Genomics Inform Search

CLOSE


Genomics Inform > Volume 12(3); 2014 > Article
Ham and Roh: A Follow-up Association Study of Genetic Variants for Bone Mineral Density in a Korean Population

Abstract

Bone mineral density (BMD) is one of the quantitative traits that are genetically inherited and affected by various factors. Over the past years, genome-wide association studies (GWASs) have searched for many genetic loci that influence BMD. A recent meta-analysis of 17 GWASs for BMD of the femoral neck and lumbar spine is the largest GWAS for BMD to date and offers 64 single-nucleotide polymorphisms (SNPs) in 56 associated loci. We investigated these BMD loci in a Korean population called Korea Association REsource (KARE) to identify their validity in an independent study. The KARE population contains genotypes from 8,842 individuals, and their BMD levels were measured at the distal radius (BMD-RT) and midshaft tibia (BMD-TT). Thirteen genomic loci among 56 loci were significantly associated with BMD variations, and 3 loci were involved in known biological pathways related to BMD. In order to find putative functional variants, nearby SNPs in relation to linkage equilibrium were annotated, and their possible functional effects were predicted. These findings reveal that tens of variants, not a single factor, may contribute to the genetic architecture of BMD; have an important role regardless of ethnic group; and may highlight the importance of a replication study in GWASs to validate genuine loci for BMD variation.

Introduction

In an aging society, osteoporosis is a major health problem. It is characterized by systemic impairment of bone mass, strength, and microarchitecture, leading to increased risk of fragility fractures, particularly hip fracture [1]. In the United States, the cost of incident fractures due to osteoporosis and low bone mass was $17 billion in 2006. It is expected to rise to around $25.3 billion by the year 2025 [2]. Likewise, the burden for them is prevalent in Asia. The annual incidence of osteoporosis-related fractures increased to 1,661, 1,646, 1,623, and 1,614 per 100,000 persons from 2005 to 2008 in Korea [3]. Osteoporosis is defined by the measurement of bone mineral density (BMD), which is so far the best single predictor [4, 5].
BMD is a genetic component, with estimates of heritability ranging from 0.46 to 0.84 at all skeletal sites [6]. In recent years, genome-wide association studies (GWASs) have enhanced the understanding of the genetic architecture underlying quantitative traits and common, complex diseases [7]. These studies provide key insights into the variation of quantitative traits and prospects for designing specifically effective strategies for diseases [8]. There have been many GWASs to identify genetic loci that influence BMD, and dozens of candidates have been presented [9,10,11,12,13]. However, only several loci have been consistently identified in a few replication studies [14,15,16], suggesting that BMD is a complex trait affected by multiple components [17].
Recently, a meta-analysis of 17 studies for BMD of the femoral neck and lumbar spine was performed for the first time in order to identify consensus BMD-associated single-nucleotide polymorphisms (SNPs) throughout races [18]. It included 32,951 individuals across North America, Europe, East Asia, and Australia. The meta-analysis offered 56 associated loci that were highly significant in both the discovery stage and replication stage. This also proposed a model of BMD loci and successfully validated it with an independent population.
Here, we investigate those 56 BMD loci in a Korean population called Korea Association REsource (KARE) [19]. The analysis showed that BMD variation is a polygenic and complex trait, and it could be even different, depending on sex and the skeletal site. The finding that some crucial loci are still significant in KARE supports the validity of BMD loci. This study may contribute to the importance of a replication study in GWASs to understand the common mechanism underlying BMD.

Methods

Study subjects

Subjects from the KARE GWAS have been described [19]. Since it launched in 2001, the total of 10,038 participants have been recruited from the Anseong and Ansan cohorts in the Republic of Korea. Both cohorts were designed to view a variety of quantitative traits from a long-term perspective. Besides, clinical surveys have been performed through epidemiological surveys and physical examinations. Participants have been examined every 2 years, with a third follow-up study in 2008. The analysis of KARE data has been approved by the Korea National Institute for Bioethics Policy (KONIBP), Republic of Korea (P01-201311-RS-02-01).

