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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2006-2607
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The Journal of Clinical Endocrinology & Metabolism Vol. 92, No. 7 2751-2757
Copyright © 2007 by The Endocrine Society

A Bivariate Whole-Genome Linkage Scan Suggests Several Shared Genomic Regions for Obesity and Osteoporosis

Zi-Hui Tang1, Peng Xiao1, Shu-Feng Lei, Fei-Yan Deng, Lan-Juan Zhao, Hong-Yi Deng, Li-Jun Tan, Hui Shen, Dong-Hai Xiong, Robert R. Recker and Hong-Wen Deng

Laboratory of Molecular and Statistical Genetics and the Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education (Z.-H.T., S.-F.L., F.-Y.D., H.-Y.D., L.-J.T., H.-W.D.), College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, People’s Republic of China; Osteoporosis Research Center and Department of Biomedical Sciences (P.X., L.-J.Z., H.-Y.D., D.-H.X., R.R.R., H.-W.D.), Creighton University Medical Center, Omaha, Nebraska 68131; and Departments of Orthopedic Surgery and Basic Medical Sciences (H.-Y.D., H.S., H.-W.D.), University of Missouri-Kansas City, Kansas City, Missouri 64108-2792

Address all correspondence and requests for reprints to: Hong-Wen Deng, Ph.D., Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri-Kansas City, 2411 Holmes Street, Room M3-C03, Kansas City, Missouri 64108-2792. E-mail: dengh{at}umkc.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: A genome-wide bivariate analysis was conducted for body fat mass (BFM) and bone mineral density (BMD) in a large Caucasian sample. We found some quantitative trait loci shared by BFM and BMD in the total sample and the gender-specific subgroups, and quantitative trait loci with potential pleiotropy were disclosed. BFM and BMD, as the respective measure for obesity and osteoporosis, are phenotypically and genetically correlated. However, specific genomic regions accounting for their genetic correlation are unknown.

Objective: To identify systemically the shared genomic regions for BFM and BMD, we performed a bivariate whole-genome linkage scan in 4498 Caucasian individuals from 451 families for BFM and BMD at the hip, spine, and wrist, respectively. Linkage analyses were performed in the total sample and the male and female subgroups, respectively.

Results: In the entire sample, suggestive linkages were detected at 7p22-p21 (LOD 2.69) for BFM and spine BMD, 6q27 (LOD 2.30) for BFM and hip BMD, and 11q13 (LOD 2.64) for BFM and wrist BMD. Male-specific suggestive linkages were found at 13q12 (LOD 3.23) for BFM and spine BMD and at 7q21 (LOD 2.59) for BFM and hip BMD. Female-specific suggestive LOD scores were 3.32 at 15q13 for BFM and spine BMD and 3.15 at 6p25–24 for BFM and wrist BMD.

Conclusions: Several shared genomic regions for BFM and BMD were identified here. Our data may benefit further positional and functional studies, aimed at eventually uncovering the complex mechanism underlying the shared genetic determination of obesity and osteoporosis.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
OBESITY IS A CONDITION of excess body fat that is associated with an increased risk of cardiovascular diseases and other common diseases (1). Osteoporosis is a chronic disease mainly characterized by low bone mass, leading to an increased susceptibility to fractures (2). Obesity and osteoporosis are two common complex diseases regulated by multiple genetic factors (1, 2, 3). Epidemiological data show that obesity and osteoporosis are two correlated diseases. Studies indicated that obesity may accompany increased bone mass and thus protects individuals from osteoporosis (4), whereas others (5) suggested that adiposity may not protect against osteoporosis.

Body fat mass (BFM) and bone mineral density (BMD), the relevant main measures of obesity and osteoporosis, have strong genetic determination (6, 7). Previous results showed that BFM and BMD are two highly correlated phenotypes (8). For example, it was found that BFM is the major determinant for whole-body BMD (9), and body fat, as an independent predictor, was inversely associated with BMD at the lumbar spine (8). We found a strong positive genetic correlation between BFM and BMD (3). However, the specific genomic regions underlying the genetic correlation between BFM and BMD are unknown.

