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. Author manuscript; available in PMC: 2018 Oct 1.
Published in final edited form as: Bone. 2017 Jun 23;103:70–77. doi: 10.1016/j.bone.2017.06.016

Genetic Sharing with Coronary Artery Disease Identifies Potential Novel Loci for Bone Mineral Density

Cheng Peng 1,2, Jie Shen 1, Xu Lin 1, Kuan-Jui Su 3, Jonathan Greenbaum 3, Wei Zhu 3, Hui-Ling Lou 2, Feng Liu 2, Chun-Ping Zeng 4, Wei-Feng Deng 5, Hong-Wen Deng 1,3,*
PMCID: PMC5796548  NIHMSID: NIHMS888872  PMID: 28651948

Abstract

Bone mineral density (BMD) is a complex trait with high missing heritability. Numerous evidences have shown that BMD variation has a relationship with coronary artery disease (CAD). This relationship may come from a common genetic basis called pleiotropy. By leveraging the pleiotropy with CAD, we may be able to improve the detection power of genetic variants associated with BMD. Using a recently developed conditional false discovery rate (cFDR) method, we jointly analyzed summary statistics from two large independent genome wide association studies (GWAS) of lumbar spine (LS) BMD and CAD. Strong pleiotropic enrichment and 7 pleiotropic SNPs were found for the two traits. We identified 41 SNPs for LS BMD (cFDR<0.05), of which 20 were replications of previous GWASs and 21 were potential novel SNPs that were not reported before. Four genes encompassed by 9 cFDR-significant SNPs were partially validated in the gene expression assay. Further functional enrichment analysis showed that genes corresponding to the cFDR-significant LS BMD SNPs were enriched in GO terms and KEGG pathways that played crucial roles in bone metabolism (adjP < 0.05). In protein-protein interaction analysis, strong interactions were found between the proteins produced by the corresponding genes. Our study demonstrated the reliability and high-efficiency of the cFDR method on the detection of trait-associated genetic variants, the present findings shed novel insights into the genetic variability of BMD as well as the shared genetic basis underlying osteoporosis and CAD.

Keywords: Bone Mineral Density (BMD), Coronary Artery Disease (CAD), Pleiotropy, Genome Wide Association Study (GWAS)

1. Introduction

Bone mineral density (BMD) is a complex trait with high heritability. It’s a major risk factor for osteoporosis, the incidence of which increased dramatically in recent years due largely to the ageing of population worldwide. Nearly 54 million adults were affected by osteoporosis or osteopenia in 2010 in the US, and this number was estimated to increase to 70.8 million from 2010 to 2030 [1]. The incidence of fragility fracture – the most severe complication of osteoporosis, also increased proportionally with the high prevalence, leading to long term disability, high mortality to afflicted individuals and huge economic burden on their families and societies [2]. BMD is a heritable trait with the highest heritability of 83% at lumbar spine (LS) [3]. To date, genome-wide association studies (GWAS) have reported more than 200 SNPs associated with BMD (https://www.ebi.ac.uk/gwas, April 2017), yet only explained a small proportion of the whole heritability [4]. For better disease control and prevention, further studies are warranted to uncover novel BMD associated genetic variants.

Numerous epidemiological and clinical evidences have shown a relationship between BMD and coronary artery disease (CAD). CAD is the most common type of heart disease, which is the major cause of death worldwide, accounting for almost 1 of every 4 death in the United States [5]. BMD and CAD are both highly heritable traits [3,6], BMD decreasing diseases (such as osteoporosis and osteopenia) and CAD have high prevalence and occur concurrently in the elderly [1,5]. It has been reported that in healthy men, BMD was inversely associated with their 10-year CAD risk [7], while among women, slight increase of annual BMD loss was notably associated with higher coronary artery calcification - a main cause of plaques in inner lining of coronary artery which eventually leads to CAD [8]. Osteoporosis and CAD share common risk factors, such as dyslipidemia, estrogen and/or vitamin D deficiency [9], and GWAS analysis has revealed strong genetic overlap between BMD and CAD risk factors, such as lipoproteins cholesterol and blood pressure [10]. Molecular pathways involved in bone mineralization and coronary artery calcification are very similar [11], meanwhile bone marrow cells including osteoclasts and osteoblasts were identified in calcified plaques of vessels [12]. In addition, in mouse models, deletion of osteoprotegerin caused both osteoporosis and vascular calcification [13].

