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Verleyen M, He Y, Burssens A, Silva MS, Callewaert B, Audenaert E. A systematic review and cross-database analysis of single nucleotide polymorphisms underlying hip morphology and osteoarthritis reveals shared mechanisms. Osteoarthritis Cartilage 2024; 32:872-885. [PMID: 38852879 DOI: 10.1016/j.joca.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/15/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024]
Abstract
OBJECTIVE Understanding the mechanisms of hip disease, such as osteoarthritis (OA), is crucial to advance their treatment. Such hip diseases often involve specific morphological changes. Genetic variations, called single nucleotide polymorphisms (SNPs), influence various hip morphological parameters. This study investigated the biological relevance of SNPs correlated to hip morphology in genome-wide association studies (GWAS). The SNP-associated genes were compared to genes associated with OA in other joints, aiming to see if the same genes play a role in both hip development and the risk of OA in other lower limb joints. METHODOLOGY A systematic literature review was conducted to identify SNPs correlated with hip morphology, based on the Population, Intervention, Comparison, Outcome, and Study (PICOS) framework. Afterwards, Gene Ontology (GO) analysis was performed, using EnrichR, on the SNP-associated genes and compared with non-hip OA-associated genes, across different databases. RESULTS Reviewing 49 GWAS identified 436 SNPs associated with hip joint morphology, encompassing variance in bone size, structure and shape. Among the SNP-associated genes, SOX9 plays a pivotal role in size, GDF5 impacts bone structure, and BMP7 affects shape. Overall, skeletal system development, regulation of cell differentiation, and chondrocyte differentiation emerged as crucial processes influencing hip morphology. Eighteen percent of GWAS-identified genes related to hip morphology were also associated with non-hip OA. CONCLUSION Our findings indicate the existence of multiple shared genetic mechanisms across hip morphology and OA, highlighting the necessity for more extensive research in this area, as in contrast to the hip, the genetic background on knee or foot morphology remains largely understudied.
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Affiliation(s)
- Marlies Verleyen
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Yukun He
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
| | - Arne Burssens
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
| | | | - Bert Callewaert
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
| | - Emmanuel Audenaert
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
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Fonseca ID, Fabbri LE, Moraes L, Coelho DB, Dos Santos FC, Rosse I. Pleiotropic effects on Sarcopenia subphenotypes point to potential molecular markers for the disease. Arch Gerontol Geriatr 2024; 127:105553. [PMID: 38970884 DOI: 10.1016/j.archger.2024.105553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 03/10/2024] [Accepted: 06/25/2024] [Indexed: 07/08/2024]
Abstract
Sarcopenia is a progressive age-related muscle disease characterized by low muscle strength, quantity and quality, and low physical performance. The clinical overlap between these subphenotypes (reduction in muscle strength, quantity and quality, and physical performance) was evidenced, but the genetic overlap is still poorly investigated. Herein, we investigated whether there is a genetic overlap amongst sarcopenia subphenotypes in the search for more effective molecular markers for this disease. For that, a Bioinformatics approach was used to identify and characterize pleiotropic effects at the genome, loci and gene levels using Genome-wide association study results. As a result, a high genetic correlation was identified between gait speed and muscle strength (rG=0.5358, p=3.39 × 10-8). Using a Pleiotropy-informed conditional and conjunctional false discovery rate method we identified two pleiotropic loci for muscle strength and gait speed, one of them was nearby the gene PHACTR1. Moreover, 11 pleiotropic loci and 25 genes were identified for muscle mass and muscle strength. Lastly, using a gene-based GWAS approach three candidate genes were identified in the overlap of the three Sarcopenia subphenotypes: FTO, RPS10 and CALCR. The current study provides evidence of genetic overlap and pleiotropy among sarcopenia subphenotypes and highlights novel candidate genes and molecular markers associated with the risk of sarcopenia.
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Affiliation(s)
- Isabela D Fonseca
- Programa de Pós-Graduação em Biotecnologia, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG Brazil; Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro Ouro Preto, MG Brazil
| | - Luiz Eduardo Fabbri
- Faculdade de Ciências Farmacêuticas, Universidade Estadual de Campinas, Campinas, SP Brazil
| | - Lauro Moraes
- Laboratório Multiusuário de Bioinformática, Pós-Graduação em Biotecnologia, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG Brazil
| | - Daniel B Coelho
- Laboratório de Fisiologia do Exercício da Escola de Educação Física, Universidade Federal de Ouro Preto, Ouro Preto, MG Brazil
| | - Fernanda C Dos Santos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health Toronto, ON Canada
| | - Izinara Rosse
- Programa de Pós-Graduação em Biotecnologia, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG Brazil; Laboratório Multiusuário de Bioinformática, Pós-Graduação em Biotecnologia, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, MG Brazil; Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro Ouro Preto, MG Brazil.
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Identification and Characterization of Genomic Predictors of Sarcopenia and Sarcopenic Obesity Using UK Biobank Data. Nutrients 2023; 15:nu15030758. [PMID: 36771461 PMCID: PMC9920138 DOI: 10.3390/nu15030758] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/28/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
The substantial decline in skeletal muscle mass, strength, and gait speed is a sign of severe sarcopenia, which may partly depend on genetic risk factors. So far, hundreds of genome-wide significant single nucleotide polymorphisms (SNPs) associated with handgrip strength, lean mass and walking pace have been identified in the UK Biobank cohort; however, their pleiotropic effects on all three phenotypes have not been investigated. By combining summary statistics of genome-wide association studies (GWAS) of handgrip strength, lean mass and walking pace, we have identified 78 independent SNPs (from 73 loci) associated with all three traits with consistent effect directions. Of the 78 SNPs, 55 polymorphisms were also associated with body fat percentage and 25 polymorphisms with type 2 diabetes (T2D), indicating that sarcopenia, obesity and T2D share many common risk alleles. Follow-up bioinformatic analysis revealed that sarcopenia risk alleles were associated with tiredness, falls in the last year, neuroticism, alcohol intake frequency, smoking, time spent watching television, higher salt, white bread, and processed meat intake; whereas protective alleles were positively associated with bone mineral density, serum testosterone, IGF1, and 25-hydroxyvitamin D levels, height, intelligence, cognitive performance, educational attainment, income, physical activity, ground coffee drinking and healthier diet (muesli, cereal, wholemeal or wholegrain bread, potassium, magnesium, cheese, oily fish, protein, water, fruit, and vegetable intake). Furthermore, the literature data suggest that single-bout resistance exercise may induce significant changes in the expression of 26 of the 73 implicated genes in m. vastus lateralis, which may partly explain beneficial effects of strength training in the prevention and treatment of sarcopenia. In conclusion, we have identified and characterized 78 SNPs associated with sarcopenia and 55 SNPs with sarcopenic obesity in European-ancestry individuals from the UK Biobank.
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Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev 2022; 76:101576. [PMID: 35104630 DOI: 10.1016/j.arr.2022.101576] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 12/13/2021] [Accepted: 01/26/2022] [Indexed: 12/17/2022]
Abstract
Sarcopenia is a systemic disease with progressive and generalized skeletal muscle dysfunction defined by age-related low muscle mass, high content of muscle slow fibers, and low muscle function. Muscle phenotypes and sarcopenia risk are heritable; however, the genetic architecture and molecular mechanisms underlying sarcopenia remain largely unclear. In recent years, significant progress has been made in determining susceptibility loci using genome-wide association studies. In addition, recent advances in omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, offer new opportunities to identify novel targets to help us understand the pathophysiology of sarcopenia. However, each individual technology cannot capture the entire view of the biological complexity of this disorder, while integrative multi-omics analyses may be able to reveal new insights. Here, we review the latest findings of multi-omics studies for sarcopenia and provide an in-depth summary of our current understanding of sarcopenia pathogenesis. Leveraging multi-omics data could give us a holistic understanding of sarcopenia etiology that may lead to new clinical applications. This review offers guidance and recommendations for fundamental research, innovative perspectives, and preventative and therapeutic interventions for sarcopenia.