The main purpose of the present study was to identify systemically the shared genomic regions [i.e. chromosome regions harboring quantitative trait loci (QTLs) influencing both BFM and BMD] for BFM and BMD via a bivariate whole genome linkage scan (WGLS).


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

The study was approved by the Creighton University Institutional Review Board at which the study protocols were implemented. All the individuals involved in the study signed informed-consent documents before entering the project. The sample was ascertained based on osteoporosis. The recruitment inclusion and exclusion criteria were detailed earlier (10). Briefly, patients with chronic diseases and conditions that might potentially affect bone mass, structure, or metabolism were excluded. All the study individuals were Caucasians of European origin and recruited from the vicinity of Omaha, Nebraska.

The sample contained a total of 4498 phenotyped individuals from 451 pedigrees, of whom 4126 were genotyped. The family size ranged from four to 416 individuals, with a mean (SD) of 11.6 (28.5). The pedigree structure is shown in Table 1Go.


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  		TABLE 1. Distribution of pedigree size

 
Measurements

There could be different QTLs influencing BMD variations at different skeletal sites (11). Epidemiological data showed that the osteoporotic bone at the total hip, lumbar spine, and wrist are susceptible to fractures (12, 13). In this study, we measured the BFM (grams) and BMD (grams per square centimeter) at total hip (femoral neck, trochanter, and intertrochanteric region), lumbar spine (L1-L4), and wrist (ultra distal region of the forearm) by dual-energy x-ray scanners (Hologic Inc., Bedford, MA). All scanners were calibrated daily, and long-term precision was monitored with external spine, hip, and wrist phantoms. The coefficients of variation of the accuracy of dual-energy x-ray measurement were 2.2, 0.%, 1.4, and 2.3% for BFM, spine BMD, hip BMD, and wrist BMD, respectively. Weight (kilograms) and height (meters) were measured at the visit for the BFM and BMD measurement.

Genotyping

DNA was extracted from peripheral blood by the Puregene DNA isolation kit (Gentra Systems, Inc., Minneapolis, MN). All the individuals were genotyped with 410 microsatellite markers (including 392 markers for 22 autosomes and 17 markers for the X chromosome with an average population heterozygosity of 0.75 and spaced on average 8.9 cM) from the Marshfield screening set 14 by Marshfield Center for Medical Genetics (Marshfield, WI). The detailed genotyping protocol is available at http://research.marshfieldclinic.org/genetics/Lab_Methods/methods.html.

We performed Pedcheck (14) to ensure that the genotype data conform to Mendelian inheritance pattern at all the marker loci. In our sample we used MERLIN (15) to detect genotyping errors of unlikely recombination (e.g. double recombination) and the overall genotyping missing and error rate was approximately 0.3%. We performed sequential oligogenic linkage analysis routines (SOLAR) commands of relatives and related pairs to analyze the relationships of relative pairs including full sibling pairs, half-sibling pairs, parent offspring pairs, and unrelated marital pairs.

Statistical analyses

Basic characteristics of the studied sample were computed by the SAS package (SAS Institute Inc., Cary, NC). Bivariate quantitative genetic analyses for the estimates of phenotypic, genetic, and environmental correlations and multipoint bivariate linkage analyses on 22 autosomes as well as two-point linkage analysis on the X chromosome were performed using SOLAR, version 3.0.4 (16).

First, we estimated the genetic correlations ({rho}G) and environmental correlations ({rho}E) between pairs of the studied phenotypes, which is based on maximum likelihood ratio and variance component decomposition. The phenotypic correlations ({rho}P) between the studied pairs of phenotypes were divided into the portions due to genes shared in common and due to shared environment, as the following formula:

Formula
where h12 and h22 are respective heritability of trait 1 and trait 2. The significance of {rho}G, {rho}E, and {rho}P was determined by the likelihood ratio test, which compares the likelihood of a full model with that of a constrained one, in which the particular component is constrained to zero.