The underlying genetic mechanism of the relationship between BMD and CAD might partially come from the presence of pleiotropic effect, which means that one single gene could influence multiple traits. By leveraging pleiotropy with associated traits and using novel analytical methods, we may be able to explore more of the traits’ total heritability. Recently, Andreassen et al. proposed a novel, high-efficient and cost effective analysis method - pleiotropy informed conditional false discovery rate (cFDR) method on existing GWAS data and successfully identified potential novel loci for a number of traits as well as pleiotropic loci for multiple disorders [14,10]. cFDR method has the advantage to increase the effective sample size of existing GWAS data for individual traits/diseases and improve the detection of genetic variants without the need of larger datasets or new recruitments. According to the authors’ simulation, while compared with the unconditional FDR during a single trait analysis, using the cFDR method could increase 15–20 times the number of non-null SNPs discovered for a FDR smaller than 0.05 [14]. In our previous studies, we successfully implemented the cFDR analyses and identified potential novel and pleiotropic variants for type 2 diabetes and/or birth weight [15] as well as for BMD and height [16]. Here, we applied the cFDR method to two large independent GWAS data of LS BMD and CAD, aiming to detect more potential novel trait-associated single nucleotide polymorphisms (SNPs) and genes for BMD.

2 Method and materials

2.1 GWAS datasets

We obtained GWAS summary statistics of LS (lumbar spine) BMD from the Genetic Factors for Osteoporosis Consortium (GEFOS). This dataset contains summary statistics from meta-analysis of 35 individual GWAS studies (n=53,236) conducted by whole genome sequencing, whole exome sequencing and deep imputation of genotype data. To date, it is the largest published GWAS data of BMD and 10,582,866 SNPs were included [17]. The CAD GWAS summary statistics were obtained from the CARDIoGRAMplusC4D Consortium. It is a meta-analysis of 22 CAD GWAS studies with 22,233 cases and 64,762 controls (n=86,995), and 2,420,360 SNPs were included [18]. There were no overlapping subjects between the LS BMD and CAD GWAS datasets. The detailed phenotype characteristics and inclusion criteria for the two GWASs were described in the original publications [18,17].

2.2 Data processing

First, based on linkage disequilibrium (LD) SNP pruning method and using HapMap 3 genotypes as a reference, we performed pruning on LS BMD and CAD datasets respectively. The linkage disequilibrium (LD) between each pair of SNPs in the datasets was calculated by Plink, for those SNP pairs with r2 > 0.2, the SNP with the smaller allele frequency (MAF) was removed [19]. After pruning, there were 369,514 SNPs remained for LS BMD and 167,894 SNPs remained for CAD. Next, the two datasets were combined and there were 128,164 common SNPs remained for both phenotypes to be used in the subsequent analysis. Genomic control has already been applied in the two datasets to ensure that the variance estimates for each SNP were not inflated due to population structure [18,17].

2.3 Statistical Analyses

2.3.1 Pleiotropic Enrichment Estimation

Quantile-quantile plot (Q-Q plot) is an exploratory tool implemented in R to evaluate whether the primary phenotype is related to the given phenotype under the null hypothesis, which is also termed as “enrichment”. The Q-Q curve was plotted for the quantile of nominal –log10 (p) values for association of the subset of variants that were below different significance threshold in the conditional trait. The empirical quantiles (-log10(q)) were plotted on x-axis and the nominal p-values (-log10 (p)) were plotted on y-axis. As the principal phenotype successively conditioned on more stringent significance criteria in the conditional phenotype, the pleiotropic enrichment could be observed intuitively by the degree of leftward deflection from the expected identity line.

2.3.2 The calculation of cFDR and conjunction cFDR (ccFDR)

The cFDR method is regarded as an extension of the standard FDR framework. It represents the possibility that a random SNP is null for association with the principal phenotype given that the observed p-values for the principal and conditional phenotypes are both less than two pre-defined trait specific significance thresholds [14]. Using GWAS p-values of the two pruned datasets, we computed cFDR for every single SNP while LS BMD was the principal phenotype conditioned on the strength of its association with CAD (LS|CAD) and reversely (CAD|LS). To assess whether the cFDR method results in the enrichment of specific SNPs, the subset of SNPs was confined based on the level of significance for the association of each variant with the conditional trait using the following criteria: p < 1 (all SNPs), p < 0.1, p < 0.01 and p < 0.001. A threshold of cFDR less than 0.05 was used to determine whether the SNP was significantly associated with the principal phenotype. The procedure of this method has been described in detail by Jonathan et al [16]. ccFDR was computed to identify pleiotropic SNPs for both LS BMD and CAD after the calculation of cFDR. It refers to the possibility that a given SNP has a false positive association with the two traits, and is taken as the maximum cFDR value between them. We used a threshold of ccFDR less than 0.05 to identify whether the SNP was significantly associated with both traits and was a pleiotropic one. Manhattan plots of cFDR and ccFDR were constructed respectively by R to mark the chromosomal locations as well as significance of various SNPs. SNPs with –log10 cFDR value or –log10 ccFDR value more than 1.3 were regarded as significant.