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Unveiling genetic variants for age-related sarcopenia by conducting a genome-wide association study on Korean cohorts. Sci Rep 2022; 12:3501. [PMID: 35241739 PMCID: PMC8894365 DOI: 10.1038/s41598-022-07567-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/22/2022] [Indexed: 11/08/2022] Open
Abstract
Sarcopenia is an age-related disorder characterised by a progressive decrease in skeletal muscle mass. As the genetic biomarkers for sarcopenia are not yet well characterised, this study aimed to investigate the genetic variations related to sarcopenia in a relatively aged cohort, using genome-wide association study (GWAS) meta-analyses of lean body mass (LBM) in 6961 subjects. Two Korean cohorts were analysed, and subgroup GWAS was conducted for appendicular skeletal muscle mass (ASM) and skeletal muscle index. The effects of significant single nucleotide polymorphisms (SNPs) on gene expression were also investigated using multiple expression quantitative trait loci datasets, differentially expressed gene analysis, and gene ontology analyses. Novel genetic biomarkers were identified for LBM (rs1187118; rs3768582) and ASM (rs6772958). Their related genes, including RPS10, NUDT3, NCF2, SMG7, and ARPC5, were differently expressed in skeletal muscle tissue, while GPD1L was not. Furthermore, the 'mRNA destabilisation' biological process was enriched for sarcopenia. Our study identified RPS10, NUDT3, and GPD1L as significant genetic biomarkers for sarcopenia. These genetic loci were related to lipid and energy metabolism, suggesting that genes involved in metabolic dysregulation may lead to the pathogenesis of age-related sarcopenia.
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Saeki C, Tsubota A. Influencing Factors and Molecular Pathogenesis of Sarcopenia and Osteosarcopenia in Chronic Liver Disease. Life (Basel) 2021; 11:life11090899. [PMID: 34575048 PMCID: PMC8468289 DOI: 10.3390/life11090899] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/27/2021] [Accepted: 08/27/2021] [Indexed: 02/07/2023] Open
Abstract
The liver plays a pivotal role in nutrient/energy metabolism and storage, anabolic hormone regulation, ammonia detoxification, and cytokine production. Impaired liver function can cause malnutrition, hyperammonemia, and chronic inflammation, leading to an imbalance between muscle protein synthesis and proteolysis. Patients with chronic liver disease (CLD) have a high prevalence of sarcopenia, characterized by progressive loss of muscle mass and function, affecting health-related quality of life and prognosis. Recent reports have revealed that osteosarcopenia, defined as the concomitant occurrence of sarcopenia and osteoporosis, is also highly prevalent in patients with CLD. Since the differentiation and growth of muscles and bones are closely interrelated through mechanical and biochemical communication, sarcopenia and osteoporosis often progress concurrently and affect each other. Osteosarcopenia further exacerbates unfavorable health outcomes, such as vertebral fracture and frailty. Therefore, a comprehensive assessment of sarcopenia, osteoporosis, and osteosarcopenia, and an understanding of the pathogenic mechanisms involving the liver, bones, and muscles, are important for prevention and treatment. This review summarizes the molecular mechanisms of sarcopenia and osteosarcopenia elucidated to data in hopes of promoting advances in treating these musculoskeletal disorders in patients with CLD.
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Affiliation(s)
- Chisato Saeki
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, The Jikei University School of Medicine, 3-25-8 Nishi-shimbashi, Minato-ku, Tokyo 105-8461, Japan;
| | - Akihito Tsubota
- Core Research Facilities, Research Center for Medical Science, The Jikei University School of Medicine, 3-25-8 Nishi-shimbashi, Minato-ku, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-3-3433-1111
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Tan LJ, Li XH, Li GG, Hu Y, Chen XD, Deng HW. Identification of novel pleiotropic gene for bone mineral density and lean mass using the cFDR method. Ann Hum Genet 2021; 85:201-212. [PMID: 34115876 DOI: 10.1111/ahg.12438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/26/2021] [Accepted: 06/01/2021] [Indexed: 11/27/2022]
Abstract
Bone mineral density (BMD) and whole-body lean mass (WBLM) are two important phenotypes of osteoporosis and sarcopenia. Previous studies have shown that BMD and lean mass were phenotypically and genetically correlated. To identify the novel common genetic factors shared between BMD and WBLM, we performed the conditional false discovery rate (cFDR) analysis using summary data of the genome-wide association study of femoral neck BMD (n = 53,236) and WBLM (n = 38,292) from the Genetic Factors for Osteoporosis Consortium (GEFOS). We identified eight pleiotropic Single Nucleotide Polymorphism (SNPs) (PLCL1 rs11684176 and rs2880389, JAZF1 rs198, ADAMTSL3 rs10906982, RFTN2/MARS2 rs7340470, SH3GL3 rs1896797, ST7L rs10776755, ANKRD44/SF3B1 rs11888760) significantly associated with femoral neck BMD and WBLM (ccFDR < 0.05). Bayesian fine-mapping analysis showed that rs11888760, rs198, and rs1896797 were the possible functional variants in the ANKRD44/SF3B1, JAZF1i, and SH3GL3 loci, respectively. Functional annotation suggested that rs11888760 was likely to comprise a DNA regulatory element and linked to the expression of RFTN2 and PLCL1. PLCL1 showed differential expression in laryngeal posterior cricoarytenoid muscle between rats of 6 months and 30 months of age. Our findings, together with PLCL1's potential functional relevance to bone and skeletal muscle function, suggested that rs11888760 was the possible pleiotropic functional variants appearing to coregulate both bone and muscle metabolism through regulating the expression of PLCL1. The findings enhanced our knowledge of genetic associations between BMD and lean mass and provide a rationale for subsequent functional studies of the implicated genes in the pathophysiology of diseases, such as osteoporosis and sarcopenia.
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Affiliation(s)
- Li-Jun Tan
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Xiao-Hua Li
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Gai-Gai Li
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yuan Hu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Xiang-Ding Chen
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Hong-Wen Deng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.,Center of Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
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Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications. Bone Res 2021; 9:23. [PMID: 33927194 PMCID: PMC8085014 DOI: 10.1038/s41413-021-00143-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/21/2020] [Indexed: 02/03/2023] Open
Abstract
Osteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.