A bivariate variance components approach is an extension of the univariate approaches (17). In the multipoint linkage model, the analogous covariance matrix for the pedigree is: Formula where {oplus} is the Kronecker product operator, M is the t x t additive genetic covariate matrix caused by the QTL, G is the residual additive genetic covariate matrix, and E is the environmental covariate matrix. Trait-specific means, variance components relating to major gene effects ({varsigma}q2), residual additive genetic effects ({varsigma}g2), and random environmental effects ({varsigma}e2) as well as the three associated correlations {rho}q, {rho}g, and {rho}e are estimated simultaneously using maximum likelihood estimates. {rho}q represents the measure of shared major gene effects near the region for which linkage is being assessed; {rho}g and {rho}e represent the extent of shared residual additive genetic and random environmental correlations on the traits. A more detailed description of this methodology was provided elsewhere (18). Based on the significant genetic correlation and using the extended variance component model in SOLAR that incorporates covariance matrix for the pedigree (17), we conducted bivariate linkage analyses. The bivariate test statistic follows a mixture distribution (1/4 {chi}22:1/2 {chi}12:1/4 {chi}02) (19) and the bivariate LOD scores have 2 degrees of freedom (df) in contrast to the univariate LOD scores (that have 1 df). To compare with the univariate LOD scores, the 2-df bivariate LOD scores were transferred into 1-df LOD scores. The adjusted 1-df LOD scores are the corresponding univariate LOD scores with the same P values as the original 2-df bivariate LOD scores (18).

SOLAR can perform two- and multipoint linkage analyses for large and complex pedigrees. However, currently, the SOLAR cannot handle multipoint linkage analyses for chromosome X, and other software, such as GENEHUNTER and MERLIN, cannot handle our large pedigrees, breaking down the large pedigree into smaller ones would result in substantial loss of statistical power. Therefore, we calculated only two-point LOD scores for X-specific markers. More specifically, the Marshfield gender-specific genetic maps were used in the gender subgroup analyses instead of the gender-averaged map used elsewhere for the combined sample of males and females in this study. (The Marshfield electronic database is at http://research.marshfieldclinic.org/genetics/.)

It is important to differentiate pleiotropy effects (a single locus influencing both traits) and coincident linkage (separate tightly clustered loci each influencing a single trait) when two or more traits show linkages to the same region. To test pleiotropy vs. coincident linkages, we used the method described by Almasy et al. (17). Briefly, the likelihood for the linkage model in which {rho}q was estimated was compared with the likelihood for the linkage model in which {rho}q was constrained to 0 (no shared major gene effect in the region, coincident linkage) or constrained to 1 (complete pleiotropy). To test coincident linkage, twice the difference in the likelihoods is distributed as a {chi}12, whereas for the test of complete pleiotropy, twice the difference in likelihoods follows a mixture distribution of 1/2 {chi}12 and 1/2 {chi}02 (19). The significance of complete pleiotropy and coincident linkage were denoted by p1 and p2, respectively, in Results. We used a P value of 0.05 as a cutoff point for the rejection of either complete pleiotropy or coincident linkage (17).

Adjustment for multiple testing was demanded because three correlated bivariate linkage analyses for BFM and BMD at the spine, hip, and wrist were performed. Applying the method described by Camp and Farnham (20), we conducted linear regression analyses for three pairs of whole genomic bivariate LOD scores and estimated the number of effectively independent tests (N). Different genetic maps were used respectively in linkage analysis for the entire sample, female, and male subgroups. In addition, hypotheses under these three separate analyses are not entirely the same. It would be difficult to compare the corresponding genome scan LOD scores among the three samples via the regression analyses; thus, adjustment for multiple testing was made for each of the three sets of samples, respectively.

