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Keywords:

  • genome-wide scan;
  • linkage;
  • BMD;
  • quantitative trait loci;
  • siblings

Abstract

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

Few genome-wide linkage studies of osteoporosis have been conducted in the Asian population. We performed a genome-wide scan involving 3093 adult siblings with at least one sib-pair extremely concordant or discordant for hip BMD. Our results indicated four genome-wide significant QTLs for BMD. In comparison with 12 previous reported linkage studies, we reveal novel linkage regions that have reaching global significance.

Introduction: The genetic basis for osteoporosis has been firmly established, but efforts to identify genes associated with this complex trait have been incomplete, especially in Asian populations. The purpose of this study was to identify quantitative trait loci (QTLs) for BMD in a Chinese population.

Materials and Methods: We performed a genome-wide scan involving 3093 siblings 25–64 years of age from 941 families, with at least one sib-pair extreme concordant or discordant for total hip BMD from a large community-based cohort (n = 23,327) in Anhui, China. Linkage analysis was performed on BMD residuals adjusted for age, height, weight, occupation, cigarette smoking, physical activity, and alcohol consumption using the revised Haseman-Elston regression-based linkage model.

Results: Our results revealed significant QTLs on chromosome 7p21.2 for femoral neck BMD (LOD = 3.68) and on chromosome 2q24.3 for total hip BMD (LOD = 3.65). Suggestive linkage regions were found to overlap among different skeletal sites on chromosomes 2q, 7p, and 16q. Sex-specific linkage analysis further revealed a significant QTL for lumbar spine BMD on chromosome 13q21.1 (LOD = 3.62) in women only. When performing multivariate linkage analysis by combining BMDs at four skeletal sites (i.e., whole body, total hip, femoral neck, and lumbar spine BMD), an additional significant QTL was found at chromosome 5q21.2 (LOD = 4.56). None of these significant QTLs found in our study overlapped with major QTLs reported by other studies.

Conclusions: This study reveals four novel QTLs in a Chinese population and suggests that BMD at different skeletal sites may also share common genetic determinants.


INTRODUCTION

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

Osteoporosis is a systematic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, with a consequent increase in fragility and susceptibility to fractures. It is estimated that by 2020, one half of all American citizens over age 50 will be at risk for fractures from osteoporosis and low bone mass.(1) BMD is frequently used as a proxy measure of osteoporosis and is also the most important predictor for osteoporotic fractures.(2) Several lines of evidence strongly suggest that BMD is heritable. Twin studies estimate that genetic factors account for 75–85% of interindividual variance in BMD.(3–6) The notion of high heritability in BMD has also been supported by familial aggregation studies that show high correlations in BMD among child-parent pairs.(7,8) Segregation analysis in healthy families suggests that bone mass is under polygenic control.(9) In an attempt to identify genes that are involved in the regulation of BMD, genetic linkage analysis and candidate gene association studies have been used to implicate several loci and candidate genes.(10) However, genes that contribute to genetic susceptibility to osteoporosis remain to be elucidated.

Linkage analysis has been successfully used to identify chromosomal regions and to map causal genes in classical Mendelian diseases. A few whole genome-scan linkage studies on BMDs have been reported. Of which, most studies are in populations of European origin,(11–17) and others are either in Mexican Americans(18) or a mix of white and blacks.(19,20) Among these studies, genome-wide significant quantitative trait loci (QTLs) of BMD with LOD ≤ 3.6(21) have been mapped at 1q21–23(19) and 14p12(20) for the lumbar spine, and at 2pter(18) and 10q21(17) for the femoral neck. Suggestive QTLs of BMD were found at 1p36, 1q31.3, 2p23–24, 2q33.1, 3p22, 3q24, 4q25, 4q31–32, 4qter, 6q27, 7p14, 9q22, 11q23, 12q23-q24, 13q14, 13q33–34, 16p13, 16q23, 18p11, 20p12, 20q13, 21q21.2, 21qter, and Xq27 for different skeletal sites. The reported genomic regions are inconsistent, which reflects the polygenic characteristic of BMD. It may also be caused by the difference of subject ascertainment, sample size, and statistical methods.