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Tian X, Wu L, Jiang M, Zhang Z, Wu R, Miao J, Liu C, Gao S. Downregulation of GLYAT Facilitates Tumor Growth and Metastasis and Poor Clinical Outcomes Through the PI3K/AKT/Snail Pathway in Human Breast Cancer. Front Oncol 2021; 11:641399. [PMID: 33968740 PMCID: PMC8100313 DOI: 10.3389/fonc.2021.641399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/23/2021] [Indexed: 12/24/2022] Open
Abstract
Background The Glycine N-acyltransferase (GLYAT) gene encodes a protein that catalyzes the transfer of acyl groups from acyl CoA to glycine, resulting in acyl glycine and coenzyme A. Aberrant GLYAT expression is associated with several malignant tumors, but its clinical importance in human breast cancer (BC), has yet to be fully addressed. This study aims to evaluate the clinical function of GLYAT in BC patients. Methods GLYAT expression was determined by immune blot and immunohistochemistry in three BC cell lines and primary cancer tissues. The MDA-MB 231 cell line was used for GLYAT gene knockdown experiments while the MCF7 cell line for overexpression experiments. Colony formation experiments, soft agar experiments, and transwell assays were utilized for further inspection of cell proliferation and migration capabilities. Immunofluorescence and western blot were used to detect markers of the epithelial-mesenchymal transition (EMT) and changes in the PI3K/AKT/Snail pathway. The role of GLYAT in tumor growth and metastasis was also assessed in nude mice in vivo. Also, a correlation analysis was performed between clinicopathological features and GLYAT expression in BC patients. Results GLYAT was decreased in human BC tissues and cell lines. Functional analysis showed that knockdown of GLYAT augmented BC cell proliferation in vitro and in vivo. However, this phenomenon was reversed when GLYAT was overexpressed in the transfected cells. Moreover, downregulation of GLYAT promoted the migratory properties of BC cells, likely through the activation of PI3K/AKT/Snail signaling, which subsequently induced the EMT. IHC analysis indicated that GLYAT was decreased in human BC tissues and lower GLYAT expression was correlated with histological grade, tumor TNM stage, Ki-67 status, and poorer survival in BC patients. Furthermore, lower GLYAT expression seemed as an independent risk factor related to poor prognosis in BC patients based on Cox regression analyses. Conclusion Our findings demonstrate that downregulation of GLYAT expression in human breast cancer is correlated with EMT via the PI3K/AKT/Snail pathway and is also associated with histological grade, tumor TNM stage, Ki-67 status, and poor survival in breast cancer patients.
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Affiliation(s)
- Xin Tian
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lina Wu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Min Jiang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zhenyong Zhang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Rong Wu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jianing Miao
- Key Laboratory of Shengjing Hospital, China Medical University, Shenyang, China
| | - Caigang Liu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
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Peng C, Liu F, Su KJ, Lin X, Song YQ, Shen J, Hu SD, Chen QC, Yuan HH, Li WX, Zeng CP, Deng HW, Lou HL. Enhanced Identification of Novel Potential Variants for Appendicular Lean Mass by Leveraging Pleiotropy With Bone Mineral Density. Front Immunol 2021; 12:643894. [PMID: 33889153 PMCID: PMC8056257 DOI: 10.3389/fimmu.2021.643894] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 03/09/2021] [Indexed: 11/22/2022] Open
Abstract
Strong relationships have been found between appendicular lean mass (ALM) and bone mineral density (BMD). It may be due to a shared genetic basis, termed pleiotropy. By leveraging the pleiotropy with BMD, the aim of this study was to detect more potential genetic variants for ALM. Using the conditional false discovery rate (cFDR) methodology, a combined analysis of the summary statistics of two large independent genome wide association studies (GWAS) of ALM (n = 73,420) and BMD (n = 10,414) was conducted. Strong pleiotropic enrichment and 26 novel potential pleiotropic SNPs were found for ALM and BMD. We identified 156 SNPs for ALM (cFDR <0.05), of which 74 were replicates of previous GWASs and 82 were novel SNPs potentially-associated with ALM. Eleven genes annotated by 31 novel SNPs (13 pleiotropic and 18 ALM specific) were partially validated in a gene expression assay. Functional enrichment analysis indicated that genes corresponding to the novel potential SNPs were enriched in GO terms and/or KEGG pathways that played important roles in muscle development and/or BMD metabolism (adjP <0.05). In protein–protein interaction analysis, rich interactions were demonstrated among the proteins produced by the corresponding genes. In conclusion, the present study, as in other recent studies we have conducted, demonstrated superior efficiency and reliability of the cFDR methodology for enhanced detection of trait-associated genetic variants. Our findings shed novel insight into the genetic variability of ALM in addition to the shared genetic basis underlying ALM and BMD.
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Affiliation(s)
- Cheng Peng
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Feng Liu
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Kuan-Jui Su
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Xu Lin
- Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan City, China
| | - Yu-Qian Song
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Jie Shen
- Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan City, China
| | - Shi-Di Hu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Qiao-Cong Chen
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Hui-Hui Yuan
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wen-Xi Li
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Hui-Ling Lou
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Yang YJ, Kim DJ. An Overview of the Molecular Mechanisms Contributing to Musculoskeletal Disorders in Chronic Liver Disease: Osteoporosis, Sarcopenia, and Osteoporotic Sarcopenia. Int J Mol Sci 2021; 22:ijms22052604. [PMID: 33807573 PMCID: PMC7961345 DOI: 10.3390/ijms22052604] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023] Open
Abstract
The prevalence of osteoporosis and sarcopenia is significantly higher in patients with liver disease than in those without liver disease and osteoporosis and sarcopenia negatively influence morbidity and mortality in liver disease, yet these musculoskeletal disorders are frequently overlooked in clinical practice for patients with chronic liver disease. The objective of this review is to provide a comprehensive understanding of the molecular mechanisms of musculoskeletal disorders accompanying the pathogenesis of liver disease. The increased bone resorption through the receptor activator of nuclear factor kappa (RANK)-RANK ligand (RANKL)-osteoprotegerin (OPG) system and upregulation of inflammatory cytokines and decreased bone formation through increased bilirubin and sclerostin and lower insulin-like growth factor-1 are important mechanisms for osteoporosis in patients with liver disease. Sarcopenia is associated with insulin resistance and obesity in non-alcoholic fatty liver disease, whereas hyperammonemia, low amount of branched chain amino acids, and hypogonadism contributes to sarcopenia in liver cirrhosis. The bidirectional crosstalk between muscle and bone through myostatin, irisin, β-aminoisobutyric acid (BAIBA), osteocalcin, as well as the activation of the RANK and the Wnt/β-catenin pathways are associated with osteosarcopenia. The increased understandings for these musculoskeletal disorders would be contributes to the development of effective therapies targeting the pathophysiological mechanism involved.
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Affiliation(s)
- Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Gangwon-do, Chuncheon 24252, Korea;
- Institute for Liver and Digestive Diseases, Hallym University, Gangwon-do, Chuncheon 24253, Korea
| | - Dong Joon Kim
- Department of Internal Medicine, Hallym University College of Medicine, Gangwon-do, Chuncheon 24252, Korea;
- Institute for Liver and Digestive Diseases, Hallym University, Gangwon-do, Chuncheon 24253, Korea
- Correspondence:
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Yoon KJ, Yi Y, Do JG, Kim HL, Lee YT, Kim HN. Variants in NEB and RIF1 genes on chr2q23 are associated with skeletal muscle index in Koreans: genome-wide association study. Sci Rep 2021; 11:2333. [PMID: 33674626 PMCID: PMC7935852 DOI: 10.1038/s41598-021-82003-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 01/14/2021] [Indexed: 11/25/2022] Open
Abstract
Although skeletal muscle plays a crucial role in metabolism and influences aging and chronic diseases, little is known about the genetic variations with skeletal muscle, especially in the Asian population. We performed a genome-wide association study in 2,046 participants drawn from a population-based study. Appendicular skeletal muscle mass was estimated based on appendicular lean soft tissue measured with a multi-frequency bioelectrical impedance analyzer and divided by height squared to derive the skeletal muscle index (SMI). After conducting quality control and imputing the genotypes, we analyzed 6,391,983 autosomal SNPs. A genome-wide significant association was found for the intronic variant rs138684936 in the NEB and RIF1 genes (β = 0.217, p = 6.83 × 10–9). These two genes are next to each other and are partially overlapped on chr2q23. We conducted extensive functional annotations to gain insight into the directional biological implication of significant genetic variants. A gene-based analysis identified the significant TNFSF9 gene and confirmed the suggestive association of the NEB gene. Pathway analyses showed the significant association of regulation of multicellular organism growth gene-set and the suggestive associations of pathways related to skeletal system development or skeleton morphogenesis with SMI. In conclusion, we identified a new genetic locus on chromosome 2 for SMI with genome-wide significance. These results enhance the biological understanding of skeletal muscle mass and provide specific leads for functional experiments.