Genotyping, quality control, and imputation

A total of 10,004 DNA samples were genotyped with the Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix, Santa Clara, CA, USA). Genotypes were called by Bayesian Robust Linear Modeling using the Mahalanobis Distance (BRLMM) algorithm [20]. The quality score criteria and procedures have been described in a previous GWAS [19]. After each step of the quality control, genotypes from 8,842 individuals with a total of 352,228 markers were left and used for the association analysis. In this study, we additionally excluded 257 individuals taking medication that influenced BMD levels.
SNP imputation was conducted using the IMPUTE2 program [21]. The 1000 Genomes Phase I integrated haplotypes (released in Dec 2013), produced using SHAPEIT2 in NCBI build 37 coordinates, were used as the reference panel [22]. All 1,092 individuals with 36.8 million SNPs were initially utilized, and haplotypes with singleton sites were removed. Since there is no X chromosome in the current version of the reference panel, a previous version (released in Mar 2012) was used as the reference panel for the X chromosome.
Because the previous GWAS was based on NCBI build 36, SNP positions should be converted to build 37 coordinates by using the University of California, Santa Cruz (UCSC) liftOver tool [23]. SNP orientations were aligned to the forward strand using PLINK [24]. In order to improve the performance of the SNP imputation, SNPs were pre-phased by SHAPEIT2 [22, 25]. Then, SNPs were imputed directly onto the haplotype estimates by IMPUTE2. Imputation was carried out for the regions within 500 kb on either side of 64 SNPs reported in the previous GWAS [18]. SNPs with a minor allele frequency < 0.01 and imputation quality score (INFO) < 0.4 were filtered out. Then, we combined 51 imputed SNPs with 9 directly typed KARE SNPs for association analyses.

Statistical analysis

The association analyses were done using PLINK version 1.07 [24] for both genotyped and imputed SNPs. A test for BMD was done by linear regression with an additive model (1 degree of freedom [d.f.]) after adjustments for age, sex, and recruitment area. Statistical significance was determined by p < 0.05 in a two-tailed test. p-values were not adjusted for multiple testing of SNPs. Proximal SNPs and pairwise linkage disequilibrium (LD) were computed by PLINK. A threshold for correlation was set as r2 > 0.8. Annotation of SNPs was done by SNPnexus [26]. The effect of SNPs on protein function was predicted using SIFT [27] and PolyPhen [28]. Transcription factor binding sites were inferred from the TRANSFAC database [29].

