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Department of Medicine (C.-L.C., V.C., A.W.C.K.), Genome Research Centre (P.C.S.), Orthopaedics and Traumatology (K.D.K.L.), The University of Hong Kong, Pokfulam, Hong Kong, China; and Program in Genetics and Genomic Biology (A.D.P.), The Hospital for Sick Children Research Institute, University of Toronto, Toronto, Ontario M5G 1L7, Canada
Address all correspondence and requests for reprints to: Annie W. C. Kung, M.D., Department of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China. E-mail: awckung@hkucc.hku.hk; or Pak C. Sham, Ph.D., Genome Research Centre, The University of Hong Kong, Pokfulam, Hong Kong, China. E-mail: pcsham{at}hkucc.hku.hk.
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Abstract |
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Objective: Our objective was to replicate and identify the novel candidate genes in the quantitative trait loci (QTL) at chromosome 14q QTL.
Subjects and Methods: Eighteen microsatellite markers were genotyped for a 117-cM interval in 306 Southern Chinese pedigrees with 1459 subjects. Successful replication of the QTL was confirmed within this region for trochanter and total hip BMD. Using a gene prioritization approach as implemented in the Endeavour program, we genotyped 65 single-nucleotide polymorphisms in the top five ranking candidate genes within the linkage peak in 706 and 760 case-control subject pairs with extremely high and low trochanter and total hip BMD, respectively.
Results: Single-marker and haplotype analyses revealed that ESR2 and latent TGF-β binding protein 2 (LTBP2) had significant associations with trochanter and total hip BMD. Multiple logistic regression revealed a strong genetic association between LTBP2 gene locus and total hip BMD variation (P = 0.0004) and prevalent fracture (P = 0.01). Preliminary in vitro study showed differential expression of LTBP2 gene in MC3T3-E1 mouse preosteoblastic cells in culture.
Conclusions: Apart from ESR2, LTBP2 is a novel positional candidate gene in chromosome 14q QTL for BMD variation and fracture.
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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More than 20 genome-wide linkage scans (GWLS) have been published on BMD and osteoporotic fractures. Nonetheless, inconsistent results remain the major challenge in the quest for identification of genes that affect BMD. Suggestive or significant linkages for BMD variation at several skeletal sites have been detected on chromosome 14q (2, 3, 4). In our recent meta-analysis study of nine GWLS with 11,842 subjects, several significant quantitative trait loci (QTL) were identified, and two neighboring bins on chromosome 14, 14q13.1-q24.1 (P = 0.003) and 14q23.3-q32.12 (P = 0.022), were shown to be significantly linked to hip BMD variation (5). These combined findings suggest that chromosome 14 may harbor multiple candidate genes that contribute to BMD variation at different skeletal sites. Estrogen receptor β (ESR2) is one of the positional candidate genes in this region that has been shown to contribute to BMD variation (6). Given the QTL size and overall strength of linkage signals observed on chromosome 14q, it is likely that more than one susceptibility locus may reside within the QTL. Nonetheless, it remains largely unknown which genes in the region also play a role in BMD regulation.
In this study, we carried out a linkage analysis of the chromosome 14 region in an independent set of southern Chinese families in attempt to replicate the previously reported linkage findings. Using a newly developed bioinformatics tool for gene prioritization, the five top-ranked genes under the QTL were selected for association analysis using the tagging approach. Our results revealed a novel candidate gene LTBP2 on chromosome 14q that is associated with hip BMD variation and fracture prevalence.
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Subjects and Methods |
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The study subjects were extracted from an expanding database being compiled at the Osteoporosis Centre at Queen Mary Hospital, the University of Hong Kong, to determine the genetic and environmental risk factors for osteoporosis. All study subjects were individuals of southern Chinese descent resident in the local community. They were recruited at road shows and health talks on osteoporosis held between 1998 and 2003 and were invited to the Osteoporosis Centre for BMD measurement. All participants gave informed consent, and the study was approved by the Ethics Committee of the University of Hong Kong and conducted according to the Declaration of Helsinki.
