Skip Navigation


Human Molecular Genetics Advance Access originally published online on March 3, 2005
Human Molecular Genetics 2005 14(7):943-951; doi:10.1093/hmg/ddi088
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
14/7/943    most recent
ddi088v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (45)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ralston, S. H.
Right arrow Articles by Bennett, S. T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ralston, S. H.
Right arrow Articles by Bennett, S. T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions{at}oupjournals.org

Loci for regulation of bone mineral density in men and women identified by genome wide linkage scan: the FAMOS study

Stuart H. Ralston1,*, Nick Galwey2, Ian MacKay2, Omar M.E. Albagha3, Lon Cardon4, Juliet E. Compston5, Cyrus Cooper6, Emma Duncan7, Richard Keen8, Bente Langdahl9, Alastair McLellan10, Jeffrey O'Riordan8, Huibert A. Pols11, David M. Reid3, Andre G. Uitterlinden11, John Wass7 and Simon T. Bennett2

1Rheumatic Diseases Unit, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK, 2Oxagen Ltd, 91 Milton Park, Abingdon OX14 4RY, UK, 3Bone Research Group, Institute of Medical Sciences, University of Aberdeen Medical School, Aberdeen AB25 2ZD, UK, 4Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK, 5University of Cambridge School of Clinical Medicine, Cambridge CB2 2QQ, UK, 6MRC Environmental Epidemiology Unit, Southampton General Hospital, Southampton SO16 6YD, UK, 7Department of Endocrinology, Oxford Centre for Diabetes, Endocrinology and Metabolism, The Churchill Hospital, Oxford, UK, 8University College London, HA4 4LP, UK, 9Department of Endocrinology, A°rhus Amtssygehus, A°rhus, Denmark, 10Department of Medicine and Therapeutics, Western Infirmary, Glasgow G11 6NT, UK and 11Department of Internal Medicine, Erasmus Medical Centre, PO Box 1738, 3000DR Rotterdam, The Netherlands

* To whom correspondence should be addressed at: Rheumatic Diseases Unit, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. Tel: +44 1315371088; Fax: +44 1315371051; Email: stuart.ralston{at}ed.ac.uk

Received January 16, 2005; Accepted February 14, 2005


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Osteoporosis is a common disease with a strong genetic component, characterized by reduced bone mass and an increased risk of fracture. Bone mineral density (BMD) is a highly heritable trait and a key determinant of osteoporotic fracture risk, but the genes responsible are incompletely defined. Here, we identified quantitative trait loci (QTL) for regulation of BMD by a genome wide scan involving 3691 individuals from 715 families, who were selected because of reduced BMD values at the lumbar spine (LS-BMD) or femoral neck (FN-BMD) in probands. Linkage analysis was conducted in the study group as a whole with correction for age, gender, weight and height. Further analyses were conducted for men and women separately to identify gender-specific QTL and for those under and over the age of 50 years to distinguish QTL for peak bone mass from those that influence bone mass in older people. No regions of suggestive or significant linkage were identified when data from all subjects were analyzed together. On subgroup analysis, however, we identified a significant QTL for FN-BMD on chromosome 10q21 (LOD score +4.42; men ≤50 years) and two suggestive QTL for LS-BMD on chromosomes 18p11 (LOD score +2.83; women >50 years) and 20q13 (LOD score +3.20; women ≤50 years). We identified five other QTL for BMD with LOD scores of greater than +2.20 on chromosomes 3q25, 4q25, 7p14, 16p13 and 16q23. This study provides evidence for gender-specific, site-specific and age-specific QTL, which regulate BMD in humans, and illustrates the importance of conducting subgroup analysis to detect these loci.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Osteoporosis is a common disease with a strong genetic component, characterized by reduced bone mass and an increased risk of fragility fractures. Bone mineral density (BMD) is one of the most important clinical predictors of osteoporotic fracture risk, and evidence from twin and family studies suggests that between 50 and 85% of the variance in BMD is genetically determined (1Go–3Go). Polymorphisms have been identified in many candidate genes that are associated with BMD or osteoporotic fractures, but these account for only a small proportion of the genetic contribution to BMD regulation and the susceptibility to osteoporotic fractures (4Go–6Go).