Results

The KARE study population included GWA genotypes from 8,842 individuals [19]. We removed 257 individuals currently taking medication to remove any potential influence on BMD. BMD was measured by speed of sound (SOS) at the distal radius (BMD-RT) and midshaft tibia (BMD-TT), but the value was transformed to T-score for analysis. The mean T-score of the radius and tibia of the 8,586 subjects was 0.038 ± 0.016 (SEM) and -0.472 ± 0.017, respectively.
The meta-analysis of 17 GWASs identified 64 SNPs in 56 loci associated with BMD with genomewide significance. The analysis also proposed a model of BMD-decreasing alleles, and their effects were validated with an independent population: a prospective study in postmenopausal Danish women aged 55-86 years. The alleles were scored with weights based on their individual effects on BMD. Even though the previous GWAS was based on lumbar spine and femoral neck BMD, we assumed that the effects of BMD loci would be available to BMD at other sites, such as the distal radius and midshaft tibia. Thus, the model was applied to the KARE population to prove its validity.
Among 64 SNPs, 9 SNPs were genotyped in the chip, but 55 other SNPs were imputed from genotypes in the 1000 Genomes (1KG) Project (Phase I). After filtering, 60 SNPs were used for the analysis, and individuals were scored according to the scoring system of the previous study. Then, individuals were grouped into five bins based on their scores, and the mean of BMD of each group was measured (Fig. 1). While BMD-RT did not follow the model proposed in the previous study, BMD-TT showed the pattern as expected. In other words, BMD at the midshaft tibia decreased as the score increased. The analysis on the basis of 60 SNPs explained 3.2% of the total genetic variance in BMD-TT.
In order to identify variants significantly influential in the Korean population, an association analysis of 60 SNPs was done with KARE data. Single-marker tests of association with BMD were carried out by trend test (1 d.f.) after adjustments for age, sex, and recruitment area (Table 1). Twelve SNPs showed a directionally consistent association with the previous GWAS (p < 0.05). Six loci were associated with BMD-RT and six loci were associated with BMD-TT, but there were no loci associated with both. One of these loci is on the X chromosome. The same analysis was carried out by gender strarification (Table 2). Three loci were prioritized in the female sample set, and one of them (rs13204965) was newly found in the analysis. Therefore, a total of 13 independent SNPs were selected for further analysis (Fig. 2).
As mentioned in the previous GWAS, some of these 13 SNPs were seen as members of biological pathways related to BMD: the RANK-RANKL-OPG pathway (TNFRSF11B), mesenchymal stem cell differentiation (SP7), and Wnt signaling (WNT4). Interestingly, SNPs in TNFRSF11B and SP7 were associated with BMD-RT, whereas the SNP in WNT4 was associated with BMD-TT (Tables 1 and 2).
For the purpose of identifying putative functional SNPs, markers in the surrounding 100-Kb region were examined using LD (Table 3). Likewise, they were imputed from 1KG Phase I, centered on 13 loci. Eight BMD-associated SNPs were highly correlated (r2 > 0.8) with functional SNPs annotated in the 1KG reference. rs13245690 was related to 5 putative functional variants, which correlated with each other by LD (r2 > 0.8). Two SNPs (rs1524498 and rs41281692) were located within coding regions in CPED1. rs1524498 leads to synonymous coding, but rs41281692 leads to a conservative change of an amino acid. The remaining 3 SNPs (rs10228519, rs10274486, and rs1357756) were located in the intronic regions in CPED1 and are predicted to alter alleles in transcription factor binding sites. Each allele was anticipated to affect the binding of CUTL1, POU2F1, and GATA-1, respectively.
rs2016266 in SP7 was correlated with two SNPs (rs1318648 and rs17125266) in ESPL1. Both were associated with missense mutations. rs17125266 was predicted to be conservative, but rs1318648 was predicted to be deleterious. rs2062377 was related to a conservative missense variant in TNFRSF11B. On the contrary, rs12821008 was related to the sense variant. Two other SNPs were associated with the 3' untranslated region: rs1346004 in GALNT3 and rs163879 in DCDC5 probably affect splicing regulation. In addition, rs7932354 was predicted to be related to a splice site variant in F2.