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Affiliation(s)
- Kyung Jae Yoon
- Department of Physical & Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea.,Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea.,Biomedical Institute for Convergence at SKKU, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Youbin Yi
- Department of Physical & Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Jong Geol Do
- Department of Physical & Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Hyung-Lae Kim
- Department of Biochemistry, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Yong-Taek Lee
- Department of Physical & Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea. .,Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea.
| | - Han-Na Kim
- Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
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13
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Zhang YX, Zhang SS, Ran S, Liu Y, Zhang H, Yang XL, Hai R, Shen H, Tian Q, Deng HW, Zhang L, Pei YF. Three pleiotropic loci associated with bone mineral density and lean body mass. Mol Genet Genomics 2021; 296:55-65. [PMID: 32970232 PMCID: PMC7903521 DOI: 10.1007/s00438-020-01724-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 09/09/2020] [Indexed: 11/26/2022]
Abstract
Both bone mineral density (BMD) and lean body mass (LBM) are important physiological measures with strong genetic determination. Besides, BMD and LBM might have common genetic factors. Aiming to identify pleiotropic genomic loci underlying BMD and LBM, we performed bivariate genome-wide association study meta-analyses of femoral neck bone mineral density and LBM at arms and legs, and replicated in the large-scale UK Biobank cohort sample. Combining the results from discovery meta-analysis and replication sample, we identified three genomic loci at the genome-wide significance level (p < 5.0 × 10-8): 2p23.2 (lead SNP rs4477866, discovery p = 3.47 × 10-8, replication p = 1.03 × 10-4), 16q12.2 (rs1421085, discovery p = 2.04 × 10-9, replication p = 6.47 × 10-14) and 18q21.32 (rs11152213, discovery p = 3.47 × 10-8, replication p = 6.69 × 10-6). Our findings not only provide useful insights into lean mass and bone mass development, but also enhance our understanding of the potential genetic correlation between BMD and LBM.
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Affiliation(s)
- Yu-Xue Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Shan-Shan Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, Suzhou, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China
| | - Shu Ran
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Yu Liu
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Hong Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, Suzhou, People's Republic of China
| | - Xiao-Lin Yang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, Suzhou, People's Republic of China
| | - Rong Hai
- Inner Mongolia Autonomous Region Center of Health Management Service, Baotou, Inner Mongolia, People's Republic of China
| | - Hui Shen
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA
| | - Qing Tian
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA
| | - Hong-Wen Deng
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
| | - Lei Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China.
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, Suzhou, People's Republic of China.
| | - Yu-Fang Pei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, Suzhou, People's Republic of China.
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd.Jiangsu, Suzhou, 215123, People's Republic of China.
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14
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Ahisar Y, Thanassoulis G, Huang KN, Ohayon SM, Afilalo J. Intersecting Genetics of Frailty and Cardiovascular Disease. J Nutr Health Aging 2021; 25:1023-1027. [PMID: 34545923 DOI: 10.1007/s12603-021-1673-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To determine the genetic correlates of physical frailty and sarcopenia, focusing on single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS), and to explore the genetic overlap of frailty with cardiovascular disease (CVD) and its risk factors. METHODS PubMed was systematically searched for GWAS studies investigating the association between SNPs and objective measures of physical frailty or sarcopenia. SNPs were retained if they were associated with one of the phenotypes of interest by a p-value of 5.0x10-8 or less. RESULTS Ten studies were included, with a total of 237 SNPs in 181 genes being associated with physical frailty or sarcopenia; as measured by handgrip strength or lean (muscle) mass. These genes were cross-referenced in the GWAS Catalog, and many of them were found to be associated with CVD or metabolic syndrome. CONCLUSIONS Evidence from GWAS has shown that frailty is associated with common genetic polymorphisms. Many of these polymorphisms have been implicated in CVD, supporting the hypothesis of a shared pathophysiology between these entities. Future studies are eagerly anticipated to map out the mechanistic links and discover therapeutic targets and novel biomarkers for frailty.
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Affiliation(s)
- Y Ahisar
- Jonathan Afilalo, MD, MSc, FACC, FRCPC, Associate Professor, McGill University, Director, Geriatric Cardiology Fellowship Program, Jewish General Hospital, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Phone: (514) 340-7540 | Fax: (514) 340-7534,
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15
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Koshiba S, Motoike IN, Saigusa D, Inoue J, Aoki Y, Tadaka S, Shirota M, Katsuoka F, Tamiya G, Minegishi N, Fuse N, Kinoshita K, Yamamoto M. Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population. Commun Biol 2020; 3:662. [PMID: 33177615 PMCID: PMC7659008 DOI: 10.1038/s42003-020-01383-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 10/18/2020] [Indexed: 02/07/2023] Open
Abstract
We performed a metabolome genome-wide association study for the Japanese population in the prospective cohort study of Tohoku Medical Megabank. By combining whole-genome sequencing and nontarget metabolome analyses, we identified a large number of novel associations between genetic variants and plasma metabolites. Of the identified metabolite-associated genes, approximately half have already been shown to be involved in various diseases. We identified metabolite-associated genes involved in the metabolism of xenobiotics, some of which are from intestinal microorganisms, indicating that the identified genetic variants also markedly influence the interaction between the host and symbiotic bacteria. We also identified five associations that appeared to be female-specific. A number of rare variants that influence metabolite levels were also found, and combinations of common and rare variants influenced the metabolite levels more profoundly. These results support our contention that metabolic phenotyping provides important insights into how genetic and environmental factors provoke human diseases.
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Affiliation(s)
- Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
| | - Ikuko N Motoike
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai, 980-8579, Japan
| | - Daisuke Saigusa
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Jin Inoue
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Yuichi Aoki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai, 980-8579, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai, 980-8579, Japan
| | - Matsuyuki Shirota
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai, 980-8579, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
- Graduate School of Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
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16
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Replication of FTO Gene associated with lean mass in a Meta-Analysis of Genome-Wide Association Studies. Sci Rep 2020; 10:5057. [PMID: 32193455 PMCID: PMC7081265 DOI: 10.1038/s41598-020-61406-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 01/13/2020] [Indexed: 12/13/2022] Open
Abstract
Sarcopenia is characterized by low skeletal muscle, a complex trait with high heritability. With the dramatically increasing prevalence of obesity, obesity and sarcopenia occur simultaneously, a condition known as sarcopenic obesity. Fat mass and obesity-associated (FTO) gene is a candidate gene of obesity. To identify associations between lean mass and FTO gene, we performed a genome-wide association study (GWAS) of lean mass index (LMI) in 2207 unrelated Caucasian subjects and replicated major findings in two replication samples including 6,004 unrelated Caucasian and 38,292 unrelated Caucasian. We found 29 single nucleotide polymorphisms (SNPs) in FTO significantly associated with sarcopenia (combined p-values ranging from 5.92 × 10−12 to 1.69 × 10−9). Potential biological functions of SNPs were analyzed by HaploReg v4.1, RegulomeDB, GTEx, IMPC and STRING. Our results provide suggestive evidence that FTO gene is associated with lean mass.
