Disease Markers
Volume 2020 (2020), Article ID 8855423, 8 pages
https://doi.org/10.1155/2020/8855423

Research Article

Pharmacogenomics Study for Raloxifene in Postmenopausal Female with Osteoporosis

Hsing-Fang Lu,¹ Po-Hsin Chou,2,3 Gan-Hong Lin,⁴ Wan-Hsuan Chou,¹ Shih-Tien Wang,2,3 Wirawan Adikusuma,1,5 Eko Mugiyanto,⁶ Kuo-Sheng Hung,4,7,8 and Wei-Chiao Chang1,4,9,10

¹Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
²School of Medicine, National Yang-Ming University, Taipei, Taiwan
³Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
⁴Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
⁵Department of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia
⁶Clinical Drug Development of Chinese Herbal Medicine, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
⁷Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei 11042, Taiwan
⁸Department of Neurosurgery, Taipei Medical University-Wan Fang Hospital, Taipei, Taiwan
⁹Pain Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
¹⁰Biotechnology Research and Development, College of Pharmacy, Taipei Medical University, Taipei, Taiwan

Correspondence should be addressed to Kuo-Sheng Hung; kshung25@gmail.com and Wei-Chiao Chang; wcc@tmu.edu.tw

Received 19 May 2020; Revised 25 June 2020; Accepted 7 August 2020; Published 31 August 2020

Academic Editor: Wen-Jun Tu

Copyright © 2020 Hsing-Fang Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Osteoporosis is characterized by decreased bone mineral density and increased risk of fracture. Raloxifene is one of the treatments of osteoporosis. However, the responses were variable among patients. Previous studies revealed that the genetic variants are involved in the regulation of treatment outcomes. To date, studies that evaluate the influence of genes across all genome on the raloxifene treatment response are still limited. In this study, a total of 41 postmenopausal osteoporosis patients under regular raloxifene treatment were included. Gene-based analysis using MAGMA was applied to investigate the genetic association with the bone mineral density response to raloxifene at the lumbar spine or femoral neck site. Results from gene-based analysis indicated several genes (GHRHR, ABHD8, and TMPRSS6) related to the responses of raloxifene. Besides, the pathways of iron ion homeostasis, osteoblast differentiation, and platelet morphogenesis were enriched which implies that these pathways might be relatively susceptible to raloxifene treatment outcome. Our study provided a novel insight into the response to raloxifene.

1. Introduction

Osteoporosis is defined as the weakened architecture of the bone and elevated risk of fracture [1]. In Taiwan, the prevalence of osteoporosis is about 6.9% in males over the age of 65 and 21.2% in postmenopausal females [2]. Several risk factors of osteoporosis have been identified, including menopause, dairy intake, life style, and genetic factors [3]. Osteoporosis is a polygenic disease that is determined by multiple genetic effects from several genes. Variants near WNT16 and FONG have been reported to be associate with bone mineral density (BMD) [4–7]. The risk of osteoporosis increases dramatically in the elderly population, that bone fractures are known to increase mortality and lead to an enormous healthcare burden on society [8, 9]. Since the aged population is growing rapidly, osteoporosis has become a crucial clinical issue.

Nonsteroidal selective estrogen-receptor modulators (SERMs) can inhibit bone resorption. SERMs have been widely used in the prevention and treatment of postmenopausal osteoporosis [10]. Raloxifene is a SERM agent used in the clinic for postmenopausal osteoporosis [11]. The drug is a partial agonist for the estrogen receptor and is known to inhibit proliferation of breast epithelium, while preserving bone mineral density (BMD). This drug increases BMD and reduces the occurrences of new vertebral fractures [12]. However, the therapeutic response of raloxifene varies widely among patients. Previous studies revealed that two polymorphisms of ESR1 (estrogen receptor 1) (rs9340799 and rs2234693) are associated with the BMD response to raloxifene in osteoporosis patients [13, 14]. UGT1A8 (UDP glucuronosyltransferase family 1 member A8) encodes an enzyme involved in extrahepatic glucuronidation of raloxifene [15], and UGT1A82 was found to be associated with more raloxifene glucuronide metabolite formation in human jejunum tissue [16]. Labad et al. also identified a missense variant of UGT1A8, rs1042597, that associated with the treatment responses to raloxifene in postmenopausal schizophrenic women [17].