For the family study, probands were identified from subjects with BMD Z score of less than or equal to –1.28 (the lowest 10th percentile of the population) at either the lumbar spine (L1–4) or hip; extended family members were also invited to participate. We estimated family informativeness for 1021 pedigrees. Based on previous heritability estimates of BMD of 70% from a similar population, the expected LOD score for each pedigree was estimated, via regression, on the basis of phenotypic values, by using the option rankFamilies in Merlin-regress. Families with the highest informativeness were selected for evaluation (1). Three hundred six families with 1459 subjects (293 males and 1166 females) spanning two to four generations were analyzed. These pedigrees contained 1260 sib pairs, 143 cousin pairs, 2356 parent-child pairs, 522 grandparent-grandchild pairs, and 512 avuncular pairs. Detailed descriptions of these families have been reported previously (7, 8).
To increase the power of the association study, a threshold-defined case-control design was adopted, and unrelated subjects from the opposite extreme of the distribution of BMD were studied for the association analysis. Low BMD subjects were arbitrarily defined as those with a BMD Z score of less than or equal to –1.28 (equivalent to the lowest 10th percentile of the population) at either the spine or hip. High BMD subjects were sex-matched controls with a BMD Z score higher than +1 (approximately equivalent to the 85th percentile of the population) at the corresponding bone site. We identified 833 unrelated case-control pairs of subjects, with 706 trochanter case-control pairs and 760 total hip case-control pairs. A total of 633 pairs constituted both trochanter and total hip cohorts. The case-control cohorts were sex and age matched. Detailed inclusion and exclusion criteria have been described previously (8, 9).
BMD measurements and prevalent fracture assessment
BMD (grams per square centimeter) at the spine L1–L4, femoral neck (FN), trochanter, and total hip was measured by dual-energy x-ray absorptiometry (Hologic QDR 4500 plus; Hologic Waltham, MA). The in vivo precision of the machine for spine, FN, and total hip region was 1.2, 1.5, and 1.5%, respectively (10). Weight and height were measured at the same visit. Thoracolumbar spine x-rays were assessed for radiographic evidence of spine fracture at baseline using the semiquantitative method (11). All low-trauma fractures at the spine, hip, and distal radius and morphometric fracture at the spine were included in the final analysis.
Microsatellite marker genotyping
Genomic DNA was extracted from peripheral blood leukocytes using a phenol/chloroform extraction method. A total of 18 high-density microsatellite markers were genotyped in the chromosome 14 region delineated by D14S972 (located at 14q12) and D14S1007 (located at 14q32.2) encompassing a 117-cM interval. This region showed significant linkage to hip BMD variation in a recent meta-analysis. All markers were commercially available through PE Applied Biosystems (ABI PRISM Linkage Mapping Sets, version 2; Norwalk, CT). Marker order and map positions were obtained from the Marshfield map (Fig. 1
). The average intermarker distance was 6.5 cM, and average population heterozygosity was 70%. Genotyping was performed on an ABI PRISM 3700 genetic analyzer using the GENESCAN and GENOTYPER software for allele identification and sizing.
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Multipoint regression-based linkage analysis was performed for BMD at the spine, FN, trochanter, and total hip in the whole study population using Merlin-regress (12, 13). Merlin-regress can handle nonrandomly ascertained samples and deviations from multivariate normality of the observed data but no loss in power when compared with the variance component method (13). Based on our previous heritability study, heritability of 0.7 was used for hip BMD in the Merlin-regress. On the basis of the criteria, LOD of at least 1.3 would be considered as significant replication of a previous reported QTL (14). To obtain the empirical P value corresponding to the linkage result, we empirically derived the P values from 1000 simulated data sets using the gene-dropping method in Merlin-regress. The P values were calculated for the full sample. If a LOD score of 1.3 is reached in 10 of the 1000 simulated data sets, this would indicate an empirical P value of 0.01.
Candidate gene selection, single-nucleotide polymorphism (SNP) selection, and genotyping
An arbitrarily defined region of a 1-LOD interval from the linkage peak was selected for fine mapping. We applied a recently developed gene prioritization approach, implemented in software Endeavor (15), to identify the positional candidate genes. Endeavor prioritizes the candidate genes in a three-step analysis. In the first step, the biological information about the disease is gathered from a set of training genes, which are known to affect the disease. The biological information is based on the literature, gene ontology, gene expression data, expressed sequence tag expression data, protein domain, protein-protein interaction, pathway, cis-regulatory modules, transcriptional motifs, and sequence similarity. The training genes were selected based on our recent review of the genetics of osteoporosis (16): VDR, ESR1, BMP2, IL-6, IGF-I, CYP19, LRP5, CLCN7, TGF-β, COL1A1, and SOST. In the second step, a set of testing genes is loaded to the software. In our study, the chromosomal region 14q22.1-24.3 was loaded for testing and prioritizing the genes. In the last step, the testing genes are ranked based on the similarity with the training properties (i.e. the biological information gathered in the first step). In our study, the top five candidate genes were selected for association analysis.