Multiple testing and thus the significance levels were adjusted as follows. In our study, the independent test numbers were 2.17, 2.67, and 2.61 for the entire sample, males, and females, respectively, according to the following formula (20, 21): Formula, where X represents the {chi}2 statistic (2 x ln10 x LOD); {alpha}(X) represents the pointwise significance level of exceeding X; C represents the number of chromosomes; G represents the total genome length in Morgane; {rho} measures how rapidly the statistic fluctuates [{rho} = 1 for classical LOD score analyses (21)]; and µ(X) represents a false-positive rate of 1 or 0.05 per genome scan [µ(X) = 1 and 0.05 for threshold of suggestive and significant linkage evidence (21)]. The corresponding genome-wide thresholds of suggestive and significant linkage evidence were 2.24 and 3.65 for the entire sample, 2.34 and 3.75 for the male subgroup, and 2.33 and 3.74 for the female subgroup. We adjusted the potential confounding effects of sex and age in the entire sample and age in males and females in SOLAR (because they generally affect studied phenotype variations significantly). The phenotypic data were examined for and conformed to normality.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Basic characteristics of the individuals are listed in Table 2Go. It is indicated that BMD at the spine, hip, and wrist generally declined, whereas BFM increased with aging. The heritabilities of BFM and BMD at the hip, spine, and wrist BMD after adjusting for gender and age ranged from 0.43 to 0.65 in the whole samples. The gender-specific heritabilities are from 0.31 to 0.70 in males and 0.43 to 0.70 in females. The phenotypic, genetic and environment correlations between BFM and BMD at different skeletal sites are given in Table 3Go. BFMs are highly genetically correlated with BMD at the spine, hip, and wrist in the whole sample and in the male and female subgroups, indicating strongly shared genetic effects among them.


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  		TABLE 2. Basic characteristics of the studied individuals stratified by age and gender

 

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  		TABLE 3. Genetic correlations ({rho}g), environmental correlations ({rho}e), and phenotypic correlations ({rho}p) between BFM and BMD

 
Bivariate linkage analyses in the entire samples

The results of the univariate WGLS for BFM (22) and BMD (10) were reported in our previous publications. For this bivariate linkage analyses in the entire sample, peak bivariate LOD scores were 2.69 (7p22-p21), 2.30 (6q27), and 2.64 (11q13) for BFM and BMD at the spine, hip, and wrist, respectively. Particularly, suggestive linkage evidence was displayed on 6q27 for both BFM and spine BMD (LOD 2.42) and BFM and hip BMD (LOD 2.30). A trivariate linkage analysis using the same extended variance component model incorporated in SOLAR (detailed in Subjects and Methods) confirmed this observation with a suggestive linkage for BFM and hip BMD and spine BMD at 6q27 (Table 4Go). The pleiotropy vs. coincident linkage analysis for the suggestive bivariate linkage showed that 6q27 has complete pleiotropic effects on the variations of BFM and spine BMD (p1 = 0.47, p2 = 0.005) and BFM and hip BMD (p1 = 0.37, p2 = 0.008). Figure 1Go, A and B, shows the LOD score distribution at chromosome 6 for both the bivariate traits and their corresponding univariate traits. However, coincident linkages were suggested for BFM and spine BMD at 7p22-p21 (p1 = 0.004, p2 = 0.35) and BFM and wrist BMD at 6p21 (p1 = 0.03, p2 = 0.5). For other genomic regions (2q32 and 11q13) with suggestive LOD scores, we failed to reject the hypotheses of both complete pleiotropy and coincident linkage.


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  		TABLE 4. Multipoint LOD scores from bivariate analyses for BFM and BMD in different groups

 

Figure 1
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  		FIG. 1. The distribution of bivariate multipoint LOD scores on chromosomes 6 and 13 for BFM and BMD and univariate LOD sores for BFM and BMD. A, Results from the entire sample. Bivariate BFM and spine BMD, solid line; univariate spine BMD, dot-dashed line; univariate BFM, long dashed line. B, Results from the entire sample. Bivariate BFM and hip BMD, solid line; univariate hip BMD, dot-dashed line; univariate BFM, long dashed line. C, Results from the males. Bivariate BFM and spine BMD, solid line; univariate spine BMD, dotted line; univariate BFM, long dashed line.