Previous studies focused on the association between therapeutic responses and variants of a single gene. This type of study design called a candidate gene approach, which is a hypothesis-driven method that tests a small number of variants [18]. Limited genetic variants are unable to discover novel mechanisms. On the other hand, genome-wide association study (GWAS) is a hypothesis-free or hypothesis-generating method that provides opportunities to identify novel insights into the phenotype. In addition, GWAS is able to explore new genetic factors of complex human traits [19]. Since the mechanism of how genes influence response of raloxifene remains unclear, the aim of the current study is using gene-based analysis to identify the main network correlated with the responses of raloxifene.

2. Materials and Methods

2.1. Sample Collection

We collected samples from the neurosurgery outpatient clinic in Wang-Fang Hospital, Taipei, Taiwan. Our inclusion criteria were as follows: (1) 60 to 85-year-old postmenopausal female, (2) , and (3) under regular raloxifene treatment (60 mg/day). Patients with continuous steroid use (of over 6 months) or long-term inflammatory disease were excluded. In total, 41 osteoporosis patients were recruited. Bone mineral density (BMD) was measured by dual-energy radiograph absorptiometry (Lunar Prodigy, version 9.1, GE Healthcare, Madison, WI, USA) with standard protocols at the lumbar spine (LS; L2-4 or L1-4) and femoral neck (FN). Age, gender, body mass index (BMI), medication profile, and follow-up duration of all individual were collected. We also collected the BMD profiles after patients receiving treatment for 1-2 years. The study was approved by the Joint Institutional Review Board of Taipei Medical University. All subjects provided written informed consent.

2.2. DNA Extraction and Genotyping Array

Peripheral whole blood was collected from the patients. Gentra Puregene Blood Kit (Qiagen, Valencia, CA, USA) was used to extract the genomic DNA. The extraction process was according to the user manual of the product. The quality of DNA was confirmed by 1% agarose gel electrophoresis. Sample with no obvious degradation can be submitted to further analysis. Affymetrix Axiom TWB SNP Array (Thermo Fisher Scientific, USA) was used to perform GWAS. The process was done by the National Center for Genome Medicine, Taiwan (NCGM).

2.3. Quality Control

PLINK [20] was applied to conduct the quality control of genotype data. We excluded SNPs with low genotyping call rate (<0.95), minor allele frequency less than 0.05, and value of Hardy-Weinberg equilibrium less than ⁻⁶. Individuals with a sample call rate less than 0.95 were excluded. The imputation task was done by Michigan Imputation Server (Minimac3). Data from the Haplotype Reference Consortium (HRC) was used as reference panels for imputation in order to increase statistical power. After imputation, only variants which and were included in the association analysis. Principle components (PCs) were generated by PLINK with samples from HapMap3. A linear regression model was used for statistically testing the associations between SNPs and phenotypes by PLINK2. We defined the percentage change of bone mineral density from baseline by the following equation:

We further normalized phenotype before association analysis. We included age, BMI, follow-up months, and PC1 to PC10 as covariates in the analysis model. The Manhattan plot and qq plot were visualized by the R package “qqman,” and the suggestive significant threshold was as the default setting of this package. The regional plots were generated by LocusZoom [21].

2.4. Variant Annotation

We annotated the rs number of candidate variates by ANNOVAR [22] with avsnp150 database. The annotation of nearby genes was according to the NCBI RefSeq hg19 reference panel. In order to compare our results with previous studies, the data of GWAS catalog v1.0.2 (20190322) was downloaded from GWAS catalog website.