To improve the efficiency of association studies, an aggressive tagging approach (17) was adopted to select a subset of informative SNPs in each gene from the HAPMAP Chinese population data Rel 21/Phase II (18), with force-tagging of the SNPs located in 5'-untranslated region and coding region. SNPs were genotyped using the high-throughput Sequenom MassARRAY system (Sequenom, San Diego, CA). Genotyping was repeated in 5% of the samples for verification and quality control; genotype data were confirmed to have an error rate of less than 0.1%.
Association analyses
The genotyping quality of each SNP was first checked for the call rate, minor allele frequency (MAF), and Hardy-Weinberg equilibrium using the HAPLOVIEW (19). SNPs with MAF less than 0.01, call rate less than 90%, and Hardy-Weinberg equilibrium less than 0.01 were excluded from further analysis. Genotype and allele frequencies for each SNP were determined by gene counting. Pairwise linkage disequilibrium (LD) was calculated as r2 for all SNP-pair combinations using HAPLOVIEW.
Odds ratio and 95% confidence interval (CI) were determined using binary logistic regression to determine the association of the SNPs and SNP haplotype and the BMD status (high vs. low); BMD was adjusted for age, sex, height, and weight in the logistic model. The global association per candidate gene was assessed by multiple logistic regression with adjustment for confounding factors (age, height, weight, and sex) while controlling for other loci located at the same gene. This omnibus test [likelihood ratio test (LRT)] provided a single test for a collection of SNPs per candidate gene locus and an overall evidence of association. It also ameliorated the multiple comparisons that are usually encountered in a single-marker test, because the LRT has already accounted for all markers in the same gene locus and provided a single P value for overall association. In practice, this genotypic multiple regression is comparable to haplotype analysis. Although it does not include phase information, the power of the two tests is similar (20). In the second stage, backward logistic regression was applied to identify the most predictive SNPs in each gene locus.
Haplotype association was performed in two ways. In the first, we applied the default block definition in HAPLOVIEW using the method of Gabriel et al. (21). Haplotype tests of association were run using logistic regression on blocks of SNP markers identified in HAPLOVIEW with or without adjustment of confounding factors. In the second approach, a three-marker sliding window approach was adopted using WHAP (22). For H haplotypes, the omnibus (global) test (H–1 degree of freedom test) assessed the overall association of haplotypes in the haplotype window with the trait. The haplotype-specific test was performed only if a significant association was observed in the global test.
Associations between SNPs and prevalent fractures at the hip, spine, and distal forearm were assessed using a single-marker test and multiple logistic regression as described above. Symptomatic as well as morphometric spine fractures were included for analysis.
Power estimation of threshold-defined case-control subjects
Power calculations were performed with the quantitative trait case-control calculator in the Genetic power calculator (23). We presumed that the tested marker was the QTL itself or was in complete LD with the causal allele and that the QTL follows additive inheritance.
The power estimation based on the above method revealed that our study had over 80% power to detect a marker responsible for at least 1% of BMD variance at the 
-level of 1–5%.
LTBP2 gene expression in MC3T3-E1 mouse preosteoblast cells
MC3T3-E1 mouse preosteoblast cells were plated at 1.5 x 105 cells/ml in a 24-well plate. Each well contained -MEM supplemented with 10% fetal calf serum, and growth medium was replaced every 2–3 d. Total RNA was isolated using TRI Reagent (Molecular Research Center Inc., Cincinnati, OH) at d 0, 1, 3, 5, 7, 10, 15, and 20 after incubation. The amount of LTBP2 and 18S mRNA expression was quantitated by semiquantitative PCR with LTBP2 primers forward GAGCTCATGATGGCAGTGTG and reverse GCTCCTTCCACTGGGATGTA and 18S primers forward TTTCGAGGCCCTGTAATTGGAATGA and reverse TTCAAAGTAAACGCTTCGGGCCC, respectively.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The results of the multipoint linkage analysis in 306 families are summarized in Fig. 1
. Demographic data have been previously described (7). One marker (D14S63) achieved a LOD score higher 1.3 with trochanter BMD, with the maximum LOD score of 1.55 (nominal P value = 0.004; empirical P value = 0.006) at 69.5 cM near the marker D14S63. For total hip BMD, three markers (D14S63, D14S258, and D14S1036) achieved a LOD score higher than 1.3, with the maximum LOD score of 1.78 (nominal P = 0.002; empirical P value = 0.009) at 69.5 cM near the marker D14S63. A LOD score lower than 1.3 was detected for spine and FN BMD.