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17
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Association of 3p27.1 Variants with Whole Body Lean Mass Identified by a Genome-wide Association Study. Sci Rep 2020; 10:4293. [PMID: 32152362 PMCID: PMC7062907 DOI: 10.1038/s41598-020-61272-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/25/2020] [Indexed: 12/25/2022] Open
Abstract
Whole body lean mass (WBLM) is a heritable trait predicting sarcopenia. To identify genomic locus underlying WBLM, we performed a genome-wide association study of fat-adjusted WBLM in the Framingham Heart Study (FHS, N = 6,004), and replicated in the Kansas City Osteoporosis Study (KCOS, N = 2,207). We identified a novel locus 3p27.1 that was associated with WBLM (lead SNP rs3732593 P = 7.19 × 10-8) in the discovery FHS sample, and the lead SNP was successfully replicated in the KCOS sample (one-sided P = 0.04). Bioinformatics analysis found that this SNP and its adjacent SNPs had the function of regulating enhancer activity in skeletal muscle myoblasts cells, further confirming the regulation of WBLM by this locus. Our finding provides new insight into the genetics of WBLM and enhance our understanding of sarcopenia.
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18
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Hernandez Cordero AI, Gonzales NM, Parker CC, Sokolof G, Vandenbergh DJ, Cheng R, Abney M, Sko A, Douglas A, Palmer AA, Gregory JS, Lionikas A. Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2. Am J Hum Genet 2019; 105:1222-1236. [PMID: 31761296 PMCID: PMC6904802 DOI: 10.1016/j.ajhg.2019.10.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 10/28/2019] [Indexed: 12/11/2022] Open
Abstract
Muscle bulk in adult healthy humans is highly variable even after height, age, and sex are accounted for. Low muscle mass, due to fewer and/or smaller constituent muscle fibers, would exacerbate the impact of muscle loss occurring in aging or disease. Genetic variability substantially influences muscle mass differences, but causative genes remain largely unknown. In a genome-wide association study (GWAS) on appendicular lean mass (ALM) in a population of 85,750 middle-aged (aged 38-49 years) individuals from the UK Biobank (UKB), we found 182 loci associated with ALM (p < 5 × 10-8). We replicated associations for 78% of these loci (p < 5 × 10-8) with ALM in a population of 181,862 elderly (aged 60-74 years) individuals from UKB. We also conducted a GWAS on hindlimb skeletal muscle mass of 1,867 mice from an advanced intercross between two inbred strains (LG/J and SM/J); this GWAS identified 23 quantitative trait loci. Thirty-eight positional candidates distributed across five loci overlapped between the two species. In vitro studies of positional candidates confirmed CPNE1 and STC2 as modifiers of myogenesis. Collectively, these findings shed light on the genetics of muscle mass variability in humans and identify targets for the development of interventions for treatment of muscle loss. The overlapping results between humans and the mouse model GWAS point to shared genetic mechanisms across species.
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Affiliation(s)
- Ana I Hernandez Cordero
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK
| | - Natalia M Gonzales
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Clarissa C Parker
- Department of Psychology, Middlebury College, Middlebury, VT 05753, USA; Program in Neuroscience, Middlebury College, Middlebury, VT, 05753, USA
| | - Greta Sokolof
- Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA 52242, USA
| | - David J Vandenbergh
- Department of Biobehavioral Health, Penn State Institute for the Neurosciences, and Molecular, Cellular, and Integrative Sciences Program, Pennsylvania State University, University Park, PA 16802, USA
| | - Riyan Cheng
- Department of Health Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Mark Abney
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Andrew Sko
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Alex Douglas
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3FX, UK
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jennifer S Gregory
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK
| | - Arimantas Lionikas
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK.
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19
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Styrkarsdottir U, Stefansson OA, Gunnarsdottir K, Thorleifsson G, Lund SH, Stefansdottir L, Juliusson K, Agustsdottir AB, Zink F, Halldorsson GH, Ivarsdottir EV, Benonisdottir S, Jonsson H, Gylfason A, Norland K, Trajanoska K, Boer CG, Southam L, Leung JCS, Tang NLS, Kwok TCY, Lee JSW, Ho SC, Byrjalsen I, Center JR, Lee SH, Koh JM, Lohmander LS, Ho-Pham LT, Nguyen TV, Eisman JA, Woo J, Leung PC, Loughlin J, Zeggini E, Christiansen C, Rivadeneira F, van Meurs J, Uitterlinden AG, Mogensen B, Jonsson H, Ingvarsson T, Sigurdsson G, Benediktsson R, Sulem P, Jonsdottir I, Masson G, Holm H, Norddahl GL, Thorsteinsdottir U, Gudbjartsson DF, Stefansson K. GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures. Nat Commun 2019; 10:2054. [PMID: 31053729 PMCID: PMC6499783 DOI: 10.1038/s41467-019-09860-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 04/03/2019] [Indexed: 12/12/2022] Open
Abstract
Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 - 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10-42, β = -0.090) and confers risk of hip fracture (P = 1.0 × 10-8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density.
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Affiliation(s)
| | | | | | | | - Sigrun H Lund
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | | | | | | | - Florian Zink
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
| | | | | | | | | | | | | | - Katerina Trajanoska
- Department of Epidemiology, ErasmusMC, 3015 GD, Rotterdam, The Netherlands
- Department of Internal Medicine, ErasmusMC, 3015 GD, Rotterdam, the Netherlands
| | - Cindy G Boer
- Department of Internal Medicine, ErasmusMC, 3015 GD, Rotterdam, the Netherlands
| | - Lorraine Southam
- Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Jason C S Leung
- Jockey Club Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Nelson L S Tang
- Faculty of Medicine, Department of Chemical Pathology and Laboratory for Genetics of Disease Susceptibility, Li Ka Shing Institute of Health Sciences,, The Chinese University of Hong Kong, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen, 518000, China
| | - Timothy C Y Kwok
- Jockey Club Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, China
| | - Jenny S W Lee
- Faculty of Medicine, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine, Alice Ho Miu Ling Nethersole Hospital and Tai Po Hospital, Hong Kong, China
| | - Suzanne C Ho
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Jacqueline R Center
- Bone Biology Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2010, Australia
| | - Seung Hun Lee
- Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - Jung-Min Koh
- Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - L Stefan Lohmander
- Orthopaedics, Department of Clinical Sciences Lund, Lund University, SE-22 100, Lund, Sweden
| | - Lan T Ho-Pham
- Bone and Muscle Research Lab, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam
| | - Tuan V Nguyen
- Bone Biology Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2010, Australia
- School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - John A Eisman
- Bone Biology Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2010, Australia
- Clinical Translation and Advanced Education, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Jean Woo
- Faculty of Medicine, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Ping-C Leung
- Jockey Club Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - John Loughlin
- Institute of Genetic Medicine, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
| | - Eleftheria Zeggini
- Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, 85764, München, Germany
| | | | - Fernando Rivadeneira
- Department of Epidemiology, ErasmusMC, 3015 GD, Rotterdam, The Netherlands
- Department of Internal Medicine, ErasmusMC, 3015 GD, Rotterdam, the Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, ErasmusMC, 3015 GD, Rotterdam, the Netherlands
| | | | - Brynjolfur Mogensen
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Emergengy Medicine, Landspitali, The National University Hospital of Iceland, 101, Reykjavik, Iceland
- Research Institute in Emergency Medicine, Landspitali, The National University Hospital of Iceland, and University of Iceland, 101, Reykjavik, Iceland
| | - Helgi Jonsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Medicine, Landspitali-The National University Hospital of Iceland, 101, Reykjavik, Iceland
| | - Thorvaldur Ingvarsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Orthopedic Surgery, Akureyri Hospital, 600, Akureyri, Iceland
- Institution of Health Science, University of Akureyri, 600, Akureyri, Iceland
| | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Research Service Center, Reykjavik, 201, Iceland
- Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, 101, Reykjavik, Iceland
| | - Rafn Benediktsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, 101, Reykjavik, Iceland
| | | | - Ingileif Jonsdottir
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Immunology, Landspitali-The National University Hospital of Iceland, 101, Reykjavik, Iceland
| | - Gisli Masson
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
| | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, 107, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, 101, Iceland.