In general, 5–15% of genetic variation occurs across major populations living on different continents, with the remaining majority of the variation occurring within such populations.(22) However, comparing the polymorphism frequencies of 63,012 single nucleotide polymorphisms (SNPs) among four populations, the correlation between Asian American and others, including blacks, European Americans, and Hispanics, are 0.23, 0.54, and 0.6, respectively.(23) Differences in allele frequencies certainly contribute to population differences in the incidence of some common diseases,(24,25) such as diabetes(26,27) and hypertension,(28) as well as low BMD and osteoporosis.(29–31) Relatively few linkage studies for BMD have been conducted in Asian populations. Thus far, only one relatively small-scale genome-wide scan for linkage in Chinese selected for hypertension has been reported by our group, with modest linkage to chromosomes 2p21 and 13q34 for forearm BMD.(32) Seeking to extend the initial observation, we conducted a large-scale genome-wide scan for BMD at four different skeletal sites (i.e., whole body, total hip, femoral neck, and lumbar spine) in 3093 Chinese men and women from 941 families using the extreme-sib-pairs (ESP) design to identify the loci that regulate BMD.

MATERIALS AND METHODS

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

Study population

This study is part of an ongoing community-based osteoporosis study initiated in 2003 among residents of Anhui Province, China. Men and women 25–64 years of age were recruited.(33) We recruited families when they met the following criteria: (1) at least two siblings in the 40–64 age range and (2) siblings who had the same biological parents. All siblings 25–64 years of age in eligible families were recruited. Participants with a history of the following conditions were excluded from the study: diabetes mellitus type 1, renal failure, chronic infections such as tuberculosis (TB) or other diseases, malignancy, rickets or other metabolic bone diseases, chronic glucocorticoid use, viral cirrhosis, and thyrotoxicosis.

This study was approved by the Human Subjects Committee (the institutional review board) of Harvard School of Public Health and the Ethics Committee of Anhui Medical University. Written informed consent was explained to, read, and signed by each participant.

BMC, BMD, and body composition measurement

DXA (GE-lunar Prodigy, Waukesha, WI, USA) was used to measure soft tissue body composition, BMC (g), and BMD (g/cm2) through whole body, anterior-posterior lumbar spine and total hip scans. Whole body fat mass and lean mass were expressed in terms of weight (g) and as a percentage of body weight. We randomly selected 71 subjects for remeasurements of the whole body and total hip. The maximum interval between the two DXA measurements was 30 days. The coefficient of variability (CV%) of the reproducibility was 1.34 and 2.05 at whole body and total hip, respectively. We defined osteoporosis(34) as a total hip BMD of >2.5 SD below the average peak BMD of young healthy Chinese in the same study area between 25 and 30 years of age (T score < −2.5). Osteopenia was defined as total hip BMD between 1 and 2.5 SD below the peak BMD (−2.5 < T score < −1).

Questionnaires

Comprehensive questionnaires were used to collect participants' demographic, occupational, and lifestyle information, reproductive history, disease history, consumption of alcohol, cigarette smoking, physical activity, history of fractures, and daily diet. Drug history, including oral contraception, hormone replacement therapy (HRT), and calcium and vitamin D supplements, was also collected. A fracture questionnaire was applied for those participants who self-reported their fracture history. Fracture sites, treatments, and the age of the participants when they had the fractures were recorded. We defined menopause status by questionnaires.

Definition of ESP

We performed a genome-wide scan on sib-pairs with extreme total hip BMDs. To define ESP, we first grouped subjects into men, premenopausal women, and postmenopausal women, and fitted a group-specific multiple linear regression model for total hip BMD including age, height, weight, physical activity (moderate, heavy), occupation (farmer, non-farmer), cigarette smoking (never, former, current), and alcohol consumption (never, former, current). We defined subjects with >5 h/day carrying (on back or shoulder), raising, lifting, or moving a load ≤20 kg as having “heavy” physical activity. The total hip BMD residual from this model was calculated for each subject. Our ESP sampling scheme required each sibling's total hip BMD residual to be either above the 90th percentile or below the 10th percentile of the group-specific distribution. Using this criterion, we identified a total of 1378 ESPs from 23,327 subjects currently enrolled. Because no parents are available, additional siblings are also included in the genome-wide scan to improve the information content. In total, we performed genotyping on 3897 siblings from 1042 families.