 
Gender-specific bivariate linkage analyses

The gender-specific bivariate linkage analyses are presented in Table 4Go. Suggestive male-specific bivariate linkages were found at 13q12 (LOD 3.23) for BFM and spine BMD and 7q21 (LOD 2.59) for BFM and hip BMD. We failed to reject the hypotheses of complete pleiotropy (p1 = 0.48) but reject the hypotheses of coincident linkage (p2 = 0.006) at 13q12 for BFM and spine BMD. Figure 1CGo shows the LOD score distribution at chromosome 13 for both the bivariate traits and their corresponding univariate traits. Suggestive female-specific linkage evidence is at 15q13 (LOD 3.32) for BFM and spine BMD and 6p25–24 (LOD 3.15) for BFM and wrist BMD. We rejected the hypothesis of complete pleiotropy (p1 = 0.0013) but failed to reject the hypothesis of coincident linkage (p2 = 0.5) at 6p25–24 for BFM and wrist BMD. For other male- and female-specific genomic regions (7q21 and 15q13) with suggestive LOD scores, we failed to reject the hypotheses of both complete pleiotropy and coincident linkage.

Because of no significant or suggestive linkage results on the X chromosome, the results of two-point linkage analysis in this chromosome were not shown. For convenience to other investigators to explore the common molecular pathways of obesity and osteoporosis in further studies, we identified and listed some interesting candidate genes and corresponding suggestive genomic regions in Table 5Go.


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  		TABLE 5. Candidate genes for BMD and BFM around the disclosed genomic regions with suggestive linkage and previous evidence of linkage

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
This study represents our first effort to investigate shared genetic region for obesity and osteoporosis per se. Our results suggested compatible linkage signals at chromosomes 2, 6, 7, and 11 for both bivariate and univariate linkage analyses. We listed previously found consistent linkage evidences for osteoporosis and/or obesity related traits in Table 5Go. We found that most chromosomal regions identified in this study overlapped with the results of ours and other previous univariate linkage studies for BFM or BMD. For example, we identified linkage region on 6q27 for both BMD and BFM. Consistent linkage evidence of this region has been reported for trochanter BMD by Devoto et al. (23) and BFM by Deng et al. (24). However, some regions, such as 13q12, have not been reported in other studies. This may be due to the difference of samples sizes, study design, and analysis methods. Moreover, the linkage signals for univariate linkage analyses are generally lower than those for bivariate linkage analyses. This may be due to the greater power of bivariate linkage analyses in detecting common QTLs than individual univariate linkage analyses. Bivariate linkage analyses exploit the effects of the shared major gene and the shared residual additive genetic and environmental influences on the traits in the correlation pattern (17). Therefore, we may detect two low univariate LOD scores in the same chromosome region for two traits in separate univariate analyses, whereas we can detect a much higher bivariate LOD score for the two traits in bivariate joint analyses, such as 6q27 for BFM and spine BMD (Fig. 1AGo).

The genomic regions with suggestive bivariate LOD scores detected in the entire sample are somewhat different from those identified in the gender-specific subgroups. The following reasons may be attributed for the difference. First, the entire group included 4498 individuals, with 2682 females and 1816 males. The smaller sample sizes in gender subgroups may result in decreased statistical power for those loci that are important in both genders. Thus, some QTLs with small effects can be found in the entire group but not in gender-specific subgroups. Second, due to gender-specific effects, at the same genomic region, the QTLs important for one gender population may not be important for another gender population or the effect of QTLs is reverse in different gender. Therefore, the admixture may result in inconsistent QTLs between the entire sample and gender-specific subgroup sample. For example, in the present study, a suggestive genomic region on 15q13 (LOD 3.32) was found in the females but in neither the males nor the entire samples.