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland.
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20
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Abstract
PURPOSE OF REVIEW To summarize the evidence from recent studies on the shared genetics between bone and muscle in humans. RECENT FINDINGS Genome-wide association studies (GWAS) have successfully identified a multitude of loci influencing the variability of different bone or muscle parameters, with multiple loci overlapping between the traits. In addition, joint analyses of multiple correlated musculoskeletal traits (i.e., multivariate GWAS) have underscored several genes with possible pleiotropic effects on both bone and muscle including MEF2C and SREBF1. Notably, several of the proposed pleiotropic genes have been validated using human cells or animal models. It is clear that the study of pleiotropy may provide novel insights into disease pathophysiology potentially leading to the identification of new treatment strategies that simultaneously prevent or treat both osteoporosis and sarcopenia. However, the role of muscle factors (myokines) that stimulate bone metabolism, as well as osteokines that affect muscles, is in its earliest stage of understanding.
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Affiliation(s)
- Katerina Trajanoska
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
| | - Douglas P Kiel
- Hebrew SeniorLife, Institute for Aging Research, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and Massachusetts Institute of Technology, Boston, MA, USA
| | - David Karasik
- Hebrew SeniorLife, Institute for Aging Research, Boston, MA, USA.
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
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21
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Liang X, Wu C, Zhao H, Liu L, Du Y, Li P, Wen Y, Zhao Y, Ding M, Cheng B, Cheng S, Ma M, Zhang L, Guo X, Shen H, Tian Q, Zhang F, Deng HW. Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis. Bone 2018; 116:301-306. [PMID: 30172743 PMCID: PMC6298225 DOI: 10.1016/j.bone.2018.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/22/2018] [Accepted: 08/29/2018] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The relationships between early growth parameters and bone mineral density (BMD) remain elusive now. In this study, we performed a large scale polygenic risk score (PRS) analysis to evaluate the potential impact of early growth parameters on the variations of BMD. METHODS We used 2286 Caucasian subjects as cohort 1 and 3404 Framingham Heart Study (FHS) subjects as cohort 2 in this study. BMD at ulna & radius, hip and spine were measured using dual energy X-ray absorptiometry. BMD values were adjusted for age, sex, height and weight as covariates. Genome-wide single-nucleotide polymorphism (SNP) genotyping of the 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The GWAS datasets of early growth parameters were driven from the Early Growth Genetics Consortium, including birth weight (BW), birth head circumference (BHC), childhood body mass index (CBMI), pubertal height growth related indexes and tanner stage. Polygenic Risk Score (PRSice) and linkage disequilibrium (LD) score regression analysis were conducted to assess the genetic correlation between early growth parameters and BMD. RESULTS We detected significant genetic correlations in cohort 1, such as total spine BMD vs. CBMI (p value = 1.51 × 10-4, rg = 0.4525), right ulna and radius BMD vs. CBMI (p value = 1.51 × 10-4, rg = 0.4399) and total body BMD vs. tanner stage (p value = 7.00 × 10-4, rg = -0.0721). For cohort 2, significant correlations were observed for total spine BMD vs. height change standard deviation score (SDS) between 8 years and adult (denoted as PGF + PGM) (p value = 3.97 × 10-4, rg = -0.1425), femoral neck BMD vs. the timing of peak height velocity by looking at the height change SDS between age 14 years and adult (denoted as PTF + PTM) (p value = 7.04 × 10-4, rg = -0.2185), and total spine BMD vs. PTF + PTM (p value = 6.86 × 10-4, rg = -0.2180). CONCLUSION Our study results suggest that some early growth parameters could affect the variations of BMD.
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Affiliation(s)
- Xiao Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - CuiYan Wu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Hongmou Zhao
- Department of Orthopedics Surgery, Red Cross Hospital, Xi'an 710054, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yanan Du
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Miao Ding
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Mei Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lu Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiong Guo
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA.
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22
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Lin C, Fesi BD, Marquis M, Bosak NP, Lysenko A, Koshnevisan MA, Duke FF, Theodorides ML, Nelson TM, McDaniel AH, Avigdor M, Arayata CJ, Shaw L, Bachmanov AA, Reed DR. Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2. Mamm Genome 2018; 29:325-343. [PMID: 29737391 DOI: 10.1007/s00335-018-9746-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 04/26/2018] [Indexed: 11/25/2022]
Abstract
To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross, backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J (donor) strains. The results from these mapping populations were concordant and showed that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. Of the protein-coding genes, 7 have missense variants, including genes that may contribute to lean body weight, such as Angpt41, Slc52c3, and Rem1. Lean body mass was increased by the B6-derived variant relative to the 129-derived allele. Burly1 influenced lean body weight at all ages but not food intake or locomotor activity. However, congenic mice with the B6 allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs and to identify a short list of candidate genes.
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Affiliation(s)
- Cailu Lin
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Brad D Fesi
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Michael Marquis
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Natalia P Bosak
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Anna Lysenko
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Fujiko F Duke
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Theodore M Nelson
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Amanda H McDaniel
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Mauricio Avigdor
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Charles J Arayata
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Lauren Shaw
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Danielle R Reed
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA.
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23
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Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus. Nat Commun 2017; 8:121. [PMID: 28743860 PMCID: PMC5527106 DOI: 10.1038/s41467-017-00108-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 06/01/2017] [Indexed: 11/24/2022] Open
Abstract
Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% (95% CI: 34–52%) for TBLH-BMD, and 39% (95% CI: 30–48%) for TB-LM, with a shared genetic component of 43% (95% CI: 29–56%). We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5. Variants in the TOM1L2/SREBF1 locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that SREBF1 is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass. Bone mineral density and lean skeletal mass are heritable traits. Here, Medina-Gomez and colleagues perform bivariate GWAS analyses of total body lean mass and bone mass density in children, and show genetic loci with pleiotropic effects on both traits.
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24
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Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, Kutalik Z, Luan J, Malkin I, Ried JS, Smith AV, Thorleifsson G, Vandenput L, Hua Zhao J, Zhang W, Aghdassi A, Åkesson K, Amin N, Baier LJ, Barroso I, Bennett DA, Bertram L, Biffar R, Bochud M, Boehnke M, Borecki IB, Buchman AS, Byberg L, Campbell H, Campos Obanda N, Cauley JA, Cawthon PM, Cederberg H, Chen Z, Cho NH, Jin Choi H, Claussnitzer M, Collins F, Cummings SR, De Jager PL, Demuth I, Dhonukshe-Rutten RAM, Diatchenko L, Eiriksdottir G, Enneman AW, Erdos M, Eriksson JG, Eriksson J, Estrada K, Evans DS, Feitosa MF, Fu M, Garcia M, Gieger C, Girke T, Glazer NL, Grallert H, Grewal J, Han BG, Hanson RL, Hayward C, Hofman A, Hoffman EP, Homuth G, Hsueh WC, Hubal MJ, Hubbard A, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson JO, Jordan JM, Jula A, Karlsson M, Khaw KT, Kilpeläinen TO, Klopp N, Kloth JSL, Koistinen HA, Kraus WE, Kritchevsky S, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Launer LJ, Lee JY, Lerch MM, Lewis JR, Lind L, Lindgren C, Liu Y, Liu T, Liu Y, Ljunggren Ö, Lorentzon M, Luben RN, Maixner W, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellström D, Melov S, Michaëlsson K, Mitchell BD, Morris AP, Mosekilde L, Newman A, Nielson CM, O'Connell JR, Oostra BA, Orwoll ES, Palotie A, Parker SCJ, Peacock M, Perola M, Peters A, Polasek O, Prince RL, Räikkönen K, Ralston SH, Ripatti S, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schadt EE, Schipf S, Scott L, Sehmi J, Shen J, Soo Shin C, Sigurdsson G, Smith S, Soranzo N, Stančáková A, Steinhagen-Thiessen E, Streeten EA, Styrkarsdottir U, Swart KMA, Tan ST, Tarnopolsky MA, Thompson P, Thomson CA, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Tuomilehto J, van Schoor NM, Verma A, Vollenweider P, Völzke H, Wactawski-Wende J, Walker M, Weedon MN, Welch R, Wichmann HE, Widen E, Williams FMK, Wilson JF, Wright NC, Xie W, Yu L, Zhou Y, Chambers JC, Döring A, van Duijn CM, Econs MJ, Gudnason V, Kooner JS, Psaty BM, Spector TD, Stefansson K, Rivadeneira F, Uitterlinden AG, Wareham NJ, Ossowski V, Waterworth D, Loos RJF, Karasik D, Harris TB, Ohlsson C, Kiel DP. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 2017; 8:80. [PMID: 28724990 PMCID: PMC5517526 DOI: 10.1038/s41467-017-00031-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 05/02/2017] [Indexed: 12/25/2022] Open
Abstract
Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10-8) or suggestively genome wide (p < 2.3 × 10-6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.
