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Liu J, Xia X, Wang Z, Wang Y, Qin G. Osteosarcopenia, osteoarthritis and frailty: a two-sample Mendelian randomization study. Aging Clin Exp Res 2025; 37:132. [PMID: 40257716 PMCID: PMC12011954 DOI: 10.1007/s40520-025-03012-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Accepted: 03/15/2025] [Indexed: 04/22/2025]
Abstract
BACKGROUND Musculoskeletal disease, which has a complicated relationship with frailty, is a common clinical problem among elderly individuals. AIMS This study evaluated the potential causal relationships between osteosarcopenia, osteoarthritis and frailty by Mendelian Randomization (MR) analysis. METHODS This study employed a two-sample MR approach to investigate the causal relationships among osteosarcopenia, osteoarthritis and frailty. Published summary statistics were used to obtain instrumental variables at the genome-wide significance level. RESULTS Among the age groups with osteoporosis, high total bone mineral density (TBMD) (45-60, OR = 0.966, 95% CI 0.940-0.993, P = 0.013) and TBMD (over 60, OR = 0.974, 95% CI 0.954-0.994, P = 0.011) reduced the risk of frailty. Similarly, high forearm BMD (FA-BMD), high ultradistal forearm BMD (UFA-BMD), and high Heel-BMD at different sites also reduced the risk of frailty (OR = 0.966, 95% CI 0.936-0.996, P = 0.028; OR = 0.975, 95% CI 0.953-0.997, P = 0.029; OR = 0.981, 95% CI 0.967-0.995, P = 0.008). Among the characteristics related to sarcopenia, grip strength in the left hand, grip strength in the right hand, appendicular lean mass, and walking pace were all protective factors for frailty (OR = 0.788, 95% CI 0.721-0.862, P < 0.001; OR = 0.800, 95% CI 0.737-0.869, P < 0.001; OR = 0.955, 95% CI 0.937-0.974, P = 0.000; OR = 0.480, 95% CI 0.388-0.593, P < 0.001), with low grip strength in those over 60 years of age significantly positively correlated with frailty (OR = 1.168, 95% CI 1.059-1.289, P = 0.002). The MR results of osteoarthritis and frailty revealed a causal relationship between specific joint sites and frailty, including KOA (OR = 1.086, 95% CI 1.017-1.160, P = 0.014), HOA (OR = 1.028, 95% CI 1.007-1.049, P = 0.009), and KOA/HOA (OR = 1.082, 95% CI 1.053-1.113, P = 0.000), increasing the risk of frailty. CONCLUSION Osteosarcopenia, osteoarthritis and frailty exhibit significant causal effects, rendering them risk factors for frailty. Therefore, in clinical practice, patients with osteosarcopenia and osteoarthritis should be required to undergo relevant interventions to reduce the risk of frailty.
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Affiliation(s)
- Jili Liu
- Department of Geriatrics, The First Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Xin Xia
- The Center of Gerontology and Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province, 610041, China
| | - Zhaolin Wang
- Department of Traditional Chinese Medicine, The Second Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Yanqin Wang
- Department of Hematology, Shanxi Hospital of Traditional Chinese Medicine, Taiyuan, Shanxi Province, 030012, China
| | - Gang Qin
- Department of Geriatrics, The First Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China.
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Yang Q, Huang L, Xu H, Feng J, Liu D, Wei S, Jiang H. Gastroesophageal reflux disease and osteoporosis: A bidirectional Mendelian randomization study. Medicine (Baltimore) 2025; 104:e42083. [PMID: 40193675 PMCID: PMC11977714 DOI: 10.1097/md.0000000000042083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2024] [Revised: 03/15/2025] [Accepted: 03/20/2025] [Indexed: 04/09/2025] Open
Abstract
In observational studies, associations between osteoporosis (OP) and gastroesophageal reflux disease (GERD) have been found. We conducted a 2-way, 2-sample Mendelian randomization (MR) analysis to determine whether these associations have a causal relationship. Data on GERD at the summary-level were sourced from extensive genome-wide association studies encompassing 129,080 cases and 473,524 control subjects. Bone mineral density (BMD) served as the phenotypic indicator for OP. BMD metrics were compiled from a cohort of 537,750 individuals, encompassing total body BMD (TB-BMD) and stratified TB-BMD across age groups, along with BMD measurements at 4 anatomical locations: lumbar spine, femoral neck, heel, and ultra-distal forearm. Multiple MR approaches, such as the inverse-variance weighted (IVW) method, MR-Egger regression, and the MR-PRESSO test, were employed, among which findings obtained by IVW method were designated as the primary outcomes. For quality assurance, sensitivity analyses were conducted using the MR-Egger intercept, Cochran Q, and leave-one-out test. There were no significant causal links between genetic inclination towards GERD and reduced BMD levels. Nonetheless, the genetic evidence suggests a causal link between higher BMD levels and lower incidence of GERD [TB-BMD: OR = 0.941, 95% confidence intervals (CI) = 0.910-0.972, P < .001; TB-BMD-1: OR = 0.919, 95% CI = 0.885-0.954, P < .001; TB-BMD-3: OR = 0.945, 95% CI = 0.915-0.977, P = .001; TB-BMD-4: OR = 0.926, 95% CI = 0.896-0.957, P < .001]. Sensitivity analyses corroborate our findings. The MR analysis indicates no significant causal link between genetic inclination towards GERD and OP or reduced BMD within the European demographic. In addition, the study suggests that lower BMD or OP, as predicted by genetics, may contribute to the development of GERD.