Several genome wide linkage scans have been performed to try and identify quantitative trait loci (QTL) that regulate BMD. A variety of study designs have been used including analysis of families with a history of osteoporosis (7Go–10Go) families drawn from the normal population (11Go,12Go), healthy sib-pairs (13Go–16Go) and dizygotic twins (14Go). Few of these studies have identified QTL which meet the criteria for genome wide significance, and only one gene which regulates susceptibility to osteoporosis has been identified by genome wide scan and positional cloning (10Go). Identification of the genes and QTL that regulate BMD in osteoporosis has been made difficult by factors such as genetic heterogeneity and the fact that the effect size of susceptibility genes is modest. There is also evidence to suggest that the genes that regulate BMD act in a gender-specific, age-specific and site-specific manner. For example, linkage studies using inbred strains of mice have shown that the loci which regulate BMD are gender-specific (17Go,18Go) and emerging data suggests that this is also true in humans (12Go,19Go–21Go). The loci that regulate peak bone mass have also been found to differ from those that regulate bone mass in older people (12Go,19Go), and it is clear that different loci are responsible for regulation of BMD at different skeletal sites (7Go,9Go,11Go,13Go,19Go). Relatively few linkage studies have attempted to separate the influences of age and gender on the regulation of BMD, either because of the fact that only women were studied or because of limited statistical power due to small sample size (7Go,9Go,12Go,19Go). Here, we report the results of a large-scale genome wide linkage scan for BMD in 3691 individuals from 715 families in which we used subgroup analysis to identify gender-specific, age-specific and site-specific loci for the regulation of BMD.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Relevant demographic details of the study subjects and their site of recruitment are shown in Table 1. Most subjects were recruited from centers in the UK (63%), with 19% from Denmark and 18% from the Netherlands. The median age of participants was 50 years with a range of 19–96 years. There was no significant difference between centers in age, BMD values or gender distribution of participants. Heritability estimates of LS-BMD, with adjustment for ascertainment, as estimated by SOLAR, were (mean±SE) 0.55±0.05 in women and 0.72±0.07 in men. Corresponding values for FN-BMD were 0.66±0.05 in women and 0.63±0.07 in men.


View this table:
[in this window]
[in a new window]
  		Table 1. Demographic details of study population
 
We initially performed linkage analysis for BMD at the spine and hip in the whole study population, but no regions of suggestive or significant linkage were identified, and the highest LOD score recorded was +2.00 for FN-BMD on chromosome 1 at 255 cM between the markers D1S2800 and D1S2850. In view of this, we conducted a subgroup analysis for men and women separately and for those aged under and over 50 years. Results of this analysis are shown in Figure 1 for men and in Figure 2 for women. We identified five QTL in men that showed suggestive or significant evidence of linkage (Fig. 1). These were on chromosome 3q25 for LS-BMD at 177 cM between the markers D3S1279 and D3S1565 (LOD score +2.43, men age ≤50), on chromosome 4q25 for FN-BMD at 117 cM between the markers D4S1572 and D4S406 (LOD score +2.22, men age ≤50), on chromosome 7p14 for FN-BMD at 57 cM between the markers D7S516 and D7S510 (LOD score +2.28, men age >50), on chromosome 10q21 for FN-BMD at 80 cM between the markers D10S196 and D10S537 (LOD score +4.42, men age ≤50) and on chromosome 16p13 for FN-BMD at 31 cM between the markers D16S3075 and D16S261 (LOD score +2.52, men age ≤50). We identified four QTL in women that showed suggestive evidence of linkage (Fig. 2). These were on chromosome 4q25 for FN-BMD at 117 cM between the markers D4S1572 and D4S406 (LOD score +2.55, women age >50), on chromosome 16q23 for LS-BMD at 31 cM between the markers D16S3091 and D16S520 (LOD score +2.28, women age ≤50), on chromosome 18p11 for LS-BMD at 48 cM between the markers D18S53 and D18S478 (LOD score +2.83; women age >50) and on chromosome 20q13 for LS-BMD at 90 cM between the markers D20S196 and D20S173 (LOD score +3.20, women age ≤50). Figures 3 and 4 show detailed LOD score plots for LS-BMD and FN-BMD on chromosomes where QTL with LOD scores of greater than +2.2 were identified. The plots for men are shown in Figure 3 and for women in Figure 4. These figures illustrate that the loci identified for BMD regulation are largely gender specific, age-group specific and site specific. We identified one locus on chromosome 4q25 where we found evidence of suggestive linkage to FN-BMD in both men and women, although the linkage signal in men was for subjects aged ≤50 and in women was for subjects aged >50 (Figs 3B and 4A). This presumably explains why we did not detect a clear signal at chromosome 4q25 in the pooled analysis where the maximum LOD score in this region was only +0.60 at D4S1572.



View larger version (29K):
[in this window]
[in a new window]
  		Figure 1. QTL for lumbar spine and femoral neck BMD in men. Filled circles on the x-axis indicate boundaries between chromosomes. The interrupted horizontal lines indicate the thresholds for suggestive linkage (+2.2) and significant linkage (+3.6). LOD score plots for men aged ≤50 are shown in blue and those for men aged >50 are shown in red. FN-BMD is femoral neck BMD and LS-BMD is lumbar spine BMD.

 


View larger version (30K):
[in this window]
[in a new window]
  		Figure 2. QTL for lumbar spine and femoral neck BMD in women. Filled circles on the x-axis indicate boundaries between chromosomes. The interrupted horizontal lines indicate the thresholds for suggestive linkage (+2.2) and significant linkage (+3.6). LOD score plots for women aged ≤50 are shown in blue and those for women aged >50 are shown in red. FN-BMD is femoral neck BMD and LS-BMD is lumbar spine BMD.

 


View larger version (27K):
[in this window]
[in a new window]
  		Figure 3. LOD score plots for chromosomes containing QTL with LOD scores greater than +2.2 in men. The horizontal lines indicate the thresholds for suggestive linkage and significant linkage. The position of flanking markers closest to the LOD score peak is indicated. LOD score plots for lumbar spine BMD (LS-BMD) are shown in blue and those for femoral neck BMD (FN-BMD) are shown in red.