2.4 Annotation of Potential Novel SNPs for LS BMD

To compare with the previous BMD GWAS findings and verify whether the cFDR-significant SNPs are potential novel ones, we assembled a confirmed_SNP set by downloading all the SNPs that were already confirmed to be associated with BMD in earlier GWASs from the web site https://www.ebi.ac.uk/gwas, April 2017 (Supplementary Table 1). Totally there were 251 SNPs that were confirmed by previous BMD GWASs. Then, based on HapMap genotype data and using LD threshold at 0.8 as a criteria, we input the set of cFDR-significant SNPs with p values > 5E-8 and the confirmed_SNP set into the SNPinfo Web Server to perform LD analysis (https://snpinfo.niehs.nih.gov/). Each LD block may contain one or more than one SNP, a SNP that is not clustered in the same LD block with SNPs of the confirmed_SNP set was regarded as a potential novel SNP. Meanwhile, SNPs that clustered in the same LD block with SNPs of the confirmed_SNP set were regarded as replication of the previous findings.

2.5 Gene Expressional Validation Analysis

Using gene expression profiling in iliac bone biopsies from 84 postmenopausal Caucasian women [20], we performed gene expressional partial validation analysis by calculating the correlation values between LS BMD and the mRNA levels of cFDR-significant LS BMD associated genes. The details of the gene expression profiling of iliac bone biopsies can be found in the original publication [20].

2.6 Functional Enrichment analysis and Protein-Protein Interaction analysis

To explore the biological roles of cFDR-significant SNPs/genes, we performed functional enrichment analysis and protein-protein interaction analysis. cFDR-significant SNPs were mapped to nearby genes by the online tool SNPinfo Web Server (https://snpinfo.niehs.nih.gov/). Using Web-Based Gene Set Analysis Toolkit [21], we input these genes into the WebGestalt system and performed functional term enrichment analysis. Gene ontology (GO) terms and KEGG canonical pathways with adjusted p value less than 0.05 were regarded as significant. Functional term enrichment analysis of the corresponding genes gave us the opportunity to further partially validate our results by identifying gene sets that play key roles in bone metabolism. Protein-protein interaction analysis for the corresponding genes were conducted using the online database STRING 10.0 (http://string-db.org/), it helped us explore the functional roles of the proteins produced by the identified LS BMD-associated genes in the context of biological mechanisms of bone metabolism.

3 Results

3.1 Pleiotropic enrichment assessment and pleiotropic SNPs identified for LS BMD and CAD

Quantile-quantile plot (Q-Q plot) was constructed to assess the pleiotropic enrichment between LS BMD and CAD. As shown in Figure 1, strong enrichment could be observed for LS BMD associated SNPs across different levels of association with CAD (LS│CAD), the separation shown between different curves indicated the extent of pleiotropic SNPs shared between both traits. The earlier departure from the expected line indicated the greater proportion of true associations for a given CAD p-value. Similar patterns of strong enrichment could also be observed for CAD conditioning on LS BMD (CAD│LS) (Detailed information of the CAD cFDR-significant SNPs were presented in Supplementary Table 2). Totally we identified 7 pleiotropic SNPs mapping to 3 chromosomes reached the significance threshold of ccFDR < 0.05 (Figure 2 and Table 1). Genes encompassed by these pleiotropic SNPs were found to be associated with bone metabolism [22,23] and/or CAD pathology [24], as well as risk factors for both osteoporosis and CAD, such as obesity and insulin resistance [25,26].

Figure 1.

Figure 1

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Quantile-quantile plots (Q-Q plots) of nominal versus empirical -log10(p) for LS BMD as a function of significance of the association with CAD (LS |CAD) at the level of -log10(p) > 0, -log10(p) > 1, -log10(p) > 2, -log10(p) > 3 corresponding to p < 1, p < 0.1, p < 0.01, p < 0.001 respectively; and reversely CAD as a function of significance of the association with LS BMD (CAD|LS).

Figure 2.

Figure 2

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Conjunction Manhattan plot of conjunction –log10 FDR values for LS BMD and CAD. The red line marking the conditional –log10 FDR value of 1.3 corresponds to a ccFDR < 0.05. The figure shows the genomic locations of pleiotropic SNPs and further details are presented in Table 1.