Discussion

In this report, we identified that the model proposed in a previous GWAS [18] is available to the Korean population. Furthermore, we found 13 genomic loci significantly associated with BMD variations in the population. Our results reveal that dozens of variants with small effects may contribute to the genetic architecture of BMD and have important roles, regardless of ethnic groups.
Our study supports the important role of several biological pathways related to BMD. The Wnt factor (WNT4) has been known to be associated with BMD in many studies [30,31,32]. A pathway involved in mesenchymal stem cell differentiation (SP7) was also previously known [33, 34]. The RANK-RANKL-OPG pathway (TNFRSF11B) is another pathway influencing BMD [35,36,37]. Interestingly, SNPs in TNFRSF11B and SP7 were associated with BMD-RT, whereas an SNP in WNT4 was associated with BMD-TT. It may suggest differential genetic influences between skeletal sites.
In line with this phenomenon, our results reveal that there is sex- and site-specificity underlying BMD variation. rs13204965 was only significant in women and showed significant sex heterogeneity. Also, in the same manner, the variant was only associated with females in a previous GWAS [18]. In a recent GWAS focusing on women of a specific age (postmenopausal women, age 55-85 years), rs13204965 was associated with BMD at the femoral neck and lumbar spine [13]. In case of rs13245690, a sex-stratified analysis in women showed a more significant result than in pooled samples. These results reflect the sexual dimorphism of bone. Based on the different composition at different skeletal sites, all 12 loci that were significant in pooled samples showed site specificity in BMD. Interestingly, even though the extent of effect size was different from skeletal sites, the direction was consistent between sites. This indicates that BMD-associated loci commonly affect BMD determination across skeletal sites, but the different sensitivity of each site may lead to differential genetic impact on BMD variation.
This could account for the decreased effect size of BMD loci in the Korean population. While the meta-analysis looked at the BMD at the femoral neck and lumbar spine, the KARE focused on BMD at the distal radius and midshaft tibia. Besides, the former were measured by dual-energy X-ray absorptiometry, but the latter were estimated as SOS by quantitative ultrasound. It has been previously shown that BMD of the radius, assessed by ultrasound, poorly coincides with radiologically assessed BMD at the lumbar spine [38]. These represent the limitations of this study- that direct replication at specific sites cannot be performed. Nevertheless, some loci found in the previous GWAS still showed a directionally consistent association in this study. This suggests that a common mechanism underlies BMD throughout different skeletal sites.
In order to identify putative functional variants, SNP-nexus was chosen to annotate proxy SNPs near the BMD-associated loci. In coding regions, some SNPs were expected to present single amino acid substitutions that could be damaging or tolerated. On the other hand, the other SNPs were expected to be synonymous. However, this type of mutation sometimes leads to unsuspected effects. Other annotated SNPs had possible functional effects on the regulation of splicing or the binding activity of transcription factors. To understand how genetic variants affect the phenotype exactly, multi-layered experimental approaches should be carried out in the future.
In conclusion, these findings highlight the polygenic and complex variation of BMD, which could differ even by sex and skeletal site, but provide evidence of the validity in BMD-associated loci between independent populations. This study may contribute to the importance of replication studies in GWASs in understanding the common mechanism underlying BMD.

Acknowledgments

This work was supported by grants from the Korea Centers for Disease Control and Prevention, Republic of Korea (4845-301, 4851-302, 4851-307); the National Research Foundation of Korea (2010-0020259 and 2011-0030049 to TYR); and BK21 PLUS fellowship program funded by the Ministry of Education, Korea (10Z20130-012243 to SH).