 


View larger version (22K):
[in this window]
[in a new window]
  		Figure 4. LOD score plots for chromosomes containing QTL with LOD scores greater than +2.2 in women. The horizontal lines indicate the thresholds for suggestive linkage and significant linkage. The position of flanking markers closest to the LOD score peak is indicated. LOD score plots for lumbar spine BMD (LS-BMD) are shown in blue and those for femoral neck BMD (FN-BMD) are shown in red.

 
We also calculated two-point LOD scores for markers adjacent to the regions where multipoint LOD scores exceeded +2.2. The results of this analysis showed reasonable concordance between the two-point and the multipoint analyses but with generally lower LOD scores in the two-point analysis (data not shown), due presumably to the fact that the two-point analysis is less informative because it does not take into account haplotype information from adjacent markers.

The results obtained in this study were compared with those of previous investigators who also conducted genome wide scans for BMD QTL (Table 2). This analysis was restricted to loci identified in previous studies where the LOD score was more than or equal to +1.7. Three regions were identified where LOD scores of more than or equal to +1.0 were observed in this study at loci identified by previous investigators and being potentially linked with BMD. For the chromosome 1q21, LS-BMD locus initially identified by Koller et al. (13Go) in pre-menopausal female sib pairs and subsequently confirmed by Econs et al. (16Go), we observed a LOD score of +1.11 for LS-BMD in women ≤50 years, which for all three studies combined would give a summated LOD score in excess of +5.0. For the FN-BMD locus identified by Kammerer et al. (12Go) on chromosome 3p24 in men, we recorded a LOD score of +1.13, which would give a summated LOD score of +2.94 in both studies. For the LS-BMD locus identified by Karasik et al. (11Go) on chromosome 14q31, we recorded a LOD score of +1.31 for LS-BMD, when men and women were analyzed together, giving a summated LOD score of +3.23 in both studies.


View this table:
[in this window]
[in a new window]
  		Table 2. Comparison of QTL for spine or hip BMD identified in previous genome wide scans with the present study
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
This is the largest family-based genome wide scan for BMD so far reported with a sample size of ~30% greater than the next largest study. Despite this, linkage analysis in the study group as a whole yielded disappointing results in that no regions of suggestive or significant linkage were identified. In view of this, we conducted genome wide scans for men and women separately in the light of previous studies, which suggest that there are important gender differences in the genetic regulation of BMD (12Go,19Go,20Go,22Go). We also subdivided subjects into two broad categories by age to explore the possibility that the loci which regulate peak bone mass may differ from those that regulate bone mass in older people. For this analysis, we chose an age cutoff of 50 years because this was the median age of participants in the study and is also close to the average age at menopause in women. As in previous genome wide linkage scans, we found little overlap between QTL for lumbar spine and femoral neck BMD, supporting the view that different genes regulate BMD at these different skeletal sites (7Go,11Go,13Go,14Go). We also found that there was limited overlap between QTL in men and women. This supports the findings that have previously been observed in mice (17Go) and humans (12Go,19Go) and demonstrates that for the most part, different genes regulate bone mass in different genders. We also found that different QTL were identified in subgroups of individuals older and younger than 50 years. This suggests that different genes may be responsible for regulating peak bone mass and bone loss, although it should be acknowledged that we did not measure bone loss directly in this study.

None of the major QTL identified in this study overlapped with major QTL identified by previous investigators who have conducted genome wide scans for BMD. Despite this, we found positive LOD scores in three QTL identified by previous workers. These were in the 1q21 locus for LS-BMD identified by Koller et al., (13Go) where we observed a LOD score for LS-BMD of +1.11 at D1S484 in women <50 years, which coincides exactly with the position of the LOD score peak previously reported. This lends support to the hypothesis that there may be an important gene on chromosome 1q21, which regulates peak bone mass in women. We also observed a LOD score of +1.31 for LS-BMD at 56 cM on chromosome 14q31, when all subjects were analyzed together, coinciding with a linkage peak of +1.92 in the same chromosomal region identified by Karasik et al. (11Go). Finally, we observed a LOD score of +1.13 at 55 cM on chromosome 3p24 in men, coinciding with the linkage peak of +1.81 identified at this locus by Kammerer et al. (12Go) in Mexican-American men. The fact that we failed to replicate other QTL identified previously could be due to a number of factors including differences in study design and selection of probands; differences in environmental interactions, and the possibility that different genes regulate BMD in different human populations. In this study, we used a family-based design, with selection of probands on the basis of low BMD, broadly similar to the approaches used by Deng et al. (9Go), Devoto et al. (7Go) and Styrkarsdottir et al. (10Go). The BMD cutoff for proband enrollment in this study was more stringent than those in the family-based studies cited previously, which could have resulted in enrichment for genes that predispose to severe osteoporosis. It is of interest that the QTL on chromosomes 3q25 and 16p13 which we identified are syntenic with those identified as regulators of BMD in mice (23Go–25Go). Although this may indicate the presence of shared genes that regulate BMD across species, caution need to be exercised in interpreting the data in light of the fact that BMD QTL have now been identified in mice on almost all chromosomes (26Go).