The genomic regions with suggestive linkages were also different between female and male subgroups. The difference of sample size between males (1816 individuals) and females (2682 individuals) could result in the different linkage signals on the same genomic region between both gender samples. Another possible reason is that there are indeed gender-specific QTLs influencing the variation of both BFM and BMD. Some evidence of gender-specific phenotypic relationship for BFM and BMD may support the above hypothesis. Reid et al. (9) had observed that BFM is related only to whole BMD in premenopausal women but not men. Some researchers have indicated that BFM and leptin were associated with the BMD in women but not men (25, 26).

In addition, the existence of genes with a sex-specific effect should be ultimately tested not just by our evidence here of detecting a region in one sex and not being able to detect it in the other gender, whereas by the evidence that we detect a region in one sex and in the mean time are able to exclude this region in the other sex. We should note that lack of a significant statistical result does not necessarily mean that there is no effect but rather that this sample is not large enough to detect the effect, especially when the effect is small. Genes of differential effects (but not sex-specific gene) in the two sexes (gene by sex interaction) may also lead to differential detection of the genes in the subgroup analyses of the two sexes.

In the entire sample, 6q27 is an interesting chromosome region with complete pleiotropic effect on BFM and BMD at both the hip and spine. In agreement with our results, the same chromosome region on 6q27 was reported to be linked to BMD by Devoto et al. (23) and to BFM by Deng et al. (24) in their genome-wide linkage studies. Currently no candidate gene with potential functions on BFM or BMD was suggested in this region. However, around 6q25, we found an interesting candidate gene, estrogen receptor 1 (ESR1). ESR1 is associated with not only BMD (27) but also obesity (28). Estrogen directly modulates the differentiation of bipotential stromal cells into the osteoblast and adipocyte lineages, causing a lineage shift toward the osteoblast (29). ESR1 is also associated with waist circumference and BFM in middle-aged women via influencing adipose tissue (29). Polymorphisms in the ESR1 gene were found associated with BMD and other osteoporosis-related traits in the cohort (30, 31, 32). Hence, all these studies supported ESR1 as an important candidate gene for both BMD and BFM.

Furthermore, two regions (7p22-p21 and 6p21) with complete coincident linkage for BFM and BMD are identified, suggesting at least two genes in these regions independently influencing the corresponding traits. The region on 7p21 was reported to be linked to BMD in genome wide scan by Deng et al. (11) and linked to another obesity phenotype BMI by Heijmans et al. (33). Karasik et al. (34) had reported QTLs of BMD was linked to chromosome 6p21 in a Framingham Study, and Lange et al. (35) reported QTLs of obesity-related traits was linked to 6p21. Some candidate genes related to BFM are located at 6p21, such as PPARD related to obesity (36) and RUNX2 related to osteoporosis (37). RAC1 and IL6 are two interesting candidate genes at 7p22-p21. RAC1 is related to osteoporosis (38). However, evidence showed that IL-6 at 7p22-p21 and TNF{alpha} at 6p21 are relevant to both BMF and BMD. IL-6 is related to not only osteoporosis (39) but also obesity (40). TNF{alpha} affects not only BMD (41) but also obesity-related phenotypes (42). TNF{alpha} was also associated with excessive fat accumulation in women (43) and contributed to the determination of obesity and obesity-associated hypertension in French Canadians (44). Our inability to detect the complete pleiotropic effect in these two regions may be due to the incomplete or partial pleiotropy effect of IL-6 and TNF{alpha} on both BMD and BFM.

In the gender-specific bivariate WGLS, 13q12 was detected as having complete pleiotropic effect for BFM and spine BMD in males. This is the first time that 13q12 was found linked to BMD and/or BFM. In the region of 13q12, ATP12A (ATPase, H+/K+ transporting, nongastric, {alpha}-polypeptide) and KL (klotho) are two important candidate genes. ATP12A is related to obesity and can increase the body weight loss in potassium-free diet (45). KL was recognized as a candidate gene for osteoporosis (46) and involved in the pathophysiology of bone loss with aging in humans (47). Our results suggest that KL or ATP12A genes or other unknown genes at 13q12 influence both the obesity- and osteoporosis-related phenotypes in males.