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Affiliation(s)
- Qinghua Yang
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Longao Huang
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hongyuan Xu
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Junfei Feng
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dun Liu
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shengwang Wei
- Department of Orthopedics, The Fourth Affiliated Hospital of Guangxi Medical University/Liuzhou Worker’s Hospital, Liuzhou, China
| | - Hua Jiang
- Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Chopra A, Song J, Weiner 3rd J, Beule D, Schaefer AS. Genetic analysis of cis-enhancers associated with bone mineral density and periodontitis in the gene SOST. PLoS One 2025; 20:e0319259. [PMID: 40127057 PMCID: PMC11932464 DOI: 10.1371/journal.pone.0319259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 01/27/2025] [Indexed: 03/26/2025] Open
Abstract
A haplotype block at the sclerostin (SOST) gene correlates with bone mineral density (BMD) and increased periodontitis risk in smokers. Investigating the putative causal variants within this block, our study aimed to elucidate the impact of linked enhancer elements on gene expression and to evaluate their role in transcription factor (TF) binding. Using CRISPR/dCas9 activation (CRISPRa) screening in SaOS-2 cells, we quantified disease-related enhancer activities regulating SOST expression. Additionally, in SaOS-2 cells, we investigated the influence of the candidate TFs CCAAT/enhancer-binding protein beta (CEBPB) on gene expression by antisense (GapmeR) knockdown, followed by RNA sequencing. The periodontitis-linked SNP rs9783823 displayed a significant cis-activating effect (25-fold change in SOST expression), with the C-allele containing a CEBPB binding motif (position weight matrix (PWM) = 0.98, Pcorrected = 7.7 x 10-7). CEBPB knockdown induced genome-wide upregulation but decreased epithelial-mesenchymal transition genes (P = 0.71, AUC = 2.2 x 10-11). This study identifies a robust SOST cis-activating element linked to BMD and periodontitis, carrying CEBPB binding sites, and highlights CEBPB's impact on epithelial-mesenchymal transition.
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Affiliation(s)
- Avneesh Chopra
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jiahui Song
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | | | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany
| | - Arne S. Schaefer
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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Martínez-Gil N, Herrera-Ubeda C, Gritti N, Roca-Ayats N, Ugartondo N, Garcia-Giralt N, Ovejero D, Nogués X, Garcia-Fernàndez J, Grinberg D, Balcells S. Regulation of WNT16 in bone may involve upstream enhancers within CPED1. Sci Rep 2025; 15:9607. [PMID: 40113825 PMCID: PMC11926113 DOI: 10.1038/s41598-025-93259-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Accepted: 03/05/2025] [Indexed: 03/22/2025] Open
Abstract
WNT16 stands up as an essential gene for bone homeostasis. Here, we present new evidence of the functional role of a particular region within WNT16. Performing 4 C chromatin conformation analysis in three osteoblast-related cells (the human fetal osteoblast hFOB 1.19 cell line, Saos 2 osteosarcoma cell line and mesenchymal Stem Cells -MSC-), we identify physical interactions between the proximal part of WNT16 intron 2, shown here to be an active promoter in Saos 2 osteosarcoma cells, and several putative regulatory regions within CPED1. Analysis of previously published RNA-seq data from hFOB cells disclosed low expression of a region located downstream of this promoter. Our results suggest a novel regulatory mechanism of WNT16 in bone, mediated by physical interaction with various enhancer regions within CPED1.
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Affiliation(s)
- N Martínez-Gil
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - C Herrera-Ubeda
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - N Gritti
- European Molecular Biology Laboratory (EMBL) Barcelona, 08003, Barcelona, Spain
| | - N Roca-Ayats
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - N Ugartondo
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - N Garcia-Giralt
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - D Ovejero
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - X Nogués
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - J Garcia-Fernàndez
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - Daniel Grinberg
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain.
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain.
| | - Susanna Balcells
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain.
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain.