References

1. Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporos. Am J Med 1993;94:646–650. PMID: 8506892.
pmid
2. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025. J Bone Miner Res 2007;22:465–475. PMID: 17144789.
crossref pmid
3. Park C, Ha YC, Jang S, Yoon HK, Lee YK. The incidence and residual lifetime risk of osteoporosis-related fractures in Korea. J Bone Miner Metab 2011;29:744–751. PMID: 21644058.
crossref pmid
4. Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, et al. Assessment of fracture risk. Osteoporos Int 2005;16:581–589. PMID: 15616758.
crossref pmid
5. Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, et al. Predictive value of BMD for hip and other fractures. J Bone Miner Res 2005;20:1185–1194. PMID: 15940371.
crossref pmid
6. Arden NK, Baker J, Hogg C, Baan K, Spector TD. The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J Bone Miner Res 1996;11:530–534. PMID: 8992884.
crossref pmid
7. Hardy J, Singleton A. Genomewide association studies and human disease. New Engl J Med 2009;360:1759–1768. PMID: 19369657.
crossref pmid pmc
8. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010;363:166–176. PMID: 20647212.
crossref pmid
9. Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, Wilson SG, et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 2008;371:1505–1512. PMID: 18455228.
crossref pmid pmc
10. Rivadeneira F, Styrkársdottir U, Estrada K, Halldórsson BV, Hsu YH, Richards JB, et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 2009;41:1199–1206. PMID: 19801982.
crossref pmid pmc
11. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, et al. New sequence variants associated with bone mineral density. Nat Genet 2009;41:15–17. PMID: 19079262.
crossref pmid
12. Richards JB, Kavvoura FK, Rivadeneira F, Styrkársdóttir U, Estrada K, Halldórsson BV, et al. Collaborative meta-analysis: associations of 150 candidate genes with osteoporosis and osteoporotic fracture. Ann Intern Med 2009;151:528–537. PMID: 19841454.
crossref pmid pmc
13. Duncan EL, Danoy P, Kemp JP, Leo PJ, McCloskey E, Nicholson GC, et al. Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLoS Genet 2011;7:e1001372. PMID: 21533022.
crossref pmid pmc
14. Uitterlinden AG, Ralston SH, Brandi ML, Carey AH, Grinberg D, Langdahl BL, et al. The association between common vitamin D receptor gene variations and osteoporosis: a participant-level meta-analysis. Ann Intern Med 2006;145:255–264. PMID: 16908916.
crossref pmid
15. Ioannidis JP, Ralston SH, Bennett ST, Brandi ML, Grinberg D, Karassa FB, et al. Differential genetic effects of ESR1 gene polymorphisms on osteoporosis outcomes. JAMA 2004;292:2105–2114. PMID: 15523071.
crossref pmid
16. Kung AW, Xiao SM, Cherny S, Li GH, Gao Y, Tso G, et al. Association of JAG1 with bone mineral density and osteoporotic fractures: a genome-wide association study and follow-up replication studies. Am J Hum Genet 2010;86:229–239. PMID: 20096396.
crossref pmid pmc
17. Peacock M, Turner CH, Econs MJ, Foroud T. Genetics of osteoporosis. Endocr Rev 2002;23:303–326. PMID: 12050122.
crossref pmid
18. Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 2012;44:491–501. PMID: 22504420.
crossref pmid pmc
19. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527–534. PMID: 19396169.
crossref pmid
20. Rabbee N, Speed TP. A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 2006;22:7–12. PMID: 16267090.
crossref pmid
21. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009;5:e1000529. PMID: 19543373.
crossref pmid pmc
22. O'Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet 2014;10:e1004234. PMID: 24743097.
crossref pmid pmc
23. Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM, Wong M, et al. The UCSC Genome Browser database: extensions and updates 2013. Nucleic Acids Res 2013;41:D64–D69. PMID: 23155063.
crossref pmid
24. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–575. PMID: 17701901.
crossref pmid pmc
25. Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat Methods 2011;9:179–181. PMID: 22138821.
crossref pmid