The findings reported in this study need to be interpreted with caution in view of the subgroup analysis we performed. Application of a Bonferroni correction for the number of analyses conducted increases the accepted LOD score thresholds (27Go) for suggestive linkage from +2.2 to +2.8 and those for significant linkage from +3.6 to +4.1. According to these criteria, we identified one QTL where the LOD score was above the threshold for significant linkage and two QTL where the LOD score exceeded the threshold for suggestive linkage. However, another six QTL were identified where the LOD scores were in excess of +2.2, and it is likely that some of these regions contain genes that regulate BMD, because the analyses we performed were not independent and so the true thresholds for significance probably lie somewhere between the corrected and uncorrected thresholds for genome wide significance.

Some interesting positional candidate genes are present within the 10p21 and 20q13 loci which we identified. For example, the chromosome 20q13 locus contains the GNAS1 gene, which is involved in the pathogenesis of both pseudohypoparathyroidism and Albright's hereditary osteodystrophy. This is an interesting candidate gene in light of the fact that it is imprinted resulting in different phenotypes depending on the parental origin of the mutation (28Go). A strong candidate gene within the 20q13 locus is BMP-7, which is known to promote bone formation in vivo and osteoblast differentiation in vitro. The chromosome 10q21 locus contains another strong candidate gene in the form of DKK1 which encodes the Dickkopf protein. This is an antagonist of wnt signaling, which is relevant in light of the recent data, which has shown that the wnt-LRP5 pathway plays an important role in bone formation and regulation of bone mass (29Go–31Go).

In summary, this study provides evidence to show that the genes which regulate BMD do so in a gender-specific, site-specific and age-specific manner. An important finding to emerge from our study was that the loci which we identified only became apparent when we conducted a subgroup analysis with conditioning by gender and age group. This has also been reported by previous workers (12Go,19Go) and shows that inclusion of gender and age into the linkage model is less powerful than subgroup analysis in identifying gender-specific and age-specific effects. Also taking account of the fact that several analyses were conducted, we identified two loci where the LOD scores exceeded the threshold for suggestive linkage and one locus where the LOD score exceeded the threshold for significant linkage, and further studies are now warranted to identify the genes responsible for BMD regulation in these regions.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Subjects
Participants were recruited from eight European referral centers for investigation and management of osteoporosis between 1999 and 2001. The centers were in Aarhus (Denmark), Aberdeen (UK), Cambridge (UK), Glasgow (UK), London (UK), Oxford (UK), Rotterdam (Netherlands) and Southampton (UK). The study protocol was approved by the Multicentre Research Ethics Committee in the UK and by the local research ethics committees of all participating centers. Subjects were recruited from a combination of clinical referrals and BMD screening programs in different centers and all subjects enrolled gave informed written consent. We recruited subjects into the study on the basis that the probands were between 18 and 90 years of age with a BMD Z-score value of less than –2.0 or at the spine or hip, which approximates to the lowest 2.5% of the population distribution of BMD values. This method of selection was used because it is believed to have greater statistical power than that of random sampling (32Go). All consenting individuals from the immediate family were enrolled, irrespective of their BMD values. We also extended recruitment beyond the immediate family of each proband by enrolling relatives of family members whose BMD Z-score values were less than –1.28 (approximating to the lowest 10% of the population distribution). All subjects completed a nurse-administered questionnaire on medical history and drug treatment and underwent a routine biochemistry and hematology screen. Subjects were excluded if they had an early menopause (age <45 years), prolonged amenorrhea (>5 years), a history of thyroid disease with suppressed TSH values at the time of treatment, evidence of chronic liver disease (AST levels more than twice the upper limit of normal), evidence of chronic renal disease (serum creatinine levels more than twice the upper limit of normal), a history of malignant disease (except localized basal carcinoma of the skin), chronic inflammatory disease or malabsorption. Subjects who had received HRT and other bone active drugs, such as bisphosphonates, were eligible for inclusion in the study, but in these subjects, BMD values used in the linkage analysis were those taken before treatment had been commenced. All of the study subjects were Caucasians of European descent. The final study group comprised 715 pedigrees with 3691 subjects. However, BMD measurements were not obtained in 33 subjects resulting in a final study group of 3658 subjects [1398 (38%) men and 2260 (62%) women]. The pedigrees ranged in size from three to 50 subjects (average 8.06) and from two to five (average 2.85) generations.

BMD measurements
BMD measurements were made at the lumbar spine [mean of lumbar vertebrae 2, 3 and 4 (LS-BMD)] and the femoral neck (FN-BMD) by dual energy X-ray absorptiometry (DXA) using one of the following types of scanner: Hologic QDR1000, Hologic QDR4500, Lunar Expert, Lunar DPX or DPXL and Norland XR26 or XR36. These DXA machines were cross-calibrated using the European Spine Phantom (33Go), and the method of calibration was validated in a collection of 991 female non-proband participants of the study (manuscript in preparation). Corrected BMD values were adjusted for age and sex, using data from the study population to generate Z-scores at the lumbar spine (L2–L4) and the femoral neck. The correction for age was modeled by a non-linear curve fitted to the calibrated data from the study population.