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Yang F, Wen J. Association between bone mineral density and scoliosis: a two-sample mendelian randomization study in european populations. Hereditas 2024; 161:57. [PMID: 39736789 DOI: 10.1186/s41065-024-00352-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Accepted: 11/19/2024] [Indexed: 01/01/2025] Open
Abstract
BACKGROUND Previous studies have shown that bone mineral density (BMD) has a certain impact on scoliosis. However, up to now, there is no clear evidence that there is a causal association between the two. The aim of this study is to investigate whether there is a causal association between BMD at different body positions and scoliosis by two-sample Mendelian randomization (MR). METHODS Genetic variants (SNPS) strongly associated with BMD (total body BMD (TB-BMD), lumbar spine BMD (LS-BMD), femoral neck BMD (FN-BMD), heel BMD (HE-BMD), and forearm BMD (FA-BMD)) were extracted from GEFOS and genome-wide association analysis (GWAS) databases SNPs) were used as instrumental variables (IVs). Scoliosis was also selected from the Finnish database as the outcome. Inverse variance weighting (IVW) method was used as the main analysis method, and multiple sensitivity analysis was performed by combining weighted median, MR-Egger, MR Multi-effect residuals and outliers. RESULTS IVW results showed that TB-BMD (OR = 0.83, 95%CI: 0.66-1.55 P = 0.13), LS-BMD (OR = 0.72, 95%CI: 0.52-0.99, P = 0.04), FN-BMD (OR = 0.74, 95%CI: 0.50-1.09, P = 0.13), FA-BMD (OR = 0.95,95%CI: 0.70-1.28, P = 0.75), HE-BMD (OR = 0.91, 95%CI: 0.77-1.08, P = 0.29). Sensitivity analyses showed no evidence of pleiotropy or heterogeneity (p > 0.05) (MR-PRESSO and Cochrane). The results were further validated by leave-one-out test and MR-Egger intercept, which confirmed the robustness of the study results. CONCLUSION In conclusion, the present study demonstrates that the causal role of genetic prediction of scoliosis increases with decreasing lumbar BMD. There was no evidence that BMD at the remaining sites has a significant causal effect on scoliosis. Our results suggest that the lumbar spine BMD should be routinely measured in the population at high risk of scoliosis. If osteoporosis occurs, appropriate treatment should be given to reduce the incidence of scoliosis. CLINICAL TRIAL NUMBER Not applicable.
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Affiliation(s)
- Fangjun Yang
- Department of orthopedic, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China
| | - Jiantao Wen
- Department of Pediatric Spine Surgery, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China.
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Xu T, Li C, Liao Y, Xu Y, Fan Z, Zhang X. Is there a causal relationship between resistin levels and bone mineral density, fracture occurrence? A mendelian randomization study. PLoS One 2024; 19:e0305214. [PMID: 39190724 DOI: 10.1371/journal.pone.0305214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/25/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND In a great many of observational studies, whether there is a relevance of resistin levels on bone mineral density (BMD) and fracture occurrence has been inconsistently reported, and the causality is unclear. METHODS We aim to assess the resistin levels on BMD and fracture occurrence within a Mendelian randomization (MR) analysis. Exposure and outcome data were derived from the Integrative Epidemiology Unit (IEU) Open genome wide association studies (GWAS) database. Screening of instrumental variables (IVs) was performed subject to conditions of relevance, exclusivity, and independence. Inverse variance weighting (IVW) was our primary method for MR analysis based on harmonized data. Weighted median and MR-Egger were chosen to evaluate the robustness of the results of IVW. Simultaneously, heterogeneity and horizontal pleiotropy were also assessed and the direction of potential causality was detected by MR Steiger. Multivariable MR (MVMR) analysis was used to identify whether confounding factors affected the reliability of the results. RESULTS After Bonferroni correction, the results showed a suggestively positive causality between resistin levels and total body BMD (TB-BMD) in European populations over the age of 60 [β(95%CI): 0.093(0.021, 0.165), P = 0.011]. The weighted median [β(95%CI): 0.111(0.067, 0.213), P = 0.035] and MR-Egger [β(95%CI): 0.162(0.025, 0.2983), P = 0.040] results demonstrate the robustness of the IVW results. No presence of pleiotropy or heterogeneity was detected between them. MR Steiger supports the causal inference result and MVMR suggests its direct effect. CONCLUSIONS In European population older than 60 years, genetically predicted higher levels of resistin were associated with higher TB-BMD. A significant causality between resistin levels on BMD at different sites, fracture in certain parts of the body, and BMD in four different age groups between 0-60 years of age was not found in our study.
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Affiliation(s)
- Taichuan Xu
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
| | - Chao Li
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
| | - Yitao Liao
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
| | - Yenan Xu
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
| | - Zhihong Fan
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
| | - Xian Zhang
- Department of Spine, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine, Wuxi, Jiangsu, China
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Xu F, Zhang X, Zhang Y, Chen W, Liao Z. Causal Relationship of Obstructive Sleep Apnea with Bone Mineral Density and the Role of BMI. Nat Sci Sleep 2024; 16:325-333. [PMID: 38533250 PMCID: PMC10964782 DOI: 10.2147/nss.s443557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/03/2024] [Indexed: 03/28/2024] Open
Abstract
Background Observational studies have yielded conflicting evidence concerning the relationships between obstructive sleep apnea (OSA) and bone mineral density (BMD). As the exact causal inferences remain inconclusive, we conducted a two-sample Mendelian randomization (MR) to identify the causal associations between OSA and BMD. Methods Single-nucleotide polymorphisms associated with OSA were extracted from the FinnGen study. Summary statistics for 10 BMD measured at different age or skeletal sites were obtained from the publicly available IEU GWAS database. Inverse-variance weighted (IVW) method was chosen as the primary analysis, combined with several sensitivity analyses to evaluate the robustness of results. The study design included two-sample MR and network MR. Results Our primary MR analysis revealed that genetically predicted OSA was positively linked to increased forearm BMD (β = 0.24, 95% confidence interval [CI]: 0.06-0.41, p = 0.009) and heel BMD (β=0.10, 95% CI = 0.02-0.18, p = 0.018), while no significant causal relationships were observed between OSA and total body BMD, lumbar spine BMD, or femoral neck BMD (all p > 0.05). Network MR suggests that OSA might act as a mediating factor in the effect of BMI on forearm BMD and heel BMD, with a mediated portion estimated at 73% and 84%, respectively. Conclusion Our findings provide support for a causal relationship between genetically predicted OSA and increased forearm BMD and heel BMD. Furthermore, our results suggest that OSA may play a role in mediating the influence of BMI on BMD.