26. Dayem Ullah AZ, Lemoine NR, Chelala C. SNPnexus: a web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Res 2012;40:W65–W70. PMID: 22544707.
crossref pmid pmc
27. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009;4:1073–1081. PMID: 19561590.
crossref pmid
28. Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 2002;30:3894–3900. PMID: 12202775.
crossref pmid pmc
29. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006;34:D108–D110. PMID: 16381825.
crossref pmid pmc
30. Baron R, Kneissel M. WNT signaling in bone homeostasis and disease: from human mutations to treatments. Nat Med 2013;19:179–192. PMID: 23389618.
crossref pmid
31. Kikuchi A, Yamamoto H, Sato A. Selective activation mechanisms of Wnt signaling pathways. Trends Cell Biol 2009;19:119–129. PMID: 19208479.
crossref pmid
32. Long F. Building strong bones: molecular regulation of the osteoblast lineage. Nat Rev Mol Cell Biol 2011;13:27–38. PMID: 22189423.
crossref pmid
33. Bruder SP, Jaiswal N, Ricalton NS, Mosca JD, Kraus KH, Kadiyala S. Mesenchymal stem cells in osteobiology and applied bone regeneration. Clin Orthop Relat Res 1998;(335 Suppl):S247–S256. PMID: 9917644.
crossref pmid
34. Krampera M, Pizzolo G, Aprili G, Franchini M. Mesenchymal stem cells for bone, cartilage, tendon and skeletal muscle repair. Bone 2006;39:678–683. PMID: 16765663.
crossref pmid
35. Grimaud E, Soubigou L, Couillaud S, Coipeau P, Moreau A, Passuti N, et al. Receptor activator of nuclear factor kappaB ligand (RANKL)/osteoprotegerin (OPG) ratio is increased in severe osteolysis. Am J Pathol 2003;163:2021–2031. PMID: 14578201.
crossref pmid pmc
36. Yogo K, Ishida-Kitagawa N, Takeya T. Negative autoregulation of RANKL and c-Src signaling in osteoclasts. J Bone Miner Metab 2007;25:205–210. PMID: 17593489.
crossref pmid
37. Hamdy NA. Targeting the RANK/RANKL/OPG signaling pathway: a novel approach in the management of osteoporosis. Curr Opin Investig Drugs 2007;8:299–303.
pmid
38. Khan KM, Sarafoglou K, Somani A, Frohnert B, Miller BS. Can ultrasound be used to estimate bone mineral density in children with growth problems? Acta Paediatr 2013;102:e407–e412. PMID: 23750846.
crossref pmid pmc
Fig. 1
Combined effect of bone mineral density (BMD)-decreasing alleles in Korea Association REsource (KARE) data. The genetic score of each individual was computed on the basis of the weighted model proposed in a previous study [18]. Effects are shown for BMD levels measured at the distal radius (BMD-RT) (A) and BMD levels measured at the midshaft tibia (BMD-TT) (B). Histograms represent the numbers of individuals with each genetic score area (left y-axis). Round spots (right y-axis) show mean BMD-RT standardized values in A and mean BMD-TT standardized values in panel (B). Vertical lines represent 95% confidence limits.
gni-12-114-g001.jpg
Fig. 2
Effects of significant BMD loci on BMD at different skeletal sites. Genetic effects are expressed as standardized values and shown for BMD levels measured at the distal radius (BMD-RT; filled circles) and BMD levels measured at the midshaft tibia (BMD-TT; open circles). Horizontal lines represent 95% confidence limits. ars13204965 was seen as significant in women only.
gni-12-114-g002.jpg
Table 1.
Estimated effects of genomewide-significant SNPs on BMD-RT and BMD-TT
SNP Closest gene A Freq. BMD-RT
BMD-TT
phet sitea
Beta p-value Beta p-value
rs13245690 CPED1 G 0.03 0.032 2.0 × 10–3 0.014 0.16 0.19
rs9533090 AKAP11 T 0.08 0.026 0.01 –0.006 0.56 0.03
rs7521902 WNT4 C 0.48 –0.025 0.02 0.001 0.90 0.07
rs5934507 FAM9B G 0.25 –0.025 0.03 –0.002 0.84 0.14
rs2016266 SP7 G 0.20 0.023 0.03 0.011 0.26 0.44
rs1053051 TMEM263 T 0.32 –0.020 0.05 –0.019 0.06 0.89
rs4233949 SPTBN1 C 0.30 –0.019 0.07 –0.035 4.9 × 10–4 0.30
rs2062377 TNFRSF11B T 0.27 0.003 0.78 0.032 1.1 × 10–3 0.04
rs1346004 GALNT3 A 0.34 –0.005 0.62 –0.028 4.2 × 10–3 0.11
rs163879 DCDC5 T 0.42 0.014 0.17 0.025 0.01 0.48
rs7932354 ARHGAP1 C 0.30 0.019 0.07 0.022 0.02 0.84
rs12821008 DHH T 0.34 0.016 0.13 0.020 0.04 0.76
rs13204965b RSPO3 C 0.01 0.014 0.20 0.009 0.39 0.72