2.5. Gene-Based Analysis and Gene-Set Analysis

Since our sample size was very limited, the effects of individual markers may be too weak to detect. Therefore, we applied MAGMA to conduct gene-based analysis to increase the power [23]. This method can analyze multiple genetic markers together to determine their joint effect. Input SNPs were mapped to 18306 protein coding genes; therefore, the Bonferroni significant threshold for gene analysis was using . To understand the biological enriched pathways related with the raloxifene treatment response, we used gene-set analysis by MAGMA. The curated gene sets and GO terms were obtained from MsigDB v6.1. Curated gene sets were collected from 9 data resources including KEGG, Reactome, BioCarta, and biomedical literatures. Variates located within transcription start region and stop region of the gene region will be mapped to that gene. We used 1000 genomes phase 3 as reference genome.

3. Results

3.1. Baseline Characteristics

The distribution of the BMD change percent is shown in Figure 1. After treatment, the BMD response to raloxifene was diverse at both lumbar spine and femoral neck sites in patients. In total, 41 patients were assessed for femoral neck BMD (S Table 1), and the average percentage of BMD change was %. The mean follow-up time was months among all subjects. The mean age of subjects was years, and the average BMI was  kg/m². Among the 41 patients assessed for FN BMD, 34 were also assessed for lumbar spine BMD. The average age and BMI of this subset were similar to the complete group.

Figure 1: The distribution of the BMD change % at the lumbar spine and femoral neck site.

3.2. Association Analysis

We conducted a genome-wide association analysis to reveal the risk loci associated with raloxifene treatment response. A principal component analysis showed that there was no population stratification (S Figure 1). Neither site revealed genome-wide significant association signal of BMD change (S Figures 2, 3). However, there were 112 variants with suggestive significance for LS percent BMD change, while 13 variants showed significance for FN percentage BMD change. The most significant variant in the LS site was rs7768089 (), which was an intronic SNP of FUT9 located on 6q16.1. Meanwhile, rs34311394 at 12q21.2 was the most significant SNP () for the FN site. This SNP is located in the intergenic region between CSRP2 and E2R7. There was no overlap variants among suggestively significant SNPs between the two sites.

3.3. Candidate Genes Cause Decreased BMD in Mutant Mice

Next, a gene-based analysis was conducted by MAGMA. However, none of the genes reached the threshold of statistical significance (Figure 2). The top ten significant genes at the LS and FN sites are shown in Tables 1 and 2. To understand the potential biological functions of these genes, we queried the phenotypes of mutant mice in the International Mouse Phenotype Consortium (IMPC) database. Among the queried genes, GHRHR, ABHD8, and TMPRSS6 showed decreased BMD in a homozygous mutant mouse in both sexes.

(a)

(b)

Figure 2: Manhattan plot of the gene-based test based on response to raloxifene. The red line represented suggestive significant threshold (⁻⁶): (a) Lumbar spine site and (b) Femoral neck site.

Table 1: Top 10 significant genes of gene-based analysis at the lumbar spine site.

Gene
CHR
START
STOP
Number of SNPs
ZSTAT
value
KO mouse bone phenotype

FUT9
6
96463860
96663488
474
4.11

NA

APBB1IP
10
26727132
26856732
304
3.962

NA

MYO15B
17
73584139
73622929
104
3.808

NA

USHBP1
19
17359985
17393595
54
3.728

Vertebral fusion

DNAH5
5
13690440
13944652
691
3.712

No

NRG2
5
139226364
139422884
238
3.709

NA

KIAA0196
8
126036502
126104082
211
3.691

NA

PHYH
10
13319796
13344412
70
3.625

No

ELTD1
1
79355449
79472403
309
3.567

NA

OR4K1
14
20403767
20404842
7
3.545

NA

CHR: chromosome; START: start position of the gene; STOP: stop position of the gene. Knockout mouse bone-related phenotypes were queried from International Mouse Phenotype Consortium.