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Affiliation(s)
- Fei Xu
- General Surgery Department, Zhongshan Boai Hospital, Zhongshan, Guangdong, 528400, People’s Republic of China
| | - XiuRong Zhang
- Breast Surgery Department, Zhongshan Boai Hospital, Zhongshan, Guangdong, 528400, People’s Republic of China
| | - YinRong Zhang
- General Surgery Department, Zhongshan Boai Hospital, Zhongshan, Guangdong, 528400, People’s Republic of China
| | - WenHui Chen
- Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, People’s Republic of China
| | - ZiCong Liao
- General Surgery Department, Zhongshan Boai Hospital, Zhongshan, Guangdong, 528400, People’s Republic of China
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Ye Y, Zhong R, Xiong XM, Wang CE. Association of coffee intake with bone mineral density: a Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1328748. [PMID: 38572474 PMCID: PMC10987693 DOI: 10.3389/fendo.2024.1328748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024] Open
Abstract
Background In observational studies, the relationship between coffee intake and bone mineral density (BMD) is contradictory. However, residual confounding tends to bias the results of these studies. Therefore, we used a two-sample Mendelian randomization (MR) approach to further investigate the potential causal relationship between the two. Methods Genetic instrumental variables (IVs) associated with coffee intake were derived from genome-wide association studies (GWAS) of the Food Frequency Questionnaire (FFQ) in 428,860 British individuals and matched using phenotypes in PhenoScanner. Summarized data on BMD were obtained from 537,750 participants, including total body BMD (TB-BMD), TB-BMD in five age brackets ≥60, 45-60, 30-45, 15-30, and 0-15 years, and BMD in four body sites: the lumbar spine, the femoral neck, the heel, and the ultradistal forearm. We used inverse variance weighting (IVW) methods as the primary analytical method for causal inference. In addition, several sensitivity analyses (MR-Egger, Weighted median, MR-PRESSO, Cochran's Q test, and Leave-one-out test) were used to test the robustness of the results. Results After Bonferroni correction, Coffee intake has a potential positive correlation with total body BMD (effect estimate [Beta]: 0.198, 95% confidence interval [Cl]: 0.05-0.35, P=0.008). In subgroup analyses, coffee intake was potentially positively associated with TB-BMD (45-60, 30-45 years) (Beta: 0.408, 95% Cl: 0.12-0.69, P=0.005; Beta: 0.486, 95% Cl: 0.12-0.85, P=0.010). In addition, a significant positive correlation with heel BMD was also observed (Beta: 0.173, 95% Cl: 0.08-0.27, P=0.002). The results of the sensitivity analysis were generally consistent. Conclusion The results of the present study provide genetic evidence for the idea that coffee intake is beneficial for bone density. Further studies are needed to reveal the biological mechanisms and offer solid support for clinical guidelines on osteoporosis prevention.
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Affiliation(s)
- Yang Ye
- Department of Spinal Surgery, Affiliated Sports Hospital of Chengdu Sport University, Chengdu, China
| | - Rui Zhong
- Department of Spinal Surgery, Affiliated Sports Hospital of Chengdu Sport University, Chengdu, China
- School of Sports Medicine and Health, Chengdu Sports University, Chengdu, China
| | - Xiao-ming Xiong
- Department of Spinal Surgery, Affiliated Sports Hospital of Chengdu Sport University, Chengdu, China
- School of Sports Medicine and Health, Chengdu Sports University, Chengdu, China
| | - Chuan-en Wang
- Department of Spinal Surgery, Affiliated Sports Hospital of Chengdu Sport University, Chengdu, China
- School of Sports Medicine and Health, Chengdu Sports University, Chengdu, China
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Wang Y, Huang X, Zhang Q, Cheng C, Qin Z, Lu L, Huang Q. The osteoporosis susceptibility SNP rs188303909 at 2q14.2 regulates EN1 expression by modulating DNA methylation and E2F6 binding. J Mol Med (Berl) 2024; 102:273-284. [PMID: 38153509 DOI: 10.1007/s00109-023-02412-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/11/2023] [Accepted: 12/19/2023] [Indexed: 12/29/2023]
Abstract
EN1 encodes a homeodomain-containing transcription factor and is a determinant of bone density and fracture. Previous powerful genome-wide association studies (GWASs) have identified multiple single-nucleotide polymorphisms (SNPs) near EN1 at 2q14.2 locus for osteoporosis, but the causal SNPs and functional mechanisms underlying these associations are poorly understood. The target genes regulated by the transcription factor EN1 are also unclear. In this study, we identified rs188303909, a functional CpG-SNP, as a causal SNP for osteoporosis at 2q14.2 through the integration of functional and epigenomic analyses. Functional experiments demonstrated that unmethylated rs188303909 acted as a strong allele-specific distal enhancer to regulate EN1 expression by modifying the binding of transcription factor E2F6, but rs188303909 methylation attenuated the active effect of E2F6 on EN1 expression. Importantly, transcription factor EN1 could differentially bind osteoporosis GWAS lead SNPs rs4869739-T and rs4355801-G to upregulate CCDC170 and COLEC10 expression, thus promoting bone formation. Our study provided a mechanistic insight into expression regulation of the osteoporosis susceptibility gene EN1, which could be a potential therapeutic target for osteoporosis precision medicine. KEY MESSAGES: CpG-SNP rs188303909 is a causal SNP at the osteoporosis susceptibility locus 2q14.2. Rs188303909 distally regulates EN1 expression by modulating DNA methylation and E2F6 binding. EN1 upregulates CCDC170 and COLEC10 expression through osteoporosis GWAS lead SNPs rs4869739 and rs4355801.