DNA extraction and genotyping
Venous blood (50 ml) was obtained from each subject and DNA was extracted by standard techniques. Genotyping was performed using a panel of fluorescent labeled markers distributed at ~10 cM intervals throughout the genome (ABI PRISM Linkage Mapping Set MD10 version 2). Approximately 1.4 million genotypes were performed, which passed PCR quality control, but 2.21% of these were discarded for technical reasons (e.g. low intensity, contamination) or because they did not conform to Mendelian inheritance as determined by the PedCheck software package (34Go) or because they were reported to depend on a double recombination event with a probability of P<0.005 using MERLIN (35Go) or SimWalk2 software (36Go).

Comparison with results of other genome wide scans
We compared LOD scores found in the present study with the results of previous genome wide scans for hip or spine BMD (7Go,9Go,11Go–14Go). To provide an appropriate comparison between the studies, the LOD scores presented in this analysis were matched for skeletal site, sex and age group. For example, when comparing the results with those of previous studies in which pre-menopausal women had been analyzed, the LOD scores from this study presented are those in women <50 years. When comparing the results with those of previous studies, which had analyzed men and women together over a range of ages, we also calculated LOD scores for men and women analyzed together in the whole study population. Where necessary, LOD scores for total hip BMD and Wards triangle and trochanter BMD were also calculated to provide site-specific comparisons.

Statistical analyses
At the outset of the study, our intention was to analyze the genome wide scan data from the whole study population for spine BMD and femoral neck BMD and to correct for the effects of age and sex, by entering these variables into the statistical model. By the time that the genome wide scan had been completed, however, it had become clear that there were sex-specific and age-specific determinants of BMD that could not be adequately captured by this approach (12Go,17Go–19Go). Because of this, we analyzed the genotype data conditioned by gender and age band (greater and less than 50), as well as in the study group as a whole. We made a Bonferroni adjustment on the basis of the assumption that we had conducted four independent genome wide searches for BMD at each skeletal site and under this scenario, the adjusted LOD scores increased from +2.2 to +2.8 for suggestive linkage and from +3.6 to +4.1 for significant linkage. Linkage analysis was carried out by the variance components methodology using SOLAR (37Go) and with a regression-based method of linkage analysis, implemented in the software MERLIN (35Go). Multipoint LOD scores on the X-chromosome were calculated using the software minx (MERLIN for X) (35Go). For these analyses, the LOD scores were adjusted for proband ascertainment, age, height, weight and height multiplied by weight. For the gender-specific and age-group specific analyses, all available genotyping data were used to assign identity-by-descent status. The gender-specific and age-specific linkage results were obtained by setting the BMD values for subjects who did not fall into the category of interest as missing values. Linkage analysis in the study group as a whole included the covariates mentioned previously, as well as gender. We excluded nine outlying, unusually high BMD values from the SOLAR analysis because they deviated by six or more standard deviations from the population mean. These outliers violate the assumption of normality that is made using variance components methodology and caused major discrepancies between the linkage peaks obtained using SOLAR and MERLIN. When the outliers were excluded, both methods gave almost identical results (data not shown). The data shown in the paper are from the SOLAR analysis with the exception of the X-chromosome data, which is from the MINX analysis. Simulation studies were conducted using a variance components model function of the QTDT software program to assess the statistical power of the study population to detect linkage. For these simulations, a model was constructed assuming an additive QTL with frequency 0.5, located 5 cM from the nearest microsatellite marker over a range of heritability estimates. For each scenario, we carried out 1000 simulations using the exact pattern of missing data that was observed in our data set and making the assumption of fully informative identity by descent. This analysis showed that the study had 98% power to detect a LOD score of +3.3 for QTL of major effect, accounting for 30% of the heritability, and had ~57% power to detect a LOD score of +3.3 for a QTL accounting for 20% of the heritability.


   ACKNOWLEDGEMENTS
 
The authors wish to thank the families who participated in the study; Dr Jon Mangion for helpful discussions and all staff at Oxagen that contributed to the collection, preparation and management of the genotype and phenotype data. The study was supported partly by an ICAC grant to D.M.R. and S.H.R. from the Arthritis Research Campaign (R0544) a co-operative group grant to S.H.R. from the Medical Research Council (G9823281); and a grant from the European Commission to A.G.U., S.H.R., B.L., H.A.P., D.M.R. and Oxagen (QLRT-2001-02621). O.M.E.A. is supported by a grant from the Arthritis Research Campaign (R0592). This study was funded by Oxagen. I.M. is employed by Oxagen and L.C. acts as consultant to Oxagen. N.G. and S.T.B. were employees of Oxagen at the time this study was performed.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Gueguen, R., Jouanny, P., Guillemin, F., Kuntz, C., Pourel, J. and Siest, G. (1995) Segregation analysis and variance components analysis of bone mineral density in healthy families. J. Bone Miner. Res., 12, 2017–2022.