Table 2: Top 10 significant genes of gene-based analysis at the femoral neck site.

Gene
CHR
START
STOP
Number of SNPs
ZSTAT
value
KO mouse bone phenotype

AC068987.1
12
52203789
52206636
1
3.953

NA

GHRHR
7
30978284
31032869
139
3.859

BMD decrease

F10
13
113777128
113803843
35
3.836

NA

SCN8A
12
51984050
52206648
200
3.674

NA

TMEM128
4
4237269
4249950
84
3.562

NA

MRPL34
19
17403418
17417652
35
3.51

NA

HSPA9
5
137890571
137911133
14
3.501

NA

ABHD8
19
17402940
17421045
42
3.494

BMD decrease

ABLIM2
4
7967039
8160559
488
3.394

NA

TMPRSS6
22
37461476
37505603
97
3.38

BMD decrease

CHR: chromosome; START: start position of the gene; STOP: stop position of the gene. Knockout mouse bone-related phenotypes were queried from International Mouse Phenotype Consortium.

3.4. Enrichment of Osteoblast Differentiation Pathway

We further applied gene-set analysis to reveal the significant enriched gene sets. After FDR adjustment, although none of the pathway achieved the statistical significance (Tables 3 and 4), iron homeostasis for the LS site () was the most significant gene set. Regarding the femoral neck site, the most significant gene set was platelet morphogenesis (). In addition, another gene sets of “osteoblast differentiation by phenylamil up” showed nominal significance for the femoral neck site as well ().

Table 3: Gene-set analysis of raloxifene response at lumbar spine site.

Database
Pathway
Number of genes
Beta
SE
value
value

GO bp
Iron ion homeostasis
63
0.363
0.101

0.884

GO bp
Regulation of immunoglobulin secretion
14
0.774
0.227

0.884

GO bp
Transition metal ion homeostasis
96
0.267
0.08

0.884

Curated gene sets
Inhibition of replication initiation of damaged DNA by rb1 e2f1
10
0.879
0.266

0.884

Curated gene sets
Apoptosis
82
0.28
0.086

0.884

GO bp
Response to protozoan
20
0.569
0.177

0.884

Curated gene sets
Cdh1 targets 3 dn
58
0.331
0.106

0.884

Curated gene sets
Treating iron overload
7
0.879
0.283

0.884

GO bp
Toll like receptor signaling pathway
77
0.3
0.099

0.884

GO bp
Cellular iron ion homeostasis
40
0.374
0.124

0.884

Curated gene sets
Tlx targets 60 hr dn
258
0.148
0.049

0.884

GO bp
Negative regulation of platelet activation
17
0.626
0.211

0.884

Curated gene sets
Tgfb1 signaling via nfic 10 hr dn
29
0.464
0.158

0.884

Curated gene sets
Response to tosedostat 24 hr dn
936
0.074
0.025

0.884

Curated gene sets
Metabolism of amino acids and derivatives
186
0.166
0.057

0.884

GO bp
Cellular defense response
54
0.323
0.111

0.884

Curated gene sets
Akt phosphorylates targets in the cytosol
12
0.559
0.193

0.884

Curated gene sets
Asbcell pathway
9
0.783
0.271

0.884

Curated gene sets
Cell cycle s
152
0.193
0.068

0.884

Curated gene sets
Ar tf pathway
47
0.352
0.124

0.884

GO: gene ontology; bp: biological process; values were from false discovery rate.

Table 4: Gene-set analysis of raloxifene response at femoral neck site.