Genotyping

Fasting blood samples were collected and stored in aliquots at –80°C. Forearm venous blood samples were obtained from all study participants by venipuncture, and genomic DNA was extracted from blood lymphocytes. Large-scale genotyping was performed at the Marshfield Center for Medical Genetics. Subjects were genotyped at 427 microsatellite markers with an average spacing of −10 cM, including 400 from autosomes and 27 from the XY chromosomes (Weber marker set v15). One hundred three duplicated samples were randomly selected and genotyped for reproducibility. Markers with higher genotyping discordant rate, higher missing genotype rate, or deviations from Hardy-Weinberg equilibrium (HWE) at the p < 0.001 level were excluded.

Statistical analyses

Kinship relationship

Sib-pairs with mean shared identity by state (IBS) deviating 3 SD from the population mean were dropped. PEDCHECK(35) and RELPAIR(36) program were used to check for errors in Mendelian inheritance and inconsistencies within pedigrees at all marker loci. The resulting cleaned genotype data were used in subsequent analyses.

Phenotypes

Standardized residuals of BMD at different skeletal sites were used as phenotypes. We first built a predictive model for each BMD trait using all 23,327 recruited participants, stratified by men, premenopausal women, and postmenopausal women. The predictive model was adjusted for age, height, weight, occupation, physical activity, cigarette smoking, and alcohol consumption. Residuals were derived from group-specific predictive models at different skeletal sites. The trait standardized residuals from the predictive model of genotyped subjects were subsequently used in all of our linkage tests. For sex-specific analysis, all available genotyping data were used and set the phenotypes for subjects who did not fall into the category of interesting as missing values.

Linkage analyses

Univariate linkage analysis was performed on the standardized residuals of BMD at whole body, total hip, femoral neck, and lumbar spine using the regression-based linkage program(37) implemented in the MERLIN package.(38) The pedigree-wide regression linkage program is based on a revised Haseman-Elston (H-E) method that performs regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs.

Multivariate analyses of BMDs at different skeletal sites are highly correlated with each other; therefore, these traits may share common linkages. To use such multivariate data efficiently in the analysis, the XWXW program was used to perform a multivariate linkage analysis that combines individual test statistics obtained from the trait-specific univariate analysis for a global assessment on linkage.(39) GeneHunter was used to estimate IBD probabilities (z0, z1, z2) among siblings at any arbitrary chromosomal location.(40) XWXW takes the IBD dump file from GeneHunter directly for all linkage tests. Multipoint linkage analysis by using the Unified H-E (UHE) regression-based method, which uses a linear combination of the estimates of the proportion of phenotypic variance explained by the additive effects of the QTL from the squared sums and squared differences of traits, was performed in XWXW.(41)

Simulation

The genome-wide significant linkage thresholds for LOD score have been shown between 2.8 and 3.6 depending on the study design and data informativeness.(21,42) To adjust for multiple comparisons and estimate the global genome-wide p value, we conducted a simulation analysis using the gene-dropping method(43,44) implemented in the MERLIN package.(38) In brief, we generated 10,000 random datasets under the null hypothesis of no linkage to observed phenotypes. In each simulation, the original phenotypes were used, and a new data set, with the same allele frequencies, marker order, genetic distances between markers, and missing-data patterns as the original data, was generated. Therefore, “significant signals” obtained through simulation are chance findings.

For each simulation, we performed the same set of analyses (including univariate linkage analyses for BMD at whole body, total hip, femoral neck, and lumbar spine; multivariate linkage analysis; and the sex-specific linkage analyses) and recorded the maximal LOD score. The distribution of the maximal LOD scores obtained from 10,000 simulated datasets (LODsimulated) was used to derive the empirical genome-wide p value of the observed test statistic for assessment of global significance. The adjusted genome-wide p values were determined as padjusted = p(LODobserved ≤ LODsimulated).

RESULTS

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

Characteristics of study subjects

After excluding non–full-sibling individuals, a total of 3093 individuals from 941 families were included in the final analysis. A majority of families had three siblings genotyped (47%). Among these 941 families, the numbers of families with two, three, four, five, six, and seven siblings genotyped were 181, 446, 220, 75, 16, and 6 families, respectively. The distribution of ESPs is shown in Table 1. There are 332 male ESPs, 246 female ESPs, and 378 male-female ESPs. Most of the sib-pairs are concordant sib-pairs (90.4% for male sib-pairs and 89% for female sib-pairs).