Table 1.

Pleiotropic SNPs for LS BMD and CAD (ccFDR<0.05)

SNP Chr Pos p value ccFDR Nearby Gene
LS CAD
rs10039254 5 178503520 1.49E-04 3.01E-04 1.08E-02 ADAMTS2
rs10765090 10 133238729 1.12E-03 1.17E-04 1.98E-02 TCERG1L and FLJ46300
rs11005240 10 57598774 7.41E-04 8.05E-04 4.02E-02 LOC389970 and ZWINT
rs12449964 17 17485429 3.74E-03 4.02E-07 2.24E-02 PEMT and RAI1
rs12943500 17 17731313 7.69E-05 3.24E-05 2.23E-03 TOM1L2
rs6502629 17 17810367 2.57E-04 1.48E-05 4.88E-03 TOM1L2
rs7208561 17 17556253 5.92E-03 4.55E-05 4.59E-02 RAI1
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Column definition: SNP – single nucleotide polymorphisms; Chr – chromosome; Pos – chromosomal position; LS – lumbar spine bone mineral density; CAD – coronary artery disease; ccFDR – conjunction conditional false discovery rate.

3.2 LS BMD SNPs identified with cFDR

We identified 41 LS BMD SNPs mapping to 13 chromosomes conditioned on CAD with a significance threshold of cFDR < 0.05 (Supplementary Figure 1), all the details were presented in Table 2. Of these cFDR-significant SNPs, 16 have been discovered in the original GWAS to be significantly associated with LS BMD (p ≤ 5E-8 in the original GWAS), and 3 other SNPs with p values > 5E-8 for LS BMD (rs10464592, rs11753987 and rs2741856) have reached GWAS significance threshold of p ≤ 5E-8 for femoral neck BMD in the original GWAS [17]. SNP rs7683315 resides in the same LD block with two other SNPs (rs6532023, rs1471403) of the confirmed_SNP set (supplementary Table 1). These 20 SNPs were regarded as successful replication of previous BMD GWASs’ findings and well demonstrated the reliability of cFDR method in detecting trait-associated genetic variants. Totally we identified 21 potential novel SNPs for LS BMD (bold SNPs in Table2), which have been overlooked in the original GWAS analysis using standard statistical methods [17]. Of these potential novel SNPs, 9 out of 21 were located in or near genes (ESR1, FLJ42280, JAG1, MEPE, SOST, TNFRSF11B, TNFSF11, WNT4) that were confirmed in previous GWASs to be significantly associated with BMD [27], there were 12 potential novel SNPs located in or near genes which were not reported to be associated with BMD before.

Table 2.

LS BMD SNPs conditioned on CAD (cFDR<0.05)