Database
Pathway
Number of genes
Beta
SE
value
value

GO bp
Platelet morphogenesis
16
0.762
0.208

0.859

GO bp
Regulation of nuclear transcribed mrna catabolic process deadenylation-dependent decay
14
0.633
0.187

0.859

GO bp
Cellular modified amino acid catabolic process
17
0.616
0.185

0.859

Curated gene sets
Osteoblast differentiation by phenylamil up
12
0.779
0.234

0.859

GO bp
Regulation of pri mirna transcription from rna polymerase ii promoter
16
0.691
0.209

0.859

GO bp
Regulation of establishment of protein localization to mitochondrion
118
0.234
0.073

0.966

GO bp
Regulation of protein targeting to mitochondrion
89
0.266
0.083

0.966

Curated gene sets
Werner syndrome and normal aging up
91
0.251
0.08

0.991

Curated gene sets
Aml methylation cluster 6 dn
32
0.418
0.137

0.999

GO bp
Epithelial structure maintenance
21
0.559
0.184

0.999

GO bp
Regulation of substrate adhesion-dependent cell spreading
41
0.379
0.129

0.999

Curated gene sets
Proteolytic cleavage of snare complex proteins
16
0.752
0.26

0.999

GO bp
Cardiac myofibril assembly
16
0.574
0.204

0.999

GO bp
Positive regulation of pri mirna transcription from rna polymerase ii promoter
10
0.702
0.25

0.999

Curated gene sets
Botulinum neurotoxicity
18
0.674
0.241

0.999

GO bp
Negative chemotaxis
38
0.391
0.142

0.999

GO bp
Regulation of endocrine process
44
0.348
0.126

0.999

Curated gene sets
Chondroitin sulfate biosynthesis
17
0.507
0.184

0.999

GO bp
Cellular metabolic compound salvage
35
0.36
0.131

0.999

GO bp
Cytokinesis
80
0.246
0.09

0.999

GO: gene ontology; bp: biological process; values were from false discovery rate.

3.5. Comparison with Previous Candidate Variants

Since some variants have been reported to be associated with response to raloxifene, we next focused two SNPs within ESR1 (rs9340799 and rs2234693) and one SNP in UGT1A8 (rs1042597). As shown in Supplementary Table 2, none of the SNPs demonstrated nominal significance () at the LS or FN site. We further extracted larger regions around these two genes (±500 Mb) of two phenotypes and generated regional plots. However, no variant within these regions reached suggestive significance (S Figure 4-7).

4. Discussion

This study used two skeletal positions to assess the relationships between BMD response to raloxifene and gene network. Although statistical power was limited sample size, several candidates including GHRHR, ABHD8, and TMPRSS6 still addressed significance after gene-based analysis. These three genes were also associated with decreased BMD in mutant mouse models. From gene-set analysis, we found that iron homeostasis and osteoblast differentiation gene sets were enriched. Previous studies indicated that iron overload affects the bone phenotype and increases risk of bone fracture. Iron overload is an important factor that is involved in the activation of osteoclast, the differentiation process, and the reduction of osteoblast formation [24, 25]. Besides, in patients with thalassemia major, which is often characterized by low BMD values and iron overload, the circulating IGF-1 levels and its binding protein are lower than those in the healthy individuals [26]. Thus, IGF-1 is critical in bone remodeling. The lower level of IGF-1 may increase the risk of hip fracture [27]. Taken together, iron overload may increase bone resorption and decrease bone formation through osteoblast and osteoclast activity or through the endocrine growth hormone- (GH-) IGF-1 axis pathway.