Table Table 1.. ESP of the Study Subjects
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The principal characteristics of the study subjects are shown in Table 2. In total, 1604 men, 1062 premenopausal women, and 427 postmenopausal women were included in the final analysis. Men had higher weight and height but lower percentage body fat (% Fat) than women. Postmenopausal women had lower BMDs at whole body, total hip, femoral neck, and lumbar spine than men and premenopausal women. However, no significant differences for BMDs between men and premenopausal women were found. Sex- and skeletal site–specific T score was calculated. The average T scores of BMD at whole body, femoral neck, and lumbar spine are < −1 for postmenopausal women. According to the WHO definition, one half of postmenopausal women can be defined as having osteopenia (T score < −1).

Table Table 2.. Mean (SD) of Age, Height, Weight, BMI, and BMDs in Study Subjects Stratified by Sex and Menopausal Status
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The majority of the women used an intrauterine device (IUD) for contraception in our study subjects. Only 2% of women currently used oral contraception. There was no significant association between oral contraception use and BMD. No significant association was found between IUD use and BMD. No woman was currently using HRT. Only 22 (0.7%) and 8 (0.3%) subjects regularly took calcium and multiple vitamins, respectively. No association was found with BMD.

Univariate linkage analysis

We genotyped 400 microsatellite markers across autosomes. Two of the markers were excluded from analysis because of a higher drop-out rate (>10%). The average drop-out of the remaining 398 markers was 2.3%. The average heterozygosity was 72.2%. The multipoint linkage results for autosomes are summarized in Fig. 1 along with the information content at each position. As shown in Table 3, our linkage analysis suggested several QTLs for BMD.

Table Table 3.. QTLs (LOD ≤ 2.2) for BMD at Different Skeletal Sites for All Subjects
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Figure Figure 1. QTL plots for total hip (TH), femoral neck (FN), whole body (WB), lumbar spine (LS) BMD and multivariate analysis (ALL) for all subjects.

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For BMD at the total hip (TH-BMD), we identified three QTLs that showed significant or suggestive evidence of linkage (Fig. 1; Table 3). The highest LOD score was 3.65 (pointwise p = 0.00002, padjusted = 0.036) on chromosome 2q24.3 at 173 cM between markers D2S1776 and D2S1391 and achieved global genome-wide significance level. The others were on chromosome 7p21.2 at 22.5 cM between markers D7S2200 and D7S3051 (LOD = 2.93, p = 0.00012, padjusted=0.128), and on chromosome 16q12.1 at 82.3 cM between markers AAT107 and D16S2624 (LOD = 3.14, p = 0.00007, padjusted = 0.085). For BMD at the femoral neck (FN-BMD), we identified three genomic regions that were close to the regions identified as QTLs for TH-BMD. The highest LOD score was 3.68 (pointwise p = 0.00002, padjusted = 0.034) on chromosome 7p21.2 at 22.5 cM and achieved the global genome-wide significance level. The other two QTLs for FN-BMD were on chromosome 2q24.3 at 173 cM (LOD = 2.31, p = 0.0005, padjusted = 0.358) and on chromosome 16q22.1 at 94.8 cM between markers D16S2624 and MFD466-TTA001 (LOD = 2.9, p = 0.00013, padjusted = 0.134). As for lumbar spine BMD (LS-BMD), we only identified one suggestive QTL on chromosome 5q21.1 at 107.5 cM between markers D5S1462 and D5S2501 (LOD = 2.71, p = 0.0002, padjusted = 0.181). Few studies have reported the QTLs for whole body BMD (WB-BMD); three QTLs were identified as having suggestive linkage evidence. They are on chromosome 2q31.1 at 180 cM (LOD = 2.71, p = 0.0002, padjusted = 0.181), on chromosome 7p21.3 at 17.5 cM (LOD = 2.47, p = 0.0004, padjusted = 0.268), and on chromosome 16q22.1 at 97.3 cM (LOD = 2.52, p = 0.0003, padjusted = 0.256), respectively.