Expressed QTL
SNP Chr Pos p (LS) cFDR (LS│C
AD)		 Nearby Gene AffymetrixID r
rs11209223 1 68373736 4.04E-06 2.04E-02 WLS NA NA
rs1864569 1 68406845 2.80E-06 1.43E-02 WLS NA NA
rs2473252 1 22411184 2.18E-05 4.53E-02 WNT4/LOC343384 230751_at/NA −0.04/NA
rs2566758 1 68405071 4.07E-08 6.02E-05 WLS NA NA
rs7554551 1 68437611 1.29E-15 1.32E-11 WLS NA NA
rs944082 1 68415679 3.36E-10 2.45E-06 WLS NA NA
rs4854512 2 68857961 5.06E-06 1.22E-02 ARHGAP25 1555076_at −0.14
rs7683315 4 88992119 2.91E-07 8.64E-04 MEPE/HSP90AB3P 221150_at/NA *0.46/NA
rs10039254 5 178503520 1.49E-04 7.38E-03 ADAMTS2 214454_at −0.12
rs2089190 5 171972970 8.69E-06 3.75E-02 SH3PXD2B/LOC100130394 1562910_at/NA −0.09/NA
rs1038304 6 151974868 1.05E-12 2.55E-08 CCDC170 220581_at *−0.25
rs11753987 6 151893121 1.10E-07 7.90E-04 CCDC170 220581_at *−0.25
rs12197879 6 151937470 7.31E-10 1.03E-05 CCDC170 220581_at *−0.25
rs1293935 6 152059651 7.89E-07 4.57E-03 CCDC170/ESR1 220581_at/211233_x_at *−0.25/−0.09
rs2504065 6 152136860 4.95E-07 2.88E-03 CCDC170/ESR1 220581_at/211233_x_at *−0.25/−0.09
rs7746854 6 151970035 4.21E-06 1.87E-02 CCDC170 220581_at *−0.25
rs10464592 7 96068622 5.08E-08 4.24E-04 FLJ42280/LOC100128444 NA/NA NA/NA
rs10499926 7 96168243 2.55E-07 1.52E-03 SHFM1 202276_at 0.12
rs1376264 7 37924814 9.30E-09 8.49E-05 SFRP4/EPDR1 204052_S_at/223253_at *0.3/0.05
rs10955924 8 120122524 7.26E-19 7.54E-14 TNFRSF11B/COLEC10 204933_s_at/207420_at −0.08/−0.13
rs12375331 8 120120103 1.52E-07 1.02E-03 TNFRSF11B/COLEC10 204933_s_at/207420_at −0.08/−0.13
rs1905776 8 120088833 2.42E-07 1.78E-03 TNFRSF11B/COLEC10 204933_s_at/207420_at −0.08/−0.13
rs1948467 8 119905753 1.77E-09 1.78E-05 LOC441377/TNFRSF11B NA/204933_s_at NA/−0.08
rs2055101 8 119956104 9.70E-12 2.82E-07 LOC441377/TNFRSF11B NA/204933_s_at NA/−0.08
rs3134063 8 120028838 1.85E-14 6.23E-10 TNFRSF11B 204933_s_at −0.08
rs1007865 10 124040120 2.60E-05 2.76E-02 BTBD16 1552566_at 0.05
rs10765090 10 133238729 1.12E-03 1.98E-02 TCERG1L/FLJ46300 231257_at/NA 0.07/NA
rs11005240 10 57598774 7.41E-04 3.52E-02 LOC389970/ZWINT NA/204026_s_at NA/0.14
rs2513277 11 68117463 5.42E-11 1.16E-06 SAPS3 NA NA
rs7936582 11 68049506 3.18E-08 2.82E-04 SAPS3 NA NA
rs12430303 13 41930027 8.30E-09 7.76E-05 FABP3P2/TNFSF11 216569_at/210643_at 0.14/−0.12
rs4454853 13 41871297 4.74E-09 5.42E-05 FABP3P2/TNFSF11 216569_at/210643_at 0.14/−0.12
rs9594738 13 41850145 3.63E-20 3.95E-15 FABP3P2/TNFSF11 216569_at/210643_at 0.14/−0.12
rs724404 16 49515626 3.22E-07 1.81E-03 LOC727992/LOC728654 NA/NA NA/NA
rs12449964 17 17485429 3.74E-03 2.24E-02 PEMT/RAI1 207621_s_at/226143_at −0.16/−0.1
rs12943500 17 17731313 7.69E-05 2.23E-03 TOM1L2 214840_at −0.06
rs2741856 17 39182365 9.59E-08 6.41E-04 LOC100128016/SOST NA/223869_at NA/*0.5
rs6502629 17 17810367 2.57E-04 4.88E-03 TOM1L2 214840_at −0.06
rs7208561 17 17556253 5.92E-03 4.59E-02 RAI1 226143_at −0.1
rs8073599 17 62797556 6.05E-06 2.93E-02 PSMD12/LOC729822 202352_s_at/NA −0.13/NA
rs6040061 20 10588306 5.94E-09 3.91E-05 JAG1 216268_s_at 0.1
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Column definition: LS BMD – lumbar spine bone mineral density; SNP – single nucleotide polymorphisms; bold SNPs represent potential novel SNPs identified by cFDR; CAD – coronary artery disease; Chr – chromosome; Pos – chromosomal position; p (LS) – p value in the original BMD GWAS for lumbar spine; cFDR – conditional false discovery rate; LS│CAD –lumbar spine bone mineral density as the principal phenotype conditioned on the strength of its association with coronary artery disease; r – correlation coefficient; NA – not applicable (undetected);

*

r values represent nominally significant (p < 0.05) Pearson correlations, that means its corresponding gene was partially validated in the gene expression assay.

3.3 Gene expressional validation analysis for cFDR-significant genes associated with LS BMD

Using gene expression profiling in iliac bone biopsies from 84 postmenopausal Caucasian women [20], the correlation values between LS BMD and the mRNA levels of cFDR-significant genes were calculated and presented in the rightmost columns of Table 1. mRNA transcripts of 4 genes (MEPE, CCDC170, SFRP4 and SOST) annotated by 9 LS BMD cFDR-significant SNPs (with *r values) were significantly correlated with LS BMD (p value < 0.05, this threshold of p value was used here for the purpose of partial validation).