Heilberg et al. reported that the rs9340799 (Xbal) AA genotype and rs2234693 (PvuII) CC genotype of the ESR1 have higher LS bone density after one-year treatment of raloxifene in Hispanic women [14]. Conversely, Mondockova et al. revealed that the AA genotype of rs9340799 and TT genotype of rs2234693 showed worse treatment response to raloxifene at the LS site in Slovak women [13]. Because of the inconsistent findings, we tried to confirm it by using this study. However, none of the variants were associated with the response to raloxifene in the current study. In another study, individuals with the CC genotype of the missense SNP (Ala173Gly) of UGT1A8 revealed more improvement in negative symptoms after raloxifene treatment [17]. However, this variant cannot be replicated in the current study as well. We found that the most significant association with drug response at LS site was an intronic SNP of fucosyltransferase 9 (FUT9), rs7768089. This gene is a member of the glycosyltransferase family, and it participates in the biosynthesis of Lewis antigen. HaploReg v4.1 demonstrated that rs7768089 may alter the transcription factor (Foxa disc2, Nrd-2 3, and TCF11::MafG) binding affinity, and according to GTEx, this SNP is an expression quantitative locus (eQTL) of FUT9 in the pancreas and brain tissue (S Table 3). However, the mechanistic relationship between FUT9 and BMD remains to be clarified.

For the FN site, the most significant SNP (rs34311394) was located within the intron between CSRP2 and E2F7. CSRP2 (cysteine and glycine rich protein 2) is involved in development and cellular differentiation, and E2F7 (E2F transcription factors) participates in the regulation of cell cycle progression. This SNP is associated with the expression of CSRP2 in the brain tissue (S Table 3). The most significant association revealed by a gene-based study was GHRHR (growth hormone-releasing hormone receptor). Although no traits related to BMD or drug response have been reported, the Ghrhrtm1.1(KOMP)Vlcg mouse model showed decreased bone mineral density according to the IMPC database. Based on the results in GWAS catalog, the genetic variants of GHRHR associated with congenital left sided heart lesions and adolescent idiopathic scoliosis. Further functional studies are needed to determine the plausible impact of these candidate genes on the bone.

Due to its estrogenic effects, raloxifene may increase the risk of thromboembolic events, such as deep vein thrombosis and pulmonary embolism. Estrogen increases the concentration of plasma fibrinogen and coagulation factors and enhances the aggregation of platelets [28]. From the gene-set analysis for the femoral neck site, the most significant treatment-associated gene-set was platelet morphogenesis. These results hint that genetic variants could possibly play a role in the occurrence of side effects. Thus, further studies may focus on the pharmacogenomics of thromboembolic events in raloxifene users. The main limitation of the current study is the sample size which is not able to confer sufficient statistical power to reach genome-wide significance. Larger cohorts are needed to replicate our results. Another limitation is the in the study. The majority of subjects are elderly patients with a mean age of 72.4 years. Previous studies have reported that bone loss may accelerate in the individuals who are over 70 years old [29]. In addition, aging influences GH and IGF-1 serum levels [27], which might result in the change of BMD levels and the pharmacological effects. Further studies should also consider the age, the concentration of raloxifene in the blood, and the drug compliance for each patient.

5. Conclusions

In summary, we found plausible gene sets in iron ion homeostasis, osteoblast differentiation, and platelet morphogenesis pathways that may influence raloxifene responses. These findings offer new important insights and warrant further investigations into the pharmacogenomics of raloxifene.

Data Availability

The data used to support the findings of this study are included within the article.

Ethical Approval

The study was approved by the Joint Institutional Review Board of Taipei Medical University (201210039).

Consent

All subjects provided written informed consent. All subjects provided written informed consent.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Authors’ Contributions

Wei-Chiao Chang and Kuo-Sheng Hung are responsible for the experimental design. Hsing-Fang Lu and Wan-Hsuan Chou performed the implementation of experiments. Hsing-Fang Lu analyzed the data. Kuo-Sheng Hung, Po-Hsin Chou, Shih-Tien Wang, and Wei-Chiao Chang are involved in the contribution of reagents/materials/analysis tools. Wei-Chiao Chang, Hsing-Fang Lu, Gan-Hong Lin, Wirawan Adikusuma, and Eko Mugiyanto interpreted the data. Kuo-Sheng Hung, Po-Hsin Chou, Shih-Tien Wang, and Hsing-Fang Lu are responsible for the clinical samples and data collection. Hsing-Fang Lu and Wei-Chiao Chang wrote the paper.