Sex-specific linkage analysis

Sex-specific linkage analysis was also performed. In addition to the genomic regions on chromosome 7p and 16q, we identified three suggestive QTLs for BMD in women. The highest LOD score was 3.62 (pointwise p = 0.00002, padjusted = 0.038) for LS-BMD on chromosome 13q21.1 at 44 cM between markers D13S894 and D13S1807 and achieved global genome-wide significance level. The others are on chromosome 5q33.1 at 155 cM for TH-BMD between markers D5S1480 and D5S820 (LOD = 2.47, p = 0.0004, padjusted = 0.278) and on chromosome 13q13.3 at 34 cM for WB-BMD (LOD = 2.5, p = 0.0005, padjusted = 0.265). As for men, two suggestive linkage evidences were found. These are on chromosome 9p21.1 at 50 cM for WB-BMD between markers D9S1121 and D9S304 (LOD = 2.65, p = 0.0002, padjusted = 0.202) and on chromosome 22q11.23 at 22 cM for LS-BMD between markers ATTT019M and D22S689. We also plotted the results, showing the detailed genomic regions on chromosome 2q, 5q, 7p, and 16q for all subjects in Fig. 2 and on chromosome 13q for women in Fig. 3, respectively.

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Figure Figure 2. LOD score for chromosomes 2, 5, 7, and 16. LOD score plot for HIP-BMD is in black solid line, for FN-BMP is in gray solid line, for WB-BMD is in black dot line, and for combined all BMDs is in gray dot line. The flanking markers closest to the peak LOD score are also indicated.

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Figure Figure 3. LOD score for chromosome 13 in women. LOD score for HIP-BMD is in black solid line, for FN-BMP is in gray solid line, for WB-BMD is in black dot line, and for LS-BMD is in gray dot line. The flanking markers closest to the peak LOD score are also indicated.

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Multivariate linkage analysis

As shown in Table 4, the highest correlation coefficient was 0.93 between FN-BMD and TH-BMD, and the lowest correlation coefficient was 0.69 between FN-BMD and LS-BMD. BMD at different skeletal sites are highly correlated with each other; therefore, these traits may share common linkages. We performed a multivariate linkage analysis by combining the individual test statistics of these four traits together. The results are shown in Fig. 1 and Table 3. The detailed genomic regions on chromosomes 2q, 5q, 7p, and 16q for all subjects and on chromosome 13q for women are shown in Figs. 2 and 3. Genomic regions on chromosomes 2q, 5q, 7p, and 16q still show linkage evidences, with peak LODs on chromosome 2q11.2 at 112 cM (LOD = 2.98, p = 0.0001, padjusted = 0.117), on chromosome 5q at 112.5 cM (LOD = 4.56, p = 0.000002, padjusted = 0.011), on chromosome 7p at 20 cM (LOD = 2.86, p = 0.0001, padjusted = 0.142), and on chromosome 16q at 97.3 cM (LOD = 2.49, p = 0.0004, padjusted = 0.268), respectively. In addition to these genomic regions that have been found to be QTLs for a single trait, we identified two suggestive QTLs for combined BMD traits. They are on chromosome 3q27.1 at 198 cM between markers D3S2427 and TTTA040 (LOD = 2.61, p = 0.0003, padjusted = 0.218) and on chromosome 13q13.3 at 36.5 cM between markers D13S894 and D13S1807 (LOD = 2.16, p = 0.0008, padjusted = 0.451), respectively.

Table Table 4.. Pearson's Correlation Coefficients Between BMD Phenotypes
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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

The primary objective of this study was to investigate QTLs for BMD in a Chinese population. By using large samples of selected extreme siblings, we found significant QTLs for TH-BMD on chromosome 2q24.3 at 173 cM (LOD = 3.65, p = 0.00002, padjusted = 0.036) and for FN-BMD on chromosome 7p21.2 at 22.5 cM (LOD = 3.68, p = 0.00002, padjusted = 0.034). When stratified by sex, we also found significant QTLs for LS BMD on chromosome 13q21.1 at 44 cM (LOD = 3.62, p = 0.00002, padjusted = 0.038). In our study, the linkage evidence was mostly overlapped among QTLs for BMD at the total hip, femoral neck, and whole body, but not for the lumbar spine, and the magnitude of significance was different, which suggests that genes may regulate BMD differently at different skeletal sites and that gene–environment interactions may also play an important role at different skeletal sites. However, by using multivariate linkage analysis, a significant QTL for combined TH-, FN-, LS- and WB-BMDs was found on chromosome 5q at 112.5 cM (LOD = 4.56, p = 0.000002, padjusted = 0.011), which showed evidence that BMD measured at these four sites might also share common genetic determinants.