  2. Arden, N.K. and Spector, T.D. (1997) Genetic influences on muscle strength, lean body mass, and bone mineral density: a twin study. J. Bone Miner. Res., 12, 2076–2081.[CrossRef][Web of Science][Medline]

  3. Smith, D.M., Nance, W.E., Kang, K.W., Christian, J.C. and Johnston, C.C. (1973) Genetic factors in determining bone mass. J. Clin. Invest., 52, 2800–2808.[Web of Science][Medline]

  4. Rubin, L.A., Hawker, G.A., Peltekova, V.D., Fielding, L.J., Ridout, R. and Cole, D.E. (1999) Determinants of peak bone mass: clinical and genetic analyses in a young female Canadian cohort. J. Bone Miner. Res., 14, 633–643.[CrossRef][Web of Science][Medline]

  5. McGuigan, F.E., Murray, L., Gallagher, A., Davey-Smith, G., Neville, C.E., van't Hof, R.J., Boreham, C. and Ralston, S.H. (2002) Genetic and environmental determinants of peak bone mass in young men and women. J. Bone Miner. Res., 17, 1273–1279.[CrossRef][Web of Science][Medline]

  6. Ralston, S.H. (2002) Genetic control of susceptibility to osteoporosis. J. Clin. Endocrinol. Metab, 87, 2460–2466.[Abstract/Free Full Text]

  7. Devoto, M., Shimoya, K., Caminis, J., Ott, J., Tenenhouse, A., Whyte, M.P., Sereda, L., Hall, S., Considine, E., Williams, C.J.et al. (1998) First-stage autosomal genome screen in extended pedigrees suggests genes predisposing to low bone mineral density on chromosomes 1p, 2p and 4q. Eur. J. Hum. Genet., 6, 151–157.[CrossRef][Web of Science][Medline]

  8. Devoto, M., Specchia, C., Li, H.H., Caminis, J., Tenenhouse, A., Rodriguez, H. and Spotila, L.D. (2001) Variance component linkage analysis indicates a QTL for femoral neck bone mineral density on chromosome 1p36. Hum. Mol. Genet., 10, 2447–2452.[Abstract/Free Full Text]

  9. Deng, H.W., Xu, F.H., Huang, Q.Y., Shen, H., Deng, H., Conway, T., Liu, Y.J., Liu, Y.Z., Li, J.L., Zhang, H.T., Davies, K.M. and Recker, R.R. (2002) A whole-genome linkage scan suggests several genomic regions potentially containing quantitative trait loci for osteoporosis. J. Clin. Endocrinol. Metab., 87, 5151–5159.[Abstract/Free Full Text]

  10. Styrkarsdottir, U., Cazier, J.-B., Kong, A., Rolfsson, O., Larsen, H., Bjarnadottir, E., Johannsdottir, V.D., Sigurdardottir, M.S., Bagger, Y., Christiansen, C. et al. (2003) Linkage of osteoporosis to chromosome 20p12 and association to BMP2. PLoS. Biol., 1, 1–10.

  11. Karasik, D., Myers, R.H., Cupples, L.A., Hannan, M.T., Gagnon, D.R., Herbert, A. and Kiel, D.P. (2002) Genome screen for quantitative trait loci contributing to normal variation in bone mineral density: the Framingham Study. J. Bone Miner. Res., 17, 1718–1727.[CrossRef][Web of Science][Medline]

  12. Kammerer, C.M., Schneider, J.L., Cole, S.A., Hixson, J.E., Samollow, P.B., O'Connell, J.R., Perez, R., Dyer, T.D., Almasy, L., Blangero, J., Bauer, R.L. and Mitchell, B.D. (2003) Quantitative trait loci on chromosomes 2p, 4p, and 13q influence bone mineral density of the forearm and hip in Mexican Americans. J. Bone Miner. Res., 18, 2245–2252.[CrossRef][Web of Science][Medline]

  13. Koller, D.L., Econs, M.J., Morin, P.A., Christian, J.C., Hui, S.L., Parry, P., Curran, M.E., Rodriguez, L.A., Conneally, P.M., Joslyn, G. et al. (2000) Genome screen for QTLs contributing to normal variation in bone mineral density and osteoporosis. J. Clin. Endocrinol. Metab., 85, 3116–3120.[Abstract/Free Full Text]

  14. Wilson, S.G., Reed, P.W., Bansal, A., Chiano, M., Lindersson, M., Langdown, M., Prince, R.L., Thompson, D., Thompson, E., Bailey, M. et al. (2003) Comparison of genome screens for two independent cohorts provides replication of suggestive linkage of bone mineral density to 3p21 and 1p36. Am. J. Hum. Genet., 72, 144–155.[CrossRef][Web of Science][Medline]

  15. Nui, T., Chen, C., Cordell, H., Yang, J., Wang, B., Wang, Z., Fang, Z., Schork, N.J., Rosen, C.J. and Xu, X. (1999) A genome-wide scan for loci linked to forearm bone mineral density. Hum. Genet., 104, 226–233.[CrossRef][Web of Science][Medline]

  16. Econs, M.J., Koller, D.L., Hui, S.L., Fishburn, T., Conneally, P.M., Johnston, C.C., Jr, Peacock, M. and Foroud, T.M. (2004) Confirmation of linkage to chromosome 1q for peak vertebral bone mineral density in premenopausal white women. Am. J. Hum. Genet., 74, 223–228.[CrossRef][Web of Science][Medline]