Acknowledgments

This work was supported by grants from the Ministry of Science and Technology in Taiwan (MOST 105-2628-B-038-001-MY4 and MOST 106-2923-B-038-002) and the Higher Education Sprout Project by the Ministry of Education, Taiwan (MOE DP2-107-21121-A-05).

Supplementary Materials

Figure S1: evaluation of population stratification by principal component analysis. Figure S2: genome-wide association of response to raloxifene at lumbar spine bone mineral density. Figure S3: genome-wide association of response to raloxifene at femoral neck bone mineral density. Figure S4: value of ESR from response to raloxifene at LS site GWAS. Figure S5: value of UGT1A8 from response to raloxifene at LS site GWAS. Figure S6: value of ESR from response to raloxifene at FN site GWAS. Figure S7: value of UGT1A8 from response to raloxifene at FN site GWAS. Table S1: baseline characteristics of inclusion individuals. Table S2: variants reported to be related to raloxifene response previously Table S3: eQTL evidence of most significant SNPs. (Supplementary Materials)

References

M. Lorentzon and S. R. Cummings, “Osteoporosis: the evolution of a diagnosis,” Journal of Internal Medicine, vol. 277, no. 6, pp. 650–661, 2015.

P. H. Chen, M. S. Lin, T. J. Huang, and M. Y. Chen, “Prevalence of and factors associated with adopting bone health promoting behaviours among people with osteoporosis in Taiwan: a cross-sectional study,” BMJ Open, vol. 7, no. 9, article e015980, 2017.

G. Hendrickx, E. Boudin, and W. Van Hul, “A look behind the scenes: the risk and pathogenesis of primary osteoporosis,” Nature Reviews Rheumatology, vol. 11, no. 8, pp. 462–474, 2015.

K. Estrada, U. Styrkarsdottir, E. Evangelou et al., “Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture,” Nature Genetics, vol. 44, no. 5, pp. 491–501, 2012.

H.‐. F. Lu, K.-S. Hung, H.-W. Chu et al., “Meta-analysis of genome-wide association studies identifies three loci associated with stiffness index of the calcaneus,” Journal of Bone and Mineral Research, vol. 34, no. 7, pp. 1275–1283, 2019.

I. Kou, A. Takahashi, T. Urano et al., “Common variants in a novel gene, FONG on chromosome 2q33.1 confer risk of osteoporosis in Japanese,” PLoS One, vol. 6, no. 5, article e19641, 2011.

H.-F. Lu, K.-S. Hung, Y.-W. Hsu et al., “Association study between the FTCDNL1 (FONG) and susceptibility to osteoporosis,” PLoS One, vol. 10, no. 10, article e0140549, 2015.

N. C. Wright, A. C. Looker, K. G. Saag et al., “The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine,” Journal of Bone and Mineral Research, vol. 29, no. 11, pp. 2520–2526, 2014.

D. Weycker, X. Li, R. Barron, R. Bornheimer, and D. Chandler, “Hospitalizations for osteoporosis-related fractures: economic costs and clinical outcomes,” Bone Reports, vol. 5, pp. 186–191, 2016.

S. H. Tella and J. C. Gallagher, “Prevention and treatment of postmenopausal osteoporosis,” The Journal of Steroid Biochemistry and Molecular Biology, vol. 142, pp. 155–170, 2014.

S. R. Cummings, S. Eckert, K. A. Krueger et al., “The effect of raloxifene on risk of breast cancer in postmenopausal women,” JAMA, vol. 281, no. 23, pp. 2189–2197, 1999.

P. D. Delmas, N. H. Bjarnason, B. H. Mitlak et al., “Effects of raloxifene on bone mineral density, serum cholesterol concentrations, and uterine endometrium in postmenopausal women,” New England Journal of Medicine, vol. 337, no. 23, pp. 1641–1647, 1997.