Candidate gene selection and association analyses
In the region of the 1-LOD interval, i.e. 14q22.1-24.3, 344 genes were identified (NCBI build 36). We applied the gene-prioritization approach implemented in the Endeavor program to prioritize these genes within the QTL of 14q. The five top-ranked genes were estrogen receptor β (ESR2), transforming growth factor β 3 (TGFB3), bone morphogenetic protein 4 (BMP4), estrogen receptor related β (ESRRB), and LTBP2. Linkage disequilibrium (LD) pattern investigation of our data revealed no evidence of LD between these five genes on chromosome 14q (data not shown).
To assess the association of these five genes with trochanter and total hip BMD variation, a tagging approach with average r2 of 0.8 was used to select a set of informative SNPs for each gene based on the HapMap Chinese population data Rel 21/Phase II (2005). Three, six, 16, seven, and 26 SNPs were tagged for BMP4, ESR2, LTBP2, TGFB3, and ESRRB, respectively, with an intermarker distance of 4.5 kb. We excluded three SNPs in ESRRB in the analysis because they did not pass the genotyping quality control.
Table 1 shows the significant results of the single-marker test and BMD variation at either trochanter or total hip. Clinical characteristics of subjects have been previously described (6). Using an empirical P value of 0.05 through 10,000 permutations, the following SNPs were significantly associated with BMD at the trochanter in either the additive, dominant, or recessive model: rs1256064, rs944052, and T-1213C in ESR2; rs2359141 and rs7569 SNPs in LTBP2; and rs12436385 and rs4903413 in ESRRB. There was no significant association between SNPs in TGFB3 and BMP4 with trochanter BMD variation. Similar results were obtained for total hip BMD: rs1256064, rs944052, and T-1213C in ESR2; rs2286411, rs3825709, and rs2043948 in LTBP2; and rs4414418 and rs12436385 in ESRRB (Table 1).
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) that included all the SNPs in a gene as predictor variables. The strongest association was observed for LTBP2 (P = 0.011 for trochanter BMD and 0.0004 for total hip BMD). The omnibus P value for total hip BMD remained significant after conservative Bonferroni correction of multiple testing with 55 SNPs and two skeletal sites.
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. Multiple SNPs in LTBP2 remained in the final logistic model, suggesting they each had a significant main effect on trochanter and total hip BMD variation.
In the haplotype analysis, we investigated the regional association by performing the three-marker sliding window test. The analysis revealed significant associations between LTBP2 and trochanter and total hip BMD variation (Table 3). Haplotype analysis using block haplotypes revealed similar results (Table 4), with the block haplotypes in LTBP2 demonstrating a significant association with BMD variation at both trochanter and total hip. The block haplotype in ESR2 showed a significant association with trochanter BMD variation.
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The association between 16 SNPs in LTBP2 gene locus and prevalent fracture at the spine, hip, and distal radius was examined. These were the most common osteoporotic fracture sites. The omnibus test revealed an overall association of LTBP2 gene locus with fractures, even after adjustment of BMD at all sites (P < 0.05) (Table 5). Six SNPs remained in the final logistic model with or without adjustment of BMD. The single-marker test revealed similar findings; rs862046 and rs2302114 showed significant associations (P < 0.05) with prevalent fractures without adjustment of BMD. After adjustment of BMD, only rs862046 remained significant.
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In vitro study using the MC3T3-E1 mouse preosteoblast cells demonstrated constitutive expression of LTBP2 gene, with differential expression during osteoblastic proliferation (first 4 d of culture) and subsequent differentiation into mature osteoblasts (Fig. 2).