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Affiliation(s)
- Ya Wang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Xinyao Huang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Qiongdan Zhang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Chen Cheng
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Zixuan Qin
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Li Lu
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Qingyang Huang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China.
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10
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Zhou S, Sosina OA, Bovijn J, Laurent L, Sharma V, Akbari P, Forgetta V, Jiang L, Kosmicki JA, Banerjee N, Morris JA, Oerton E, Jones M, LeBlanc MG, Idone V, Overton JD, Reid JG, Cantor M, Abecasis GR, Goltzman D, Greenwood CMT, Langenberg C, Baras A, Economides AN, Ferreira MAR, Hatsell S, Ohlsson C, Richards JB, Lotta LA. Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis. Nat Genet 2023; 55:1277-1287. [PMID: 37558884 DOI: 10.1038/s41588-023-01444-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/09/2023] [Indexed: 08/11/2023]
Abstract
In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.
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Affiliation(s)
- Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
| | - Olukayode A Sosina
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Jonas Bovijn
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Laetitia Laurent
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Vasundhara Sharma
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Parsa Akbari
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Vincenzo Forgetta
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Five Prime Sciences Inc, Montréal, Québec, Canada
| | - Lai Jiang
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Jack A Kosmicki
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Nilanjana Banerjee
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Marcus Jones
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Michelle G LeBlanc
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - John D Overton
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Jeffrey G Reid
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Michael Cantor
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Goncalo R Abecasis
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - David Goltzman
- Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris N Economides
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Manuel A R Ferreira
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - Claes Ohlsson
- Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada.
- Five Prime Sciences Inc, Montréal, Québec, Canada.
- Department of Twin Research, King's College London, London, UK.
| | - Luca A Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
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11
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Chen S, He W. Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Bone Mineral Density. Calcif Tissue Int 2023; 112:543-562. [PMID: 36877247 DOI: 10.1007/s00223-023-01069-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/01/2023] [Indexed: 03/07/2023]
Abstract
Mounting evidence has supported osteoporosis (OP) as a metabolic disorder. Recent metabolomics studies have discovered numerous metabolites related to bone mineral density (BMD). However, the causal effects of metabolites on BMD at distinct sites remained underexplored. Leveraging genome-wide association datasets, we conducted two-sample Mendelian randomization (MR) analyses to investigate the causal relationship between 486 blood metabolites and bone mineral density at five skeletal sites including heel (H), total body (TB), lumbar spine (LS), femoral neck (FN), and ultra-distal forearm (FA). Sensitivity analyses were performed to test the presence of the heterogeneity and the pleiotropy. To exclude the influences of reverse causation, genetic correlation, and linkage disequilibrium (LD), we further performed reverse MR, linkage disequilibrium regression score (LDSC), and colocalization analyses. In the primary MR analyses, 22, 10, 3, 7, and 2 metabolite associations were established respectively for H-BMD, TB-BMD, LS-BMD, FN-BMD, and FA-BMD at the nominal significance level (IVW, P < 0.05) and passing sensitivity analyses. Among these, one metabolite, androsterone sulfate showed a strong effect on four out of five BMD phenotypes (Odds ratio [OR] for H-BMD = 1.045 [1.020, 1.071]; Odds ratio [OR] for TB-BMD = 1.061 [1.017, 1.107]; Odds ratio [OR] for LS-BMD = 1.088 [1.023, 1.159]; Odds ratio [OR] for FN-BMD = 1.114 [1.054, 1.177]). Reverse MR analysis provided no evidence for the causal effects of BMD measurements on these metabolites. Colocalization analysis have found that several metabolite associations might be driven by shared genetic variants such as mannose for TB-BMD. This study identified some metabolites causally related to BMD at distinct sites and several key metabolic pathways, which shed light on predictive biomarkers and drug targets for OP.