V. Mondockova, M. Adamkovicova, M. Lukacova et al., “The estrogen receptor 1 gene affects bone mineral density and osteoporosis treatment efficiency in Slovak postmenopausal women,” BMC Medical Genetics, vol. 19, no. 1, p. 174, 2018.

I. P. Heilberg, E. Hernandez, E. Alonzo et al., “Estrogen receptor (ER) gene polymorphism may predict the bone mineral density response to raloxifene in postmenopausal women on chronic hemodialysis,” Renal Failure, vol. 27, no. 2, pp. 155–161, 2009.

D. C. Kemp, P. W. Fan, and J. C. Stevens, “Characterization of raloxifene glucuronidation in vitro: contribution of intestinal metabolism to presystemic clearance,” Drug Metabolism and Disposition, vol. 30, no. 6, pp. 694–700, 2002.

D. Sun, N. R. Jones, A. Manni, and P. Lazarus, “Characterization of raloxifene glucuronidation: potential role of UGT1A8 genotype on raloxifene metabolism in vivo,” Cancer Prevention Research, vol. 6, no. 7, pp. 719–730, 2013.

J. Labad, L. Martorell, E. Huerta-Ramos et al., “Pharmacogenetic study of the effects of raloxifene on negative symptoms of postmenopausal women with schizophrenia: a double-blind, randomized, placebo-controlled trial,” European Neuropsychopharmacology, vol. 26, no. 10, pp. 1683–1689, 2016.

J. M. Kwon and A. M. Goate, “The candidate gene approach,” Alcohol Research & Health, vol. 24, no. 3, pp. 164–168, 2000.

L. E. Duncan, M. Ostacher, and J. Ballon, “How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete,” Neuropsychopharmacology, vol. 44, no. 9, pp. 1518–1523, 2019.

S. Purcell, B. Neale, K. Todd-Brown et al., “PLINK: a tool set for whole-genome association and population-based linkage analyses,” American Journal of Human Genetics, vol. 81, no. 3, pp. 559–575, 2007.

R. J. Pruim, R. P. Welch, S. Sanna et al., “LocusZoom: regional visualization of genome-wide association scan results,” Bioinformatics, vol. 26, no. 18, pp. 2336-2337, 2010.

K. Wang, M. Li, and H. Hakonarson, “ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data,” Nucleic Acids Research, vol. 38, no. 16, pp. e164–e164, 2010.

C. A. de Leeuw, J. M. Mooij, T. Heskes, and D. Posthuma, “MAGMA: generalized gene-set analysis of GWAS data,” PLoS Computational Biology, vol. 11, no. 4, article e1004219, 2015.

E. Balogh, G. Paragh, and V. Jeney, “Influence of iron on bone homeostasis,” Pharmaceuticals, vol. 11, no. 4, p. 107, 2018.

V. Jeney, “Clinical impact and cellular mechanisms of Iron overload-associated bone loss,” Frontiers in Pharmacology, vol. 8, no. 77, 2017.

A. Gaudio, N. Morabito, A. Catalano, R. Rapisarda, A. Xourafa, and A. Lasco, “Pathogenesis of thalassemia major-associated osteoporosis: a review with insights from clinical experience,” Journal of Clinical Research in Pediatric Endocrinology, vol. 11, no. 2, pp. 110–117, 2019.

P. P. M. M. Geusens and S. Boonen, “Osteoporosis and the growth hormone-insulin-like growth factor axis,” Hormone Research, vol. 58, no. 3, pp. 49–55, 2002.

J. Bonnar, “Coagulation effects of oral contraception,” American Journal of Obstetrics and Gynecology, vol. 157, no. 4, pp. 1042–1048, 1987.

C. Berger, L. Langsetmo, L. Joseph et al., “Change in bone mineral density as a function of age in women and men and association with the use of antiresorptive agents,” CMAJ, vol. 178, no. 13, pp. 1660–1668, 2008.