidemiol 19 (Suppl 1): S8S14.
  • 43
    Goring HH, Terwilliger JD, Blangero J 2001 Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69: 13571369.
  • 44
    Abecasis GR, Cherny SS, Cookson WO, Cardon LR 2002 Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30: 97101.
  • 45
    Seeman E 2001 Clinical review 137: Sexual dimorphism in skeletal size, density, and strength. J Clin Endocrinol Metab 86: 45764584.
  • 46
    Taaffe DR, Lang TF, Fuerst T, Cauley JA, Nevitt MC, Harris TB 2003 Sex- and race-related differences in cross-sectional geometry and bone density of the femoral mid-shaft in older adults. Ann Hum Biol 30: 329346.
  • 47
    Riggs BL, Melton IL III, Robb RA, Camp JJ, Atkinson EJ, Peterson JM, Rouleau PA, McCollough CH, Bouxsein ML, Khosla S 2004 Population-based study of age and sex differences in bone volumetric density, size, geometry, and structure at different skeletal sites. J Bone Miner Res 19: 19451954.
  • 48
    Beck TJ, Ruff CB, Bissessur K 1993 Age-related changes in female femoral neck geometry: Implications for bone strength. Calcif Tissue Int 53 (Suppl 1): S41S46.
  • 49
    Beck TJ, Looker AC, Ruff CB, Sievanen H, Wahner HW 2000 Structural trends in the aging femoral neck and proximal shaft: Analysis of the Third National Health and Nutrition Examination Survey dual-energy X-ray absorptiometry data. J Bone Miner Res 15: 22972304.
  • 50
    Smith RW, Walker RR 1964 Femoral expansion in aging women: Implications for osteoporosis and fractures. Science 145: 156157.
  • 51
    Akhter MP, Iwaniec UT, Covey MA, Cullen DM, Kimmel DB, Recker RR 2000 Genetic variations in bone density, histomorphometry, and strength in mice. Calcif Tissue Int 67: 337344.
  • 52
    Styrkarsdottir U, Cazier JB, Kong A, Rolfsson O, Larsen H, Bjarnadottir E, Johannsdottir VD, Sigurdardottir MS, Bagger Y, Christiansen C, Reynisdottir I, Grant SF, Jonasson K, Frigge ML, Gulcher JR, Sigurdsson G, Stefansson K 2003 Linkage of Osteoporosis to Chromosome 20p12 and Association to BMP2. PLoS Biol 1: 351360.
  • 53
    Aubin JE, Bonnelye E 2000 Osteoprotegerin and its ligand: A new paradigm for regulation of osteoclastogenesis and bone resorption. Osteoporos Int 11: 905913.
  • 54
    Hofbauer LC, Khosla S, Dunstan CR, Lacey DL, Boyle WJ, Riggs BL 2000 The roles of osteoprotegerin and osteoprotegerin ligand in the paracrine regulation of bone resorption. J Bone Miner Res 15: 212.
  • 55
    Mezquita-Raya P, de la Higuera M, Garcia DF, Alonso G, Ruiz-Requena ME, de Dios Luna J, Escobar-Jimenez F, Munoz-Torres M 2005 The contribution of serum osteoprotegerin to bone mass and vertebral fractures in postmenopausal women. Osteoporos Int (in press).
  • 56
    Langdahl BL, Carstens M, Stenkjaer L, Eriksen EF 2002 Polymorphisms in the osteoprotegerin gene are associated with osteoporotic fractures. J Bone Miner Res 17: 12451255.
  • 57
    Kaptoge S, Dalzell N, Loveridge N, Beck TJ, Khaw KT, Reeve J 2003 Effects of gender, anthropometric variables, and aging on the evolution of hip strength in men and women aged over 65. Bone 32: 561570.
  • 58
    Beck TJ, Ruff CB, Scott WW Jr, Plato CC, Tobin JD, Quan CA 1992 Sex differences in geometry of the femoral neck with aging: A structural analysis of bone mineral data. Calcif Tissue Int 50: 2429.
  • 59
    Deng HW, Shen H, Xu FH, Deng H, Conway T, Liu YJ, Liu YZ, Li JL, Huang QY, Davies KM, Recker RR 2003 Several genomic regions potentially containing QTLs for bone size variation were identified in a whole-genome linkage scan. Am J Med Genet 119: 121131.
  • 60
    Huang QY, Xu FH, Shen H, Deng HY, Conway T, Liu YJ, Liu YZ, Li JL, Li MX, Davies KM, Recker RR, Deng HW 2004 Genome scan for QTLs underlying bone size variation at 10 refined skeletal sites: Genetic heterogeneity and the significance of phenotype refinement. Physiol Genomics 17: 326331.
  • 61
    Sammalisto SA, Hiekkalinna T, Suviolahti E, Sood K, Metzidis A, Pajukanta P, Lilja HE, Soro-Paavonen A, Taskinen MR, Tuomi T, Almgren P, Orho-Melander M, Groop L, Peltonen L, Perola M 2005 A male-specific QTL on 1p21 controlling human stature. J Med Genet.
  • 62
    Willemsen G, Boomsma DI, Beem AL, Vink JM, Slagboom PE, Posthuma D 2004 QTLs for height: Results of a full genome scan in Dutch sibling pairs. Eur J Hum Genet 12: 820828.