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Affiliation(s)
- Shuhong Chen
- Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, China.
| | - Weiman He
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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12
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Zawistowski M, Fritsche LG, Pandit A, Vanderwerff B, Patil S, Schmidt EM, VandeHaar P, Willer CJ, Brummett CM, Kheterpal S, Zhou X, Boehnke M, Abecasis GR, Zöllner S. The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients. CELL GENOMICS 2023; 3:100257. [PMID: 36819667 PMCID: PMC9932985 DOI: 10.1016/j.xgen.2023.100257] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 06/07/2022] [Accepted: 01/05/2023] [Indexed: 02/04/2023]
Abstract
Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.
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Affiliation(s)
- Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Lars G. Fritsche
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Anita Pandit
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Ellen M. Schmidt
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Peter VandeHaar
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Cristen J. Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, Department of Human Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Chad M. Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Xiang Zhou
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
| | - Gonçalo R. Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48103, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48103, USA
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13
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Åsvold BO, Langhammer A, Rehn TA, Kjelvik G, Grøntvedt TV, Sørgjerd EP, Fenstad JS, Heggland J, Holmen O, Stuifbergen MC, Vikjord SAA, Brumpton BM, Skjellegrind HK, Thingstad P, Sund ER, Selbæk G, Mork PJ, Rangul V, Hveem K, Næss M, Krokstad S. Cohort Profile Update: The HUNT Study, Norway. Int J Epidemiol 2023; 52:e80-e91. [PMID: 35578897 PMCID: PMC9908054 DOI: 10.1093/ije/dyac095] [Citation(s) in RCA: 152] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Bjørn Olav Åsvold
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Tommy Aune Rehn
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Grete Kjelvik
- Norwegian National Advisory Unit on Ageing and Health (Ageing and Health), Tønsberg, Norway
| | - Trond Viggo Grøntvedt
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
| | - Elin Pettersen Sørgjerd
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jørn Søberg Fenstad
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
| | - Jon Heggland
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
| | - Oddgeir Holmen
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
| | - Maria C Stuifbergen
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
| | - Sigrid Anna Aalberg Vikjord
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Department of Medicine and Rehabilitation, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Ben M Brumpton
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Håvard Kjesbu Skjellegrind
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Pernille Thingstad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Health and Social Services, Trondheim Municipality, Trondheim, Norway
| | - Erik R Sund
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health (Ageing and Health), Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Paul Jarle Mork
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vegar Rangul
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Kristian Hveem
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Marit Næss
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Steinar Krokstad
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
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14
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Rämö JT, Kiiskinen T, Seist R, Krebs K, Kanai M, Karjalainen J, Kurki M, Hämäläinen E, Häppölä P, Havulinna AS, Hautakangas H, Mägi R, Palta P, Esko T, Metspalu A, Pirinen M, Karczewski KJ, Ripatti S, Milani L, Stankovic KM, Mäkitie A, Daly MJ, Palotie A. Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure. Nat Commun 2023; 14:157. [PMID: 36653343 PMCID: PMC9849444 DOI: 10.1038/s41467-022-32936-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 08/24/2022] [Indexed: 01/19/2023] Open
Abstract
Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 × 10-8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.
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Affiliation(s)
- Joel T Rämö
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Richard Seist
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mitja Kurki
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eija Hämäläinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Paavo Häppölä
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Heidi Hautakangas
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - Konrad J Karczewski
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Konstantina M Stankovic
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Antti Mäkitie
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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15
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Duan Y, Su YT, Ren J, Zhou Q, Tang M, Li J, Li SX. Kidney tonifying traditional Chinese medicine: Potential implications for the prevention and treatment of osteoporosis. Front Pharmacol 2023; 13:1063899. [PMID: 36699069 PMCID: PMC9868177 DOI: 10.3389/fphar.2022.1063899] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
The aging global population is increasingly affected by osteoporosis (OP), which is one of the most significant threats to the elderly. Moreover, its prevention and treatment situations have become increasingly severe. Therefore, it is imperative to develop alternatives or complementary drugs for preventing and treating osteoporosis. Kidney tonifying traditional Chinese medicine (KTTCM) has been used for the treatment of osteoporosis for a long time. Pharmacological studies have shown that kidney tonifying traditional Chinese medicine can promote osteoblasts, inhibit osteoclasts, and regulate the level of estrogen and plays vital roles in stimulating osteogenesis, restraining adipogenesis of marrow mesenchymal stem cells (MSCs), regulating the metabolism of calcium and phosphorus, and inhibiting oxidative stress. These effects are mediated by OPG/RANKL/RANK, BMP/Smads, MAPKs, and Wnt/β-catenin systems. To develop a safe, synergistic, effective, and homogenized TCM formula with robust scientific evidence to provide faster and more economical alternatives, the anti-osteoporosis ingredients and pharmacological mechanisms of kidney tonifying traditional Chinese medicine are recapitulated from the perspective of molecular and cell biology, and the safety and toxicity of kidney tonifying traditional Chinese medicine have also been reviewed in this paper.