3.4 Functional enrichment analysis for LS BMD cFDR-significant genes

To explore the functional role of LS BMD associated genes in bone metabolism, we performed a series of analysis. Firstly, we input the corresponding genes into WebGestalt system for GO term analysis and KEGG pathway analysis. The corresponding genes were enriched in as much as 47 GO terms (Supplementary Table 3), many of which were significantly associated with bone metabolism (Table 3). As shown in Table 3, “Ossification” (adjP = 2.30E-03) was one of the most significantly enriched GO terms for LS BMD associated genes. It is the process of bone formation and a major part of bone metabolism. Other GO terms which obviously played important roles in bone were also found for the corresponding genes, such as “negative regulation of canonical Wnt receptor signaling pathway” (adjP = 3.70E-03), “regulation of bone resorption” (adjP = 7.30E-03), “regulation of bone remodeling” (adjP = 8.00E-03), “negative regulation of ossification” (adjP = 9.00E-03). Interestingly, LS BMD associated genes were also enriched in GO terms related to estrogen, such as “Response to estrogen stimulus” (adjP = 2.40E-03) and “Cellular response to estrogen stimulus” (adjP = 3.70E-03). BMD is believed to be a marker of women’s lifetime exposure to estrogen, estrogen helps maintain bone mass and its deficiency plays a key role in the process of osteoporosis among menopausal women [28]. This interesting GO term result further validated the functional roles of LS BMD associated genes in the estrogen related bone metabolism. The significant KEGG pathways were presented in Table 4, of which “proteasome” (adjP = 1.20E-02) was the most significantly enriched. Protein degradation mediated by ubiquitin-proteasome is very crucial to both bone formation and resorption [29]. TNFRSF11B and TNFSF11 were enriched in “osteoclast differentiation pathway” (adjP = 4.30E-02), which was crucial to the resorption and remodeling of bone. WNT4 and SFRP4 were enriched in “Wnt signaling pathway” (adjP = 4.30E-03). Activation of Wnt signaling pathway increases bone mass while inhibition of it decreases bone mass and promotes osteoporosis in mice [30], and polymorphisms in Wnt pathway have been identified to be associated with altered BMD variation in GWAS and meta-analysis [31].

Table 3.

Bone associated GO term results for LS BMD cFDR-significant genes (adjP < 0.05)

GO ID adjP GO Term Genes
1503 2.30E-03 ossification WNT4,TNFSF11,MEPE,SOST,ESR1
43627 2.40E-03 response to estrogen stimulus WNT4,ESR1,SFRP4
71391 3.70E-03 cellular response to estrogen stimulus WNT4,ESR1
90090 3.70E-03 negative regulation of canonical Wnt receptor signaling pathway WNT4,SOST,SFRP4
60828 7.30E-03 regulation of canonical Wnt receptor signaling pathway WNT4,SOST,SFRP4
45124 7.30E-03 regulation of bone resorption TNFSF11,TNFSF11B
30178 7.30E-03 negative regulation of Wnt receptor signaling pathway WNT4,SOST,SFRP4
46850 8.00E-03 regulation of bone remodeling TNFSF11,TNFSF11B
30279 9.00E-03 negative regulation of ossification MEPE,SOST
30278 1.08E-02 regulation of ossification WNT4,MEPE,SOST
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Column definition: GO – gene ontology term; adjP – p value adjusted by the multiple test adjustment.

Table 4.

KEGG pathway analysis for LS BMD cFDR-significant genes (adjP < 0.05)

Pathway Name adjP Genes
Proteasome 1.20E-03 SHFM1, PSMD12
Wnt signaling pathway 4.30E-03 WNT2, SFRP4
Osteoclast differentiation 4.30E-03 TNFRSF11B, TNFSF11
Cytokine-cytokine receptor interaction 9.50E-03 TNFRSF11B, TNFSF11
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Column definition: adjP – p value adjusted by the multiple test adjustment.

3.5 Protein-protein interaction analysis for LS BMD cFDR-significant genes

Based on connection evidence with STRING 10.0 summary score above 0.4, protein-protein interaction network was constructed to explore the biological function of the cFDR-significant genes (Figure 3). Strong interactions were detected between proteins produced by cFDR-significant genes, especially among WNT4, SFRP4, WLS, JAG1, MEPE, ESR1, TNFRSF11B, TNFSF11 and SOST. For example, protein produced by SFRP4 regulates Wnt signaling through direct interaction [32], JAG1 encodes Jagged1 protein, Jagged1-Notch1 signaling induces the expression of WNT4 in encocardial cells [33]. SOST inhibits Wnt signaling and negatively regulates bone formation [34], TNFRSF11B is the decoy receptor for TNFSF11 and neutralizes its function in osteoclastogenesis [35]. Interestingly, though some genes encompassed by cFDR-significant SNPs did not have any interaction with other cFDR-significant genes in this network, they were found in studies to be associated with bone tumors (TOM1L2 and ADAMTS2) [22,23] and risk factors to osteoporosis, such as insulin resistance (TCERG1L) [36] and obesity (RAI1) [25].