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Affiliation(s)
- M Carola Zillikens
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Serkalem Demissie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Yi-Hsiang Hsu
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Laura M Yerges-Armstrong
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Wen-Chi Chou
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Gregory Livshits
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Linda Broer
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Toby Johnson
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Daniel L Koller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Ida Malkin
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Janina S Ried
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | | | - Liesbeth Vandenput
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Weihua Zhang
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Ali Aghdassi
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Kristina Åkesson
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
- Department of Orthopedics, Skåne University Hospital, Malmö, S-205 02, Sweden
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
- NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 OQQ, UK
- Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, CB2 OQQ, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Experimental & Integrative Genomics, University of Lübeck, Lübeck, 23562, Germany
- School of Public Health, Faculty of Medicine, Imperial College London, London, W6 8RP, UK
| | - Rainer Biffar
- Centre of Oral Health, Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University of Greifswald, Greifswald, 17489, Germany
| | - Murielle Bochud
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
- Division of Biostatistics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Liisa Byberg
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Harry Campbell
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | | | - Jane A Cauley
- Department of Epidemiology Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Peggy M Cawthon
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Henna Cederberg
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Zhao Chen
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Nam H Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Youngtong-Gu, Suwon, 16499, Korea
| | - Hyung Jin Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
- Department of Internal Medicine, Chungbuk National University Hospital, Cheongju Si, Korea
| | - Melina Claussnitzer
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139, USA
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
| | - Francis Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Philip L De Jager
- Harvard Medical School, Boston, MA, 02115, USA
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
| | - Ilja Demuth
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
- Institute of Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, 13353, Germany
| | | | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, H3A 0G1, Canada
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | | | - Anke W Enneman
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Mike Erdos
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, 00014, Finland
- Unit of General Practice, Helsinki University Central Hospital, Helsinki, 00014, Finland
- Folkhalsan Research Centre, Helsinki, 00250, Finland
- Vasa Central Hospital, Vasa, 65130, Finland
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Joel Eriksson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Karol Estrada
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Mao Fu
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Melissa Garcia
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Nicole L Glazer
- Departments of Medicine and Epidemiology, Boston University School of Medicine and Public Health, Boston, MA, 02118, USA
| | - Harald Grallert
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- CCG Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- CCG Nutrigenomics and Type 2 Diabetes. Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Jagvir Grewal
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Bok-Ghee Han
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Albert Hofman
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Eric P Hoffman
- Department of Pharmaceutical Sciences, SUNY Binghamton, Binghamton, NY, 13902, USA
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, 17487, Germany
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Monica J Hubal
- Department of Exercise and Nutrition Sciences, George Washington University, Washington, DC, 20052, USA
- Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, 20052, USA
| | - Alan Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Kim M Huffman
- Division of Rheumatology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Lise B Husted
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Human Genetics, Hannover Medical School, Hannover, 30625, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | - Erik Ingelsson
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Till Ittermann
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - John-Olov Jansson
- Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE 405 30, Sweden
| | - Joanne M Jordan
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Antti Jula
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Magnus Karlsson
- Department of Clinical Sciences and Orthopaedics, Lund University, Skåne University Hospital SUS, Malmö, 22362, Sweden
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Tuomas O Kilpeläinen
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen, 2100, Denmark
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Norman Klopp
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | | | - Heikki A Koistinen
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Department of Health, National Institute for Health and Welfare, Helsinki, 00271, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, 00290, Finland
| | - William E Kraus
- Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Stephen Kritchevsky
- Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Teemu Kuulasmaa
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Jari Lahti
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Thomas Lang
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Bente L Langdahl
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Jong-Young Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Markus M Lerch
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Joshua R Lewis
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Centre for Kidney Research, School of Public Health, University of Sydney, Sydney, 2006, Australia
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27517, USA
| | - Tian Liu
- Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
- Max Planck Institute for Human Development, Berlin, 14195, Germany
| | - Youfang Liu
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Östen Ljunggren
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Mattias Lorentzon
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Robert N Luben
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - William Maixner
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Fiona E McGuigan
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Thomas Meitinger
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Håkan Melhus
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Dan Mellström
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Simon Melov
- Buck Institute for Research on Aging, Novato, CA, 94945, USA
- Leonard Davis School of Gerontology, University of Southern California, LA, CA, 90089, USA
| | - Karl Michaëlsson
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Leif Mosekilde
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Anne Newman
- Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | | | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ben A Oostra
- Department of Clinical Genetics, Erasmus MC, Rotterdam, 300 CA, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Eric S Orwoll
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, FI00014, Finland
| | - Stephen C J Parker
- Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Munro Peacock
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, FI00014, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Ozren Polasek
- Faculty of Medicine, Department of Public Health, University of Split, Split, 21000, Croatia
| | - Richard L Prince
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gardiner Hospital, Perth, 6009, Australia
| | - Katri Räikkönen
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Stuart H Ralston
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Hjelt Institute, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - John A Robbins
- Department of Medicine, University of California at Davis, Sacramento, CA, 95817, USA
| | - Jerome I Rotter
- Institute for Translational Genomic and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor UCLA Medical Center, Torrance, CA, 90502, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Suzanne Satterfield
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Science, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sabine Schipf
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Laura Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Joban Sehmi
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Jian Shen
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, Reykjavik, 101, Iceland
| | - Shad Smith
- Center for Translational Pain Medicine, Department of Anesthiology, Duke University Medical Center, Durham, NC, 27110, USA
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Alena Stančáková
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Elisabeth Steinhagen-Thiessen
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
| | - Elizabeth A Streeten