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Affiliation(s)
- Yan Duan
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Yu-Ting Su
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Jie Ren
- Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Qun Zhou
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Min Tang
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Juan Li
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China
| | - Shun-Xiang Li
- Hunan Engineering Technology Research Center for Bioactive Substance Discovery of Chinese Medicine, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China,Hunan Province Sino-US International Joint Research Center for Therapeutic Drugs of Senile Degenerative Diseases, Changsha, China,*Correspondence: Shun-Xiang Li,
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16
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Fang Y, Fritsche LG, Mukherjee B, Sen S, Richmond-Rakerd LS. Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals. Biol Psychiatry 2022; 92:923-931. [PMID: 35965108 PMCID: PMC10712651 DOI: 10.1016/j.biopsych.2022.06.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 04/01/2022] [Accepted: 06/02/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome. METHODS We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative. RESULTS The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression. CONCLUSIONS The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.
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Affiliation(s)
- Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan.
| | - Lars G Fritsche
- Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan; Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, Michigan; Center for Statistical Genetics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan; Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, Michigan; Center for Statistical Genetics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan; Department of Epidemiology, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan; Department of Psychiatry, University of Michigan Medicine, Ann Arbor, Michigan
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Lazareva TE, Barbitoff YA, Changalidis AI, Tkachenko AA, Maksiutenko EM, Nasykhova YA, Glotov AS. Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies. J Pers Med 2022; 12:2040. [PMID: 36556260 PMCID: PMC9783756 DOI: 10.3390/jpm12122040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/19/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.
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Affiliation(s)
- Tatyana E. Lazareva
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Yury A. Barbitoff
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Anton I. Changalidis
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
- Faculty of Software Engineering and Computer Systems, ITMO University, 197101 St. Petersburg, Russia
| | - Alexander A. Tkachenko
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
| | - Evgeniia M. Maksiutenko
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
| | - Yulia A. Nasykhova
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
| | - Andrey S. Glotov
- Departemnt of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia
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18
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Brumpton BM, Graham S, Surakka I, Skogholt AH, Løset M, Fritsche LG, Wolford B, Zhou W, Nielsen JB, Holmen OL, Gabrielsen ME, Thomas L, Bhatta L, Rasheed H, Zhang H, Kang HM, Hornsby W, Moksnes MR, Coward E, Melbye M, Giskeødegård GF, Fenstad J, Krokstad S, Næss M, Langhammer A, Boehnke M, Abecasis GR, Åsvold BO, Hveem K, Willer CJ. The HUNT study: A population-based cohort for genetic research. CELL GENOMICS 2022; 2:100193. [PMID: 36777998 PMCID: PMC9903730 DOI: 10.1016/j.xgen.2022.100193] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/10/2022] [Accepted: 09/13/2022] [Indexed: 11/06/2022]
Abstract
The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.
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Affiliation(s)
- Ben M. Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim 7030, Norway
| | - Sarah Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- Department of Dermatology, Clinic of Orthopaedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Lars G. Fritsche
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Brooke Wolford
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonas Bille Nielsen
- Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark
| | - Oddgeir L. Holmen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
| | - Maiken E. Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
| | - Laurent Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- Department of Clinical and Molecular Medicine, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- BioCore—Bioinformatics Core Facility, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Humaira Rasheed
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - He Zhang
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hyun Min Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Whitney Hornsby
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Marta Riise Moksnes
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Eivind Coward
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Mads Melbye
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Guro F. Giskeødegård
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Jørn Fenstad
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
| | - Steinar Krokstad
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Marit Næss
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim 7030, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
| | - Cristen J. Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7030, Norway
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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Lorentzon M, Abrahamsen B. Osteoporosis epidemiology using international cohorts. Curr Opin Rheumatol 2022; 34:280-288. [PMID: 35758867 DOI: 10.1097/bor.0000000000000885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW To provide an update on the most important new cohort studies within osteoporosis and their bearing on clinical management and directions for future research. RECENT FINDINGS We identified a collection of new observational cohort studies - including new reports from already established large cohorts - and intervention studies providing new insights into osteoporosis pathophysiology, risk finding, intervention, and treatment barriers. SUMMARY Recent cohort studies in osteoporosis highlight the importance of timely identification and treatment of people who are at high risk of suffering osteoporotic fractures. Physical performance is a strong indicator of fracture risk and one that is tightly linked to a number of chronic conditions, not least inflammatory conditions like rheumatoid arthritis. Advances in case finding may involve opportunistic screening for low bone mineral density and vertebral fractures of radiology images obtained for other purposes, polygenic risk scores, and routinely collected medication and comorbidity information.
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Affiliation(s)
- Mattias Lorentzon
- Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
- Region Västra Götaland, Department of Geriatric Medicine, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Bo Abrahamsen
- Holbæk Hospital, Department of Medicine, Holbæk, Denmark
- Nuffield Department of Rheumatology and Musculoskeletal Science, University of Oxford, Oxford, UK
- Open Patient Data Explorative Network (OPEN), University of Southern Denmark and Odense University Hospital, Odense, Denmark
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20
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Fitzgerald T, Birney E. CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank. CELL GENOMICS 2022; 2:100167. [PMID: 36779085 PMCID: PMC9903682 DOI: 10.1016/j.xgen.2022.100167] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.