Figure 3.

Figure 3

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Protein-protein interaction analysis for LS BMD cFDR-significant genes. Connections are based on evidence with a STRING 10.0 summary score above 0.4. Network nodes represent proteins produced by the corresponding genes, edges between nodes indicate protein-protein associations, edge color indicates the type of interaction and was specified on the right in the figure.

4 Discussion

Our study demonstrated a strong pleiotropy between LS BMD and CAD, 7 pleiotropic SNPs were identified and validated the hypotheses of the shared genetic basis between the two complex traits. More importantly, by leveraging power from the epidemiologically and clinically related trait CAD, we successfully identified 41 LS BMD-associated SNPs with cFDR < 0.05, of which, 20 SNPs have been identified in the original and other BMD GWAS analyses to be significantly associated with BMD [17,37]. These 20 SNPs were considered as successful replication of the previous BMD GWASs and demonstrated the reliability of cFDR method on detecting trait-associated genetic variants for complex phenotypes. Totally we identified 21 potential novel LS BMD associated SNPs without any further recruitment or new study participants, some of these SNPs were located in or near genes that reached detection level in our gene expression validation assay, and most of their corresponding genes were enriched in GO terms and KEGG pathways that have close relationship with bone metabolism. Our findings strongly highlighted the reliability, cost-effectiveness and high-efficiency of the current cFDR method on its enhanced power of detecting potential novel potential genetic variants in complex traits and diseases.

Of the 21 potential novel LS BMD SNPs identified by cFDR method, there were 8 SNPs located in or near 5 genes which were confirmed before to be GWAS-associated with BMD. These 5 previously confirmed genes were WNT4, WLS, CCDC170, ESR1 and TNFRSF11B. rs11209223 and rs1864569 were located in WLS, rs2473252 was located in WNT4. Transmembrane protein produced by WLS can regulate WNT proteins’ sorting and secretion in a feedback regulation [38]. Meanwhile, as an important member of WNT family, WNT4 was the key gene in the enrichment analysis of our study, it was included in nearly all the GO terms with adjP < 0.05, many of which have been proved to be very important to bone metabolism, such as “Ossification”, “regulation of canonical Wnt receptor signaling pathway” and “regulation of bone remodeling”. In osteoporosis mouse models, WNT4 decreases osteoclast formation as well as bone resorption via inhibiting nuclear factor-kappaB (NF-kappaB), and its expression on osteoblast in transgenic mice can greatly promote bone formation [39]. rs7746854 was located in CCDC170, rs1293935 and rs2504065 were located near CCDC170 and ESR1. CCDC170 was one of the genes that were validated in our gene expressional validation assay, its genetic variation was proved to be significantly associated with BMD after adjustment of age and gender [40]. ESR1 is a crucial gene involved in hormone-associated diseases, it encodes estrogen receptor and regulates the function of estrogen on its target tissues including bone. rs12375331 and rs1905776 were located near TNFRSF11B. TNFRSF11B encodes osteoprotegerin (OPG), which is a decoy receptor for receptor activator of nuclear factor kappa-B ligand (RANKL), after binding to RANKL, OPG can prevent osteoclast maturation and reduce bone resorption [41]. Considering the functional roles these 5 genes have played in bone metabolism, we believed that these 8 potential novel SNPs represent important genetic variants to LS BMD, further replication studies and biological function research may follow their attention on them.