Compared with 12 previous genome-wide linkage studies, we have unique findings in terms of linkage regions and the magnitude of the global significance level of linkage. A variety of study designs have been used in previous reported genome-wide linkage studies, including analysis of families with probands having lower BMD,(11,13,16,17) families with a history of osteoporosis,(15) families drawn from the normal population,(12,18,45) dizygotic twins,(14) sib-pairs from the normal population,(19,20) and sib-pairs selected for hypertension.(32) We estimated the multiple point LOD scores for TH-, FN-, LS- and WB-BMDs at previous reported QTL regions (LOD ≤ 2.2) in our study. Sex-specific LOD scores were shown if previous studies reported sex-specific QTLs. As shown in Table 5, no significant or suggestive QTL has been found in our linkage analysis for previously reported genomic regions. Only two previously reported regions have LOD scores >1, but for different skeletal sites. One is on chromosome 10q21 (LOD = 1.4 for WB-BMD in our study), which has been reported as a significant QTL for the femoral neck (LOD = 4.42) in white men.(17) The other is on chromosome 13q14 (LOD = 1.8 for LS-BMD in our study), which has been reported as a suggestive QTL for the femoral neck (LOD = 2.51) and femoral trochanter (LOD = 3.46) in Mexican-American men.(18)

Table Table 5.. LOD of This Study at Previously Reported QTL Positions With LOD ≤ 2.2
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We compared our reported QTL regions to previous studies. As shown in Table 6, we searched LOD within 40 cM of our significant or suggestive QTL regions in other studies. None of the major QTLs found in our study overlapped with major QTLs reported by other studies. We listed the highest LOD score reported by other studies, of which the peak genome regions were within 40 cM of our reported QTL regions. Most of our reported peak LOD regions on chromosomes 2q, 7p, and 16q are −20 cM distance away from previous reported regions. Kammerer et al.(18) found a suggestive linkage at 13q14 in male Mexican Americans (LOD = 2.51), which is the closest to our QTL region on chromosome 13q. However, we found significant QTLs for LS-BMD only in women (LOD = 3.62, p = 0.00002), but not in men. The inconsistency of reported QTLs may be caused by the differences of subject ascertainment, sample size, statistical methods, and ethnicity. The ethnicity/geographic location might influence bone density through different environmental factors, different frequencies of genetic variants, as well as their interactions that underlie risk of osteoporosis. For example, the Sp1 binding site polymorphism in the collagen, type I, α1 (COL1A1) gene has been shown to be associated with lower spine and femoral neck BMD and higher risk of fractures(46,47) in the white population, but didn't find polymorphism in Asian populations.(30,48) Ferrari et al.(49) reported that healthy white males with the V667M polymorphism in the low-density lipoprotein receptor-related protein 5 (LRP5) gene had significantly lower BMC, lower bone area, and lower stature at the lumbar spine, but we didn't find this polymorphism in our study population.(31) However, the Q89R polymorphism in the LRP5 gene (no polymorphism in white population) is associated with a lower BMD in the Chinese population.(31)

Table Table 6.. Comparison of QTL Regions for Chromosome Regions Within 40 cM Between This Study and Previously Conducted Genome-Wide Linkage Studies
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Mouse models offer a useful tool for causal gene identification and functional analysis.(50) By using F2 intercross of high BMD and low BMD inbred progenitor strains of mice, studies have found several QTLs for bone mass in F2 male mice,(51) whole body BMD,(52) femur length and width,(53) femur and vertebral BMD,(54) and trabecular BMD volume in F2 female mice.(55) Based on the human-mouse homology map, several of our reported QTLs have been found with observed regions of the mouse QTL including (1) human chromosome 5q with mouse chromosome 18 at marker D18Mit36 with LOD = 13.7 for femoral BMD, LOD = 8.4 for vertebral BMD,(54) and LOD = 4.3 for trabecular thickness(55); (2) human chromosome 13q with mouse chromosome 14 at marker D14Mit35 with LOD = 3.4 for femur length,(53) and at marker D14Mit160 with LOD = 8.3 for trabecular thickness(55); and (3) human chromosome 16q with mouse chromosome 8 at marker D8Mit75 with LOD = 3.4 for bone volume of the vertebral trabecular region.(55) A QTL with LOD = 4 for ultimate force at lumbar vertebra (L5) in F2 female rats also matched up with our human chromosome 16q QTL region.(56) This evidence suggests that these may be relevant QTLs. However, we should notice that the mouse QTLs were for the traits that differed from those in our reported QTLs.