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Affiliation(s)
- Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
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21
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Genetic variation in WNT16 and its association with bone mineral density, fractures and osteoporosis in children with bone fragility. Bone Rep 2022; 16:101525. [PMID: 35535173 PMCID: PMC9077160 DOI: 10.1016/j.bonr.2022.101525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 11/20/2022] Open
Abstract
Several genome-wide association studies (GWAS), GWAS meta-analyses, and mouse studies have demonstrated that wingless-related integration site 16 (WNT16) gene is associated with bone mineral density (BMD), cortical bone thickness, bone strength and fracture risk. Practically no data exist regarding the significance of WNT16 in childhood-onset osteoporosis and related fractures. We hypothesized that pathogenic variants and genetic variations in WNT16 could explain skeletal fragility in affected children. We screened the WNT16 gene by Sanger sequencing in three pediatric cohorts: 35 with primary osteoporosis, 59 with multiple fractures, and in 95 healthy controls. Altogether, we identified 12 variants in WNT16. Of them one was a rare 5′UTR variant rs1386898215 in genome aggregate and medical trans-omic databases (GnomAD, TOPMED; minor allele frequency (MAF) 0.00 and 0.000008, respectively). One variant rs1554366753, overrepresented in children with osteoporosis (MAF = 0.06 vs healthy controls MAF = 0.01), was significantly associated with lower BMD. This variant was found associated with increased WNT16 gene expression at mRNA level in fibroblast cultures. None of the other identified variants were rare (MAF < 0.001) or deemed pathogenic by predictor programs. WNT16 may play a role in childhood osteoporosis but genetic WNT16 variation is not a common cause of skeletal fragility in childhood. No pathogenic WNT16 variants were found associated with pediatric osteoporosis or fracture-prone patients Altogether, twelve WNT16 variants were found in pediatric osteoporosis or fracture-prone patients The genetic variation rs1554366753 in the WNT16 gene is associated with bone mineral density and primary osteoporosis
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22
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Erickson CB, Hill R, Pascablo D, Kazakia G, Hansen K, Bahney C. A timeseries analysis of the fracture callus extracellular matrix proteome during bone fracture healing. JOURNAL OF LIFE SCIENCES (WESTLAKE VILLAGE, CALIF.) 2021; 3:1-30. [PMID: 35765657 PMCID: PMC9236279 DOI: 10.36069/jols/20220601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
While most bones fully self-heal, certain diseases require bone allograft to assist with fracture healing. Bone allografts offer promise as treatments for such fractures due to their osteogenic properties. However, current bone allografts made of decellularized bone extracellular matrix (ECM) have high failure rates, and thus grafts which improve fracture healing outcomes are needed. Understanding specific changes to the ECM proteome during normal fracture healing would enable the identification of key proteins that could be used enhance osteogenicity of bone allograft. Here, we performed a timeseries analysis of the fracture callus in mice to investigate proteomic and mineralization changes to the ECM at key stages of fracture healing. We found that changes to the ECM proteome largely coincide with the distinct phases of fracture healing. Basement membrane proteins (AGRN, COL4, LAMA), cartilage proteins (COL2A1, ACAN), and collagen crosslinking enzymes (LOXL, PLOD, ITIH) were initially upregulated, followed by bone specific proteoglycans and collagens (IBSP, COL1A1). Various tissue proteases (MMP2, 9, 13, 14; CTSK, CTSG, ELANE) were expressed at different levels throughout fracture healing. These changes coordinated with mineralization of the fracture callus, which increased steeply during the initial stages of healing. Interestingly the later timepoint was characterized by a response to wound healing and high expression of clotting factors (F2, 7, 9, 10). We identified ELANE and ITIH2 as tissue remodeling enzymes having no prior known involvement with fracture healing. This data can be further mined to identify regenerative proteins for enhanced bone graft design.
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Affiliation(s)
- Christopher B. Erickson
- Department of Biochemistry and Molecular Genetics,University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Ryan Hill
- Department of Biochemistry and Molecular Genetics,University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Donna Pascablo
- Orthopaedic Trauma Institute, University of California, San Francisco (UCSF), San Francisco, CA
| | - Galateia Kazakia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, CA
| | - Kirk Hansen
- Department of Biochemistry and Molecular Genetics,University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Chelsea Bahney
- Stedman Philippon Research Institute (SPRI), Center for Regenerative and Personalized Medicine. Vail, CO
- Orthopaedic Trauma Institute, University of California, San Francisco (UCSF), San Francisco, CA
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23
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Tesi N, van der Lee S, Hulsman M, Holstege H, Reinders MJT. snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets. Nucleic Acids Res 2021; 49:W603-W612. [PMID: 34048563 PMCID: PMC8262737 DOI: 10.1093/nar/gkab410] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/19/2021] [Accepted: 05/01/2021] [Indexed: 02/06/2023] Open
Abstract
Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer.
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Affiliation(s)
- Niccolo Tesi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Sven van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marc Hulsman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
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