In addition to the 8 potential novel SNPs mentioned above, there were other 13 potential novel SNPs located in or near genes that were not found to be GWAS-associated with BMD before, including 7 pleiotropic SNPs (rs11005240, rs12449964, rs12943500, rs6502629, rs10039254, rs10765090 and rs7208561). Some genes annotated by these SNPs were found in studies to be associated with bone and/or CAD. rs12449964 was located near PEMT and RAI1, rs7208561 was located in the intron region of RAI1. PEMT expression could be positively activated by estrogen [42], which was also a crucial regulator to bone metabolism. RAI1 was confirmed to be a CAD significant gene in a previous GWAS [24], in mice and humans, haploinsufficiency of RAI1 was revealed to be significantly associated with obesity - a risk factor for both osteoporosis and CAD [25]. TOM1L2 was encompassed by rs12943500 and rs6502629, it was reported in a previous study that TOM1L2 was associated with 17p amplicons in osteosarcoma – a fatal malignant tumor of bone [23]. rs10039254 was located in the intron region of ADAMTS2, which was associated with the invasion and metastasis of chondrosarsoma – a common bone tumor [22], increased ADAMTS2 expression was detected in coronary plaques causing myocardial infarction [43]. rs10765090 was located near TCERG1L, in African Americans, TCERG1L was confirmed to be significantly associated with insulin resistance [26], which negatively affects bone mass and is an important risk factor for CAD. To date, studies of these potential novel LS BMD associated SNPs (including the 7 pleiotropic SNPs) were very limited, there is still much to learn about the precise mechanism how they affect BMD and/or CAD, yet taken the various relationship between their corresponding genes and bone and/or CAD mentioned above, we inferred that these cFDR-significant SNPs might be potential novel SNPs associated with BMD and/or CAD, RAI1, TOM1L2, ADAMTS2 and TCERG1L might play key roles in the pleiotropy of LS BMD and CAD. More studies were warranted to follow up with them and explore their biological function in bone metabolism and/or pathophysiology of CAD.

In addition to LS BMD, we also performed the pleiotropy analysis on CAD GWAS data with GEFOS data for femoral neck BMD and forearm BMD respectively. However, no significant pleiotropy was found between them. The results may not be surprising, since different skeleton sites do not have all the same genetic determination and the degree of their pleiotropic determination with CAD may also be different [44].

There may be some limitations of our present study. First, cFDR method were not able to identify causal variants for the interested trait, the aim of the present study was to provide more potential novel BMD associated genetic variants, so that replication studies or fine mapping studies could follow up with them in the future. Second, the contribution of our findings to the proportion of BMD’s variability could not be estimated, since we only analyzed GWAS summary statistics, and the raw genotype data was unavailable.

5 Conclusion

cFDR method increased the effective sample size of existing GWAS data and greatly improved the detection of trait-associated genetic variants. By leveraging the pleiotropy with the associated phenotype CAD, we identified 21 potential novel SNPs associated with LS BMD, genes encompassed by these SNPs played important roles in bone metabolism. Our findings shed novel insight into the genetic variability of BMD as well as the shared genetic basis underlying osteoporosis and CAD.

Supplementary Material

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Highlights.

  • Applied cFDR method to GWAS data to identify potential novel SNPs for complex trait

  • Identified pleiotropic SNPs for BMD and coronary artery disease

  • Identified 21 potential novel loci for lumbar spine BMD

Acknowledgments

Hong-Wen Deng was partially supported by grants from the National Institutes of Health [R01AR057049, R01AR059781, D43TW009107, P20 GM109036, R01MH107354, R01MH104680, R01GM109068], the Edward G. Schlieder Endowment fund to Tulane University. We acknowledge GEFOS-seq Consortium, AOGC Consortium, UK10K Consortium, CARDIoGRAMplusC4D Consortium for their GWAS summary statistics posted online. Chun-ping Zeng was partially supported by Medical Research Fund of Guangdong Province, Guangdong, China (A2017575). Cheng Peng was partially supported by Guangzhou Planed Project of Science and Technology, Guangzhou, China [201704020105] and Guangzhou First People’s Hospital during this study.

Footnotes

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Conflicts of interest: None

Contributors:

Hong-Wen Deng conceived and initiated the development of this study, he is responsible for general development and design of the study and contributed to critical revisions and finalization of the manuscript, he is guarantor. Cheng Peng contributed to the acquisition and analysis of the data and drafted the manuscript. Kuan-Jui Su, Wei Zhu contributed to data analysis. Jonathan Greenbaum, Wei-Feng Deng, Chun-Ping Zeng contributed to critical revisions. Hui-Ling Lou, Feng Liu and Jie Shen contributed to the general study design and development. All authors have given approval to the final version of the manuscript. All authors agree to be accountable for the work and ensure that any questions relating to the accuracy and integrity of the paper are investigated and properly resolved.

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Supplementary Materials

1
NIHMS888872-supplement-1.docx (128.3KB, docx)
2
NIHMS888872-supplement-2.docx (35.2KB, docx)
3
NIHMS888872-supplement-3.docx (16.4KB, docx)
4
NIHMS888872-supplement-4.docx (15.6KB, docx)

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