Boldface indicates p < 0.05 or site-specific p < 0.05.

SNP, single-nucleotide polymorphism; BMD-RT, bone mineral density (BMD) levels measured at the distal radius; BMD-TT, BMD levels measured at the midshaft tibia; A, allele; Freq., allele frequency of A; Beta, effect estimates are expressed as standardized values per copy of the SNP allele.

a Site specificity null hypothesis, BetaBMD-RT = BetaBMD-TT;

b rs13204965 was seen as significant in the analysis of women only.

Table 2.
Gender-stratified association analysis of SNPs on BMD-RT and BMD-TT
SNP BMD-RT
BMD-TT
Females
Males
phet gendera Females
Males
phet gendera
Beta p-value Beta p-value Beta p-value Beta p-value
rs13245690 0.049 2.8 × 10–4 0.018 0.24 0.04 0.036 9.9 × 10–3 –0.006 0.73 0.03
rs9533090 0.014 0.29 0.037 0.02 0.42 –0.005 0.74 –0.015 0.35 0.65
rs7521902 –0.015 0.28 –0.028 0.07 0.88 –0.003 0.83 0.014 0.39 0.33
rs5934507 –0.024 0.10 –0.015 0.37 0.96 0.003 0.82 0.002 0.89 0.55
rs2016266 0.021 0.13 0.042 8.3 × 10–3 0.41 0.015 0.29 0.025 0.13 0.73
rs1053051 –0.003 0.81 –0.037 0.02 0.14 –0.022 0.11 –0.011 0.47 0.48
rs4233949 –0.036 8.0 × 10–3 0.002 0.88 0.02 –0.050 3.3 × 10–4 –0.019 0.23 0.05
rs2062377 0.009 0.51 –0.004 0.81 0.41 0.048 4.2 × 10–4 0.020 0.21 0.05
rs1346004 0.000 1.00 –0.008 0.62 0.93 –0.033 0.02 –0.025 0.11 0.38
rs163879 0.001 0.96 0.029 0.06 0.25 0.019 0.17 0.037 0.02 0.46
rs7932354 0.018 0.18 0.025 0.11 0.97 0.023 0.10 0.030 0.05 0.94
rs12821008 0.022 0.11 –0.002 0.91 0.32 0.015 0.29 0.023 0.14 0.85
rs13204965 0.035 0.01 –0.014 0.38 0.01 0.028 0.04 –0.015 0.35 0.02

Boldface indicates p < 0.01 or gender-specific p < 0.05. Effect estimates are expressed as standardized values per copy of the SNP allele.

SNP, single-nucleotide polymorphism; BMD-RT, bone mineral density (BMD) levels measured at the distal radius; BMD-TT, BMD levels measured at the midshaft tibia; Beta, effect estimates are expressed as standardized values per copy of the SNP allele.

a Gender specificity null hypothesis, BetaFemale = Betamale.

Table 3.
Predicted functional SNPs in LD with targeted SNP in each locus
Targeted SNP Functional SNP Gene symbol Dist. r2 D' Type Possible functional effects
rs13245690 rs1524498 CPED1 20.6 0.82 0.91 Synonymous Sense/synonymous; splicing regulation
rs41281692 8.9 0.91 1.00 Non-synonymous Missense (conservative); splicing regulation
rs10228519 14.0 0.98 0.99 Intronic Regulatory region of CUX1
rs10274486 60.2 0.87 1.00 Intronic Regulatory region of POU2F1
rs1357756 67.1 0.98 0.99 Intronic Regulatory region of GATA1 and LMO2
rs2016266 rs1318648 ESPL1 57.4 0.86 0.95 Non-synonymous Missense (non-conservative); splicing regulation
rs17125266 57.0 0.84 0.93 Non-synonymous Missense (conservative); regulatory region of HNF4A
rs1053051 rs1053051 TMEM263 0 1.00 1.00 3' UTR Splicing regulation
rs2062377 rs2073618 TNFRSF11B 43.4 0.93 0.97 Non-synonymous Missense (conservative); splicing regulation
rs1346004 rs13429321 GALNT3 4.0 0.97 1.00 3' UTR Splicing regulation
rs163879 rs3741025 DCDC5 99.7 0.82 0.93 3' UTR Splicing regulation
rs7932354 rs2070852 F2 22.7 0.99 1.00 Splice site Splicing site
rs12821008 rs10747559 KMT2D 40.5 0.97 0.99 Synonymous coding Sense/synonymous; splicing regulation

A threshold for LD is r2 > 0.8.

SNP, single-nucleotide polymorphism; LD, linkage disequilibrium; Dist., distance (kb) between target SNP and functional SNP; UTR, untranslated region.



ABOUT
ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
Room No. 806, 193 Mallijae-ro, Jung-gu, Seoul 04501, Korea
Tel: +82-2-558-9394    Fax: +82-2-558-9434    E-mail: kogo3@kogo.or.kr                

Copyright © 2022 by Korea Genome Organization.

Developed in M2PI

Close layer
prev next