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Over 300 known genes are present under the QTL on chromosome 14q. Prioritization of the candidate gene while focusing on just a few genes was possible by integrating the information available from multiple publicly available databases (15). Using this approach to select the top five genes from genotyping, we were able to reconfirm our previous findings of the association of ESR2 with trochanter and hip BMD. Besides, LTBP2 showed consistent and multiple significant associations at the single-SNP, multiple-SNP, and haplotype levels, suggesting LTBP2 is another positional QTL gene on chromosome 14q. Using fracture per se as the phenotype, LTBP2 also showed a significant association with prevalent fractures, even after adjustment of BMD, suggesting LTBP2 may exert its independent effect on BMD and fracture.
LTBP2 is located at 84 cM, in close proximity to the second peak near the marker D14S1036 (Fig. 1). The LTBP2 protein is known to play a structural role within elastic fiber and affects extracellular matrix homeostasis (26). It belongs to the family of fibrillin/LTBP glycoproteins and contains two domains, epidermal growth-factor like domains and eight-cysteine repeats, that are important in gene-gene interaction (27). In a recent in vitro study of LTBP2 expression during chondrogenic differentiation of mesenchymal stem cell and chondrocytes, LTBP2 was up-regulated during dedifferentiation and down-regulated during chondrocyte differentiation (28). In addition, the level of expression of LTBP2 in the joint differed between patients and controls with osteoarthritis (29) and in the synovium of patients with systemic lupus erythematosus (26). Although this study did not evaluate the role of LTBP2 in bone metabolism, preliminary in vitro study using the MC3T3-E1 mouse preosteoblast cells demonstrated differential expression of LTBP2 during different stages of osteoblast differentiation (Fig. 2). Based on the common pathway of chondrocyte and osteoblast differentiation from mesenchymal stem cells, and together with our association and preliminary expression data, it is likely that LTBP2 may be involved in osteoblast differentiation, BMD determination, matrix homeostasis, and fracture etiology.
The present study, with over 80% power, identified a number of significant markers with the gene-based omnibus approach even after correction for multiple testing for 55 markers and two traits (P < 0.00045) that warrant further evaluation in other populations (30). Although it could be argued that none of the SNPs were significant in the single-marker test (Table 1) if corrected for multiple testing, it should be noted that allelic heterogeneity (i.e. presence of more than one susceptibility allele in a locus or gene) greatly reduced the power for testing of an individual SNP (31). Therefore, a single gene-based omnibus test (LRT) was used to ameliorate the situation by simply testing the global null hypothesis about the SNPs located per gene. The gene-based omnibus test is a direct and powerful means of protecting the overall false-positive rate when a collection of loci are tested, because the P value from the omnibus test has already reflected the number of SNPs included in the number of degrees of freedom (32). In this regression framework, the association was examined with controlling for other SNPs at the same gene without stratifying by effects at other loci, hence having more advantage over the simple single-marker test (33). Using this omnibus test approach, a significant enrichment in P value was observed in the LTBP2 gene when compared with the single-marker test, suggesting the presence of untagged susceptibility variants. Conversely, the P value in the omnibus test of ESR2 was not enriched when compared with the single-marker test. This suggested that either an absence of an additional susceptibility variant or the causal variant may already be included or tagged (with very high LD) in the study.
In the haplotype analyses, a number of rare haplotypes were associated with BMD variation. These observations may be explained by the existence of an additional rare variant in LD with the rare haplotypes. Although rare haplotypes may not be robust to genotyping error and type I error, the significant result generated from the haplotype omnibus test in a relatively large case-control cohort suggested the overall effect of the test loci should be valid (34). Of course, future replication study in our population will help to validate our findings. Apart from the gene prioritization method, there are other approaches for selection of candidate genes, and we cannot preclude the possibility of the presence of other candidate genes in this QTL.
In conclusion, QTL in chromosome 14 for hip BMD variation was replicated in the Southern Chinese population. In addition to ESR2, significant associations between LTBP2 and BMD and osteoporotic fractures were observed. The association with fracture was through mechanisms both dependent and independent of BMD. These results suggest that the LTBP2 gene might be clinically important in fracture management.
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Acknowledgments |
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Footnotes |
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Current address for C.-L.C.: Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, Boston, Massachusetts 02131.
Disclosure Statement: The authors have nothing to disclose.
First Published Online August 12, 2008
Abbreviations: BMD, Bone mineral density; CI, confidence interval; FN, femoral neck; GWLS, genome-wide linkage scans; LD, linkage disequilibrium; LRT, likelihood ratio test; MAF, minor allele frequency; QTL, quantitative trait loci; SNP, single-nucleotide polymorphism.
Received December 26, 2007.
Accepted August 5, 2008.
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