  17. Orwoll, E.S., Belknap, J.K. and Klein, R.F. (2001) Gender specificity in the genetic determinants of peak bone mass. J. Bone Miner. Res., 16, 1962–1971.[CrossRef][Web of Science][Medline]

  18. Turner, C.H., Sun, Q., Schriefer, J., Pitner, N., Price, R., Bouxsein, M.L., Rosen, C.J., Donahue, L.R., Shultz, K.L. and Beamer, W.G. (2003) Congenic mice reveal sex-specific genetic regulation of femoral structure and strength. Calcif. Tissue Int., 73, 297–303.[CrossRef][Web of Science][Medline]

  19. Karasik, D., Cupples, L.A., Hannan, M.T. and Kiel, D.P. (2003) Age, gender, and body mass effects on quantitative trait loci for bone mineral density: the Framingham Study. Bone, 33, 308–316.[Medline]

  20. Duncan, E.L., Cardon, L.R., Sinsheimer, J.S., Wass, J.A. and Brown, M.A. (2003) Site and gender specificity of inheritance of bone mineral density. J. Bone Miner. Res., 18, 1531–1538.[CrossRef][Web of Science][Medline]

  21. Naganathan, V., Macgregor, A., Snieder, H., Nguyen, T., Spector, T. and Sambrook, P. (2002) Gender differences in the genetic factors responsible for variation in bone density and ultrasound. J. Bone Miner. Res., 17, 725–733.[CrossRef][Web of Science][Medline]

  22. Naganathan, V. and Sambrook, P. (2003) Gender differences in volumetric bone density: a study of opposite-sex twins. Osteoporos. Int., 14, 564–569.[CrossRef][Web of Science][Medline]

  23. Beamer, W.G., Shultz, K.L., Donahue, L.R., Churchill, G.A., Sen, S., Wergedal, J.R., Baylink, D.J. and Rosen, C.J. (2001) Quantitative trait loci for femoral and lumbar vertebral bone mineral density in C57BL/6J and C3H/HeJ inbred strains of mice. J. Bone Miner. Res., 16, 1195–1206.[CrossRef][Web of Science][Medline]

  24. Klein, R.F., Mitchell, S.R., Phillips, T.J., Belknap, J.K. and Orwoll, E.S. (1998) Quantitative trait loci affecting peak bone mineral density in mice. J. Bone Miner. Res., 13, 1648–1656.[CrossRef][Web of Science][Medline]

  25. Drake, T.A., Hannani, K., Kabo, J.M., Villa, V., Krass, K. and Lusis, A.J. (2001) Genetic loci influencing natural variations in femoral bone morphometry in mice. J. Orthop. Res., 19, 511–517.[CrossRef][Web of Science][Medline]

  26. Liu, Y.Z., Liu, Y.J., Recker, R.R. and Deng, H.W. (2003) Molecular studies of identification of genes for osteoporosis: the 2002 update. J. Endocrinol., 177, 147–196.[Abstract]

  27. Lander, E.S. and Kruglyak, L. (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet., 11, 241–247.[CrossRef][Web of Science][Medline]

  28. Levine, M.A., Germain-Lee, E. and de Beur, J. (2003) Genetic basis for resistance to parathyroid hormone. Horm. Res, 60 (Suppl. 2), 87–95.

  29. Kato, M., Patel, M.S., Levasseur, R., Lobov, I., Chang, B.H., Glass, D.A., Hartmann, C., Li, L., Hwang, T.H., Brayton, C.F. et al. (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.[Abstract/Free Full Text]

  30. Van Wesenbeeck, L., Cleiren, E., Gram, J., Beals, R.K., Benichou, O., Scopelliti, D., Key, L., Renton, T., Bartels, C., Gong, Y. et al. (2003) Six novel missense mutations in the LDL receptor-related protein 5 (LRP5) gene in different conditions with an increased bone density. Am. J. Hum.Genet., 72, 763–771.[CrossRef][Web of Science][Medline]

  31. Gong, Y., Slee, R.B., Fukai, N., Rawadi, G., Roman-Roman, S., Reginato, A.M., Wang, H., Cundy, T., Glorieux, F.H., Lev, D., Zacharin, M., Oexle, K. et al. (2001) LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell, 107, 513–523.[CrossRef][Web of Science][Medline]

  32. Risch, N. and Zhang, H. (1995) Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science, 268, 1584–1589.[Abstract/Free Full Text]

  33. Pearson, J., Dequeker, J., Henley, M., Bright, J., Reeve, J., Kalender, W., Laval-Jeantet, A.M., Ruegsegger, P., Felsenberg, D. and Adams, J. (1995) European semi-anthropomorphic spine phantom for the calibration of bone densitometers: assessment of precision, stability and accuracy. The European Quantitation of Osteoporosis Study Group. Osteoporos. Int., 5, 174–184.[CrossRef][Web of Science][Medline]

  34. O'Connell, J.R. and Weeks, D.E. (1998) PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am. J. Hum.Genet., 63, 259–266.[CrossRef][Web of Science][Medline]