A chromosome region of 6p25-p24 has complete coincident linkage effect for BFM and wrist BMD in females. This region harbors two candidate genes that could independently influence BMD and BFM, bone morphogenetic protein 6 for BMD (48) and peroxisomal D3, D2-enoyl-CoA isomerase related to BFM (49). The same region was reported to be linked to BMI in genome-wide scan by Heijmans et al. (34).

For the other genome regions, complete pleiotropy was rejected, suggesting incomplete pleiotropy. Complete pleiotropy and incomplete pleiotropy both indicated shared major gene effect on the trait pair under study. Incomplete pleiotropy (0 < {rho}q < 1) would be expected under multiple circumstances. For example, there could be multiple functional variants in a putative major gene, with some variants influencing both phenotypes and some influencing only one. Genotype-by-environment interactions may modify the putative major gene’s effect on one of the two traits.

In the study, genetic correlations estimated for BFM and BMD is between 0.11 and 0.32; thus, the bivariate heritability estimates for these values should be in the range of 1.0–10% (16, 18). Although significant, the correlation extent is still moderate. Even if the effect is caused by a single common gene, sufficient power is required to detect such moderate genetic effect. In the entire sample of this study, the heritabilities of BFM and three site-specific BMD ranged from 45 to 70%; the QTL effects located on the identified regions ranged from 10 to 20%. In males, the heritability of BFM and spine BMD ranged from 30 to 50%, and the QTL effect located on 13q12 (LOD score = 3.23) ranged from 20 to 35%. In females, the heritability of BFM and wrist BMD ranged from 30 to 40%, and the QTL effect located on 6p25–24 (LOD score = 3.15) ranged from 15 to 30%. Under the circumstances, the sample size required for 90% power to detect linkage in the entire sample, males, and females should be more than 4133, 1837, and 2075 (50); therefore, the sample size in the present study ensured reasonably large power to detect these chromosome regions. The identified loci may be of importance, contributing to the moderate correlation of the trait pairs under study.

In summary, this study identified suggestive shared genomic regions for both BFM and BMD in our total sample and gender-specific subgroups with pleiotropic effect or coincident linkage. We also identified some candidate genes that may deserve further studies. The findings from this study will benefit further research on the interrelationship of complex diseases such as obesity and osteoporosis and give important clues on candidate genes for the future molecular biology studies.


   Acknowledgments
 
We thank all the study individuals for their willingness to participate in the study. The genotyping experiment was performed by the Marshfield Center for Medical Genetics.


   Footnotes
 
This work was supported by grants from the National Institutes of Health (K01 AR02170-01, R01 AR45349-01, R01 GM60402-01 A1, R21AG027110, and R01AG026564), a key project grant from the National Science Foundation of China (NSFC) (30230210), Hunan Provincial Natural Science Foundation of China (04JJ1004), two general grants from the NSFC (30600364, 30470534) and Scientific Research Fund of Hunan Provincial Education Department (05B037), and an LB595 grant from the State of Nebraska. The Marshfield Center for Medical Genetics was supported by National Heart, Lung, and Blood Institute Mammalian Genotyping Service (contract HV48141).

Disclosure Statement: All authors have no conflicts of interest.

First Published Online May 1, 2007

1 Z.-H.T. and P.X. contributed equally to this article. Back

Abbreviations: BFM, Body fat mass; BMD, bone mineral density; df, degrees of freedom; ESR1, estrogen receptor 1; QTL, quantitative trait locus; SOLAR, sequential oligogenic linkage analysis routines; WGLS, whole genome linkage scan.

Received December 4, 2006.

Accepted April 23, 2007.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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