  • 63
    Geller F, Dempfle A, Gorg T 2003 Genome scan for body mass index and height in the Framingham Heart Study. BMC Genet 4 (Suppl 1): S91.
  • 64
    Mukhopadhyay N, Weeks DE 2003 Linkage analysis of adult height with parent-of-origin effects in the Framingham Heart Study. BMC Genet 4 (Suppl 1): S76.
  • 65
    Beck SR, Brown WM, Williams AH, Pierce J, Rich SS, Langefeld CD 2003 Age-stratified QTL genome scan analyses for anthropometric measures. BMC Genet 4 (Suppl 1): S31.
  • 66
    Xu J, Bleecker ER, Jongepier H, Howard TD, Koppelman GH, Postma DS, Meyers DA 2002 Major recessive gene(s) with considerable residual polygenic effect regulating adult height: Confirmation of genomewide scan results for chromosomes 6, 9, and 12. Am J Hum Genet 71: 646650.
  • 67
    Liu YZ, Xu FH, Shen H, Liu YJ, Zhao LJ, Long JR, Zhang YY, Xiao P, Xiong DH, Dvornyk V, Li JL, Conway T, Davies KM, Recker RR, Deng HW 2004 Genetic dissection of human stature in a large sample of multiplex pedigrees. Ann Hum Genet 68: 472488.
  • 68
    Wiltshire S, Frayling TM, Hattersley AT, Hitman GA, Walker M, Levy JC, O'Rahilly S, Groves CJ, Menzel S, Cardon LR, McCarthy MI 2002 Evidence for linkage of stature to chromosome 3p26 in a large U.K. Family data set ascertained for type 2 diabetes. Am J Hum Genet 70: 543546.
  • 69
    Deng HW, Xu FH, Liu YZ, Shen H, Deng H, Huang QY, Liu YJ, Conway T, Li JL, Davies KM, Recker RR 2002 A whole-genome linkage scan suggests several genomic regions potentially containing QTLs underlying the variation of stature. Am J Med Genet 113: 2939.
  • 70
    Perola M, Ohman M, Hiekkalinna T, Leppavuori 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: 117123.
  • 71
    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: 106116.
  • 72
    Ahlborg HG, Johnell O, Turner CH, Rannevik G, Karlsson MK 2003 Bone loss and bone size after menopause. N Engl J Med 349: 327334.
  • 73
    Melton LJ III, Beck TJ, Amin S, Khosla S, Achenbach SJ, Oberg AL, Riggs BL 2005 Contributions of bone density and structure to fracture risk assessment in men and women. Osteoporos Int 16: 460467.
  • 74
    Moffett SP, Zmuda JM, Oakley JI, Beck TJ, Cauley JA, Stone KL, Lui LY, Ensrud KE, Hillier TA, Hochberg MC, Morin P, Peltz G, Greene D, Cummings SR 2005 Tumor necrosis factor alpha polymorphism, bone strength phenotypes, and the risk of fracture in older women. J Clin Endocrinol Metab 90: 34913497.
  • 75
    Heaney RP, Barger-Lux MJ, Davies KM, Ryan RA, Johnson ML, Gong G 1997 Bone dimensional change with age: Interactions of genetic, hormonal, and body size variables. Osteoporos Int 7: 426431.
  • 76
    Allen M, Heinzmann A, Noguchi E, Abecasis G, Broxholme J, Ponting CP, Bhattacharyya S, Tinsley J, Zhang Y, Holt R, Jones EY, Lench N, Carey A, Jones H, Dickens NJ, Dimon C, Nicholls R, Baker C, Xue L, Townsend E, Kabesch M, Weiland SK, Carr D, von Mutius E, Adcock IM, Barnes PJ, Lathrop GM, Edwards M, Moffatt MF, Cookson WO 2003 Positional cloning of a novel gene influencing asthma from chromosome 2q14. Nat Genet 35: 258263.
  • 77
    Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner TH, Mashima H, Schwarz PE, Bosque-Plata L, Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Concannon P, Iwasaki N, Schulze J, Baier LJ, Bogardus C, Groop L, Boerwinkle E, Hanis CL, Bell GI 2000 Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 26: 163175.
  • 78
    Zhang Y, Leaves NI, Anderson GG, Ponting CP, Broxholme J, Holt R, Edser P, Bhattacharyya S, Dunham A, Adcock IM, Pulleyn L, Barnes PJ, Harper JI, Abecasis G, Cardon L, White M, Burton J, Matthews L, Mott R, Ross M, Cox R, Moffatt MF, Cookson WO 2003 Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nat Genet 34: 181186.
  • 79
    Klein RF, Allard J, Avnur Z, Nikolcheva T, Rotstein D, Carlos AS, Shea M, Waters RV, Belknap JK, Peltz G, Orwoll ES 2004 Regulation of bone mass in mice by the lipoxygenase gene Alox15. Science 303: 229232.
  • 80
    Dvornyk V, Xiao P, Liu YJ, Shen H, Deng HW 2004 Systemic approach to the study of complex bone disorders at the whole-genome level. Curr Genomics 5: 93108.