To avoid selection bias, it is desirable that the study population is genetically and ethnically homogeneous. Our study population comes from a rural area, with limited access to public transportation and seems to be relatively stable and fairly homogeneous with respect to ethnicity, lifestyle variables, and social and cultural norms, which significantly minimized nongenetic confounding factors. By comparing the correlation coefficient of the proximal radial BMD among 290 dizygotic (DZ), 170 monozygotic (MZ) twins, and 387 sib-pairs from the same area, the estimated heritability for proximal radial BMD is 0.70, again indicating a strong genetic component in our study population (unpublished data).

The information content varies according to marker heterozygosity, marker density, and family structure. The range of information content in our study is from 0.25 to 0.70, with an average of 0.51. Because we did not have the parents' genotypes, and the majority of families only genotyped three to four siblings, compared with other linkage studies with both parents genotyped, the information content in our study is low; therefore, we may not have enough power to detect modest QTLs.(39) A denser set of markers in those significant and suggestive QTL regions is necessary to increase the resolution of gene mapping.

Each of our QTL regions contains 50–200 known genes. Several genes have been found functionally relevant to bone metabolism. Interesting potential candidate genes in chromosome 7p QTL region include sclerostin domain-containing protein 1 (SOSTDC1) and interleukin-6 (IL6). SOSTDC1 belongs to a class of bone morphogenetic protein (BMP) antagonists that bind BMPs and regulate their signaling during cellular proliferation and differentiation.(57)IL6 is located on chromosome 7p15, which is 13 cM away from our peak LOD region. The LOD score near the IL6 region is 2.29 for FN-BMD. Serum IL6 level is associated with femoral BMD loss in postmenopausal women and accounts for up to 34% of the total variability of change in femoral BMD.(58) A potential candidate gene in chromosome 5q QTL region is peroxisome proliferative activated receptor γgg, co-activator 1 (PPARGC1B). PPARGC1B is a major activator to the PPARγ gene, which has been found to be associated with BMD(59) and has been found to play a major role of the central pathway of bone for congenic mice (C Ackert-Bicknell and C Rosen, unpublished data, 2005). Two potential candidate genes in chromosome 2q QTL region are lipoprotein receptor-related protein-2 (LRP2) and secreted frizzled-related protein 3 (SFRP3). LRP2 gene is a member of a family of receptors with structural similarities to the low-density lipoprotein receptor (LDLR). LRP2 knockout animals showed severe vitamin D deficiency and bone disease with abnormal urinary excretion of 25(OH)vitamin D3. LRP2 is essential to preserve vitamin D metabolites and to deliver the precursor for generation of 1,25(OH)2 vitamin D3.(60) The protein encoded by SFRP3 is a soluble antagonist of WNT signaling. Deficient Wnt signaling has been implicated in disorders of reduced bone mass. A functional polymorphism (Arg324Gly) in the SFRP3 gene was associated with a higher risk of hip osteoarthritis in female.(61) It is necessary to further explore if the variants in these potential candidate genes are associated with BMD.

In summary, we identified three significant QTLs at chromosomes 2q24.3, 7p21.2, and 13q21.1 in a Chinese population. When performing multivariate linkage analysis by combining BMDs at four skeletal sites (i.e., whole body, total hip, femoral neck, and lumbar spine), an additional significant QTL was found at chromosome 5q21.2, which suggests that BMD at different skeletal sites may also share common genetic determinants. The different findings between our study in Chinese and previous studies in white imply that genetic variation underlying BMD may depend on ethnicity/geographic location. However, we can not rule out the facts that it could be caused by the differences in study design and within ethnic group heterogeneity. Further studies are currently being undertaken to identify the genes responsible for BMD regulation in these regions.

Acknowledgements

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

This study was supported by NIAMS Grant R01 AR045651. We thank Melissa Veno for editing this manuscript.

REFERENCES

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