  35. Abecasis, G.R., Cherny, S.S., Cookson, W.O. and Cardon, L.R. (2002) Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet., 30, 97–101.[CrossRef][Web of Science][Medline]

  36. Sobel, E. and Lange, K. (1996) Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics. Am. J. Hum. Genet., 58, 1323–1337.[Web of Science][Medline]

  37. Almasy, L. and Blangero, J. (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet., 62, 1198–1211.[CrossRef][Web of Science][Medline]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Hum Mol GenetHome page
C.-L. Cheung, B. Y.Y. Chan, V. Chan, S. Ikegawa, I. Kou, H. Ngai, D. Smith, K. D.K. Luk, Q.-Y. Huang, S. Mori, et al.
Pre-B-cell leukemia homeobox 1 (PBX1) shows functional and possible genetic association with bone mineral density variation
Hum. Mol. Genet., February 15, 2009; 18(4): 679 - 687.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
K. E. Govoni, L. R. Donahue, C. Marden, and S. Mohan
Complex genetic regulation of bone mineral density and insulin-like growth factor-I in C57BL/6J-Chr #A/J/NaJ chromosome substitution strains
Physiol Genomics, October 8, 2008; 35(2): 159 - 164.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
I. Alam, Q. Sun, L. Liu, D. L. Koller, Y. Liu, H. J. Edenberg, M. J. Econs, T. Foroud, and C. H. Turner
Genomic expression analysis of rat chromosome 4 for skeletal traits at femoral neck
Physiol Genomics, October 8, 2008; 35(2): 191 - 196.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Endocrinol. Metab.Home page
J.-M. Kaufman, A. Ostertag, A. Saint-Pierre, M. Cohen-Solal, A. Boland, I. Van Pottelbergh, K. Toye, M.-C. de Vernejoul, and M. Martinez
Genome-Wide Linkage Screen of Bone Mineral Density (BMD) in European Pedigrees Ascertained through a Male Relative with Low BMD Values: Evidence for Quantitative Trait Loci on 17q21-23, 11q12-13, 13q12-14, and 22q11
J. Clin. Endocrinol. Metab., October 1, 2008; 93(10): 3755 - 3762.
[Abstract] [Full Text] [PDF]

Home page
 JAMAHome page
J. B. J. van Meurs, T. A. Trikalinos, S. H. Ralston, S. Balcells, M. L. Brandi, K. Brixen, D. P. Kiel, B. L. Langdahl, P. Lips, O. Ljunggren, et al.
Large-Scale Analysis of Association Between LRP5 and LRP6 Variants and Osteoporosis
JAMA, March 19, 2008; 299(11): 1277 - 1290.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Endocrinol. Metab.Home page
E. Grundberg, K. Akesson, A. Kindmark, P. Gerdhem, A. Holmberg, D. Mellstrom, O. Ljunggren, E. Orwoll, H. Mallmin, C. Ohlsson, et al.
The Impact of Estradiol on Bone Mineral Density Is Modulated by the Specific Estrogen Receptor-{alpha} Cofactor Retinoblastoma-Interacting Zinc Finger Protein-1 Insertion/Deletion Polymorphism
J. Clin. Endocrinol. Metab., June 1, 2007; 92(6): 2300 - 2306.
[Abstract] [Full Text] [PDF]

Home page
 Genome ResHome page
B. Edderkaoui, D. J. Baylink, W. G. Beamer, J. E. Wergedal, R. Porte, A. Chaudhuri, and S. Mohan
Identification of mouse Duffy Antigen Receptor for Chemokines (Darc) as a BMD QTL gene
Genome Res., May 1, 2007; 17(5): 577 - 585.
[Abstract] [Full Text] [PDF]

Home page
 Ann Rheum DisHome page
G. Livshits, B. S Kato, G. Zhai, D. J Hart, D. Hunter, A. J MacGregor, F. M K Williams, and T. D Spector
Genomewide linkage scan of hand osteoarthritis in female twin pairs showing replication of quantitative trait loci on chromosomes 2 and 19
Ann Rheum Dis, May 1, 2007; 66(5): 623 - 627.
[Abstract] [Full Text] [PDF]

Home page
 ANN INTERN MEDHome page
A. G. Uitterlinden, S. H. Ralston, M. L. Brandi, A. H. Carey, D. Grinberg, B. L. Langdahl, P. Lips, R. Lorenc, B. Obermayer-Pietsch, J. Reeve, et al.
The Association between Common Vitamin D Receptor Gene Variations and Osteoporosis: A Participant-Level Meta-Analysis
Ann Intern Med, August 15, 2006; 145(4): 255 - 264.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
C. A. Parsons, H. J. Mroczkowski, F. E.A. McGuigan, O. M.E. Albagha, S. Manolagas, D. M. Reid, S. H. Ralston, and R. J. S. Reis
Interspecies synteny mapping identifies a quantitative trait locus for bone mineral density on human chromosome Xp22
Hum. Mol. Genet., November 1, 2005; 14(21): 3141 - 3148.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
14/7/943    most recent
ddi088v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (45)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ralston, S. H.
Right arrow Articles by Bennett, S. T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ralston, S. H.
Right arrow Articles by Bennett, S. T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?