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatric Research and Education Clinical Center (GRECC) - Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | | | - Karin M A Swart
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Sian-Tsung Tan
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Mark A Tarnopolsky
- Department of Medicine, McMaster University Medical Center, Hamilton, ON, Canada, L8N 3Z5
| | - Patricia Thompson
- Department of Pathology, Stony Brook School of Medicine, Stony Brook, NY, 11794, USA
| | - Cynthia A Thomson
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Emmi Tikkanen
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Gregory J Tranah
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Jaakko Tuomilehto
- Vasa Central Hospital, Vasa, 65130, Finland
- Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, 3500, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, 12589, Saudi Arabia
- Dasman Diabetes Institute, Dasman, 15462, Kuwait
| | - Natasja M van Schoor
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Arjun Verma
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Peter Vollenweider
- Department of Medicine and Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, CH-1011, Switzerland
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - H-Erich Wichmann
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, 81377, Germany
- Institute of Medical Statistics and Epidemiology, Technical University, Munich, 81675, Germany
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - James F Wilson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Nicole C Wright
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Weijia Xie
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - John C Chambers
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust and Imperial College, London, SW3 6NP, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Angela Döring
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Michael J Econs
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Jaspal S Kooner
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
- Kaiser Permanente Washington Health Research Institute, Washington, Seattle, WA, 98101, USA
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Vicky Ossowski
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Dawn Waterworth
- Medical Genetics, GlaxoSmithKline, Philadelphia, PA, 19112, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Institute of Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Genetics of Obesity and Related Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David Karasik
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, 1311502, Israel
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Claes Ohlsson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Douglas P Kiel
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
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Wen Y, Hao J, Xiao X, Guo X, Wang W, Yang T, Shen H, Tian Q, Tan L, Deng HW, Zhang F. Evaluation of the relationship and genetic overlap between Kashin-Beck disease and body mass index. Scand J Rheumatol 2016; 45:512-517. [PMID: 27053287 DOI: 10.3109/03009742.2016.1139742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVES Body mass index (BMI) is one of the major factors affecting the development of osteoarthritis (OA) but there is currently no information available regarding the relationship between BMI and Kashin-Beck disease (KBD). Our aim in this study was to investigate the relationship and genetic overlap between BMI and KBD. METHOD A total of 2050 Han Chinese subjects participated in this study. Using a cohort of 333 grade I KBD patients, logistic regression analysis was conducted to evaluate the correlation between BMI and KBD. Another independent sample of 1717 subjects was genotyped for a genome-wide association study (GWAS) using Affymetrix Human SNP 6.0 Arrays. Single nucleotide polymorphism (SNP) effect concordance analysis (SECA) was applied to the GWAS summaries of KBD and BMI for pleiotropy analysis. Genome-wide bivariate association analysis (GWBAA) of KBD and BMI was carried out to identify the genes with pleiotropic effects on KBD and BMI. The relevance of identified genes with KBD was validated by gene expression profiling and immunohistochemistry. RESULTS BMI correlated positively with knee movement disorder in KBD (coefficient β = 0.068, p = 0.045). SECA identified a significant pleiotropic effect (empirical p = 0.021) between KBD and BMI. In the GWBAA, the rs1893577 of the ADAMTS1 gene achieved the most significant association signal (p = 7.38 × 10-9). ADAMTS1 was also up-regulated in KBD vs. normal (ratio = 2.64 ± 2.80) and KBD vs. OA (ratio = 2.31 ± 2.01). The rate of ADAMTS1-positive chondrocytes in KBD was significantly higher than that in OA (p < 0.05) and healthy controls (p < 0.05). CONCLUSIONS Our results suggest that ADAMTS1 is a novel susceptibility gene for KBD.
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Affiliation(s)
- Y Wen
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
| | - J Hao
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
| | - X Xiao
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
| | - X Guo
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
| | - W Wang
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
| | - T Yang
- b Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology , Xi'an Jiaotong University , Xi'an , P. R. China
| | - H Shen
- c Department of Biostatistics and Bioinformatics , Tulane University School of Public Health and Tropical Medicine , New Orleans , LA , USA.,d Center for Bioinformatics and Genomics , Tulane University , New Orleans , LA , USA
| | - Q Tian
- c Department of Biostatistics and Bioinformatics , Tulane University School of Public Health and Tropical Medicine , New Orleans , LA , USA.,d Center for Bioinformatics and Genomics , Tulane University , New Orleans , LA , USA
| | - L Tan
- e Laboratory of Molecular and Statistical Genetics, College of Life Sciences , Hunan Normal University , Changsha , P. R. China
| | - H-W Deng
- c Department of Biostatistics and Bioinformatics , Tulane University School of Public Health and Tropical Medicine , New Orleans , LA , USA.,d Center for Bioinformatics and Genomics , Tulane University , New Orleans , LA , USA
| | - F Zhang
- a Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center , Xi'an Jiaotong University , Xi'an , P. R. China
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A bivariate genome-wide association study identifies ADAM12 as a novel susceptibility gene for Kashin-Beck disease. Sci Rep 2016; 6:31792. [PMID: 27545300 PMCID: PMC4992896 DOI: 10.1038/srep31792] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 07/26/2016] [Indexed: 11/28/2022] Open
Abstract
Kashin-Beck disease (KBD) is a chronic osteoarthropathy, which manifests as joint deformities and growth retardation. Only a few genetic studies of growth retardation associated with the KBD have been carried out by now. In this study, we conducted a two-stage bivariate genome-wide association study (BGWAS) of the KBD using joint deformities and body height as study phenotypes, totally involving 2,417 study subjects. Articular cartilage specimens from 8 subjects were collected for immunohistochemistry. In the BGWAS, ADAM12 gene achieved the most significant association (rs1278300 p-value = 9.25 × 10−9) with the KBD. Replication study observed significant association signal at rs1278300 (p-value = 0.007) and rs1710287 (p-value = 0.002) of ADAM12 after Bonferroni correction. Immunohistochemistry revealed significantly decreased expression level of ADAM12 protein in the KBD articular cartilage (average positive chondrocyte rate = 47.59 ± 7.79%) compared to healthy articular cartilage (average positive chondrocyte rate = 64.73 ± 5.05%). Our results suggest that ADAM12 gene is a novel susceptibility gene underlying both joint destruction and growth retardation of the KBD.
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Livshits G, Gao F, Malkin I, Needhamsen M, Xia Y, Yuan W, Bell CG, Ward K, Liu Y, Wang J, Bell JT, Spector TD. Contribution of Heritability and Epigenetic Factors to Skeletal Muscle Mass Variation in United Kingdom Twins. J Clin Endocrinol Metab 2016; 101:2450-9. [PMID: 27144936 PMCID: PMC4891794 DOI: 10.1210/jc.2016-1219] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CONTEXT Skeletal muscle mass (SMM) is one of the major components of human body composition, with deviations from normal values often leading to sarcopenia. OBJECTIVE Our major aim was to conduct a genome-wide DNA methylation study in an attempt to identify potential genomic regions associated with SMM. DESIGN This was a mixed cross-sectional and longitudinal study. SETTING Community-based study. PARTICIPANTS A total of 1550 middle-aged United Kingdom twins (monozygotic [MZ] and dizygotic [DZ]), 297 of which were repeatedly measured participated in the study. MAIN OUTCOME MEASURE Appendicular lean mass assessed using dual-energy X-ray absorptiometry technology, and methylated DNA immunoprecipitation sequencing DNA methylation profiling genome-wide were obtained from each individual. RESULTS Heritability estimate of SMM, with simultaneous adjustment for covariates obtained using variance decomposition analysis, was h(2) = 0.809 ± 0.050. After quality control and analysis of longitudinal stability, the DNA methylation data comprised of 723 029 genomic sites, with positive correlations between repeated measurements (Rrepeated = 0.114-0.905). Correlations between MZ and DZ twins were 0.51 and 0.38 at a genome-wide average, respectively, and clearly increased with Rrepeated. Testing for DNA methylation association with SMM in 50 discordant MZ twins revealed 36 081 nominally significant results, of which the top-ranked 134 signals (P < .01 and Rrepeated > 0.40) were subjected to replication in the sample of 1196 individuals. Seven SMM methylation association signals replicated at a false discovery rate less than 0.1, and these were located in or near genes DNAH12, CAND1, CYP4F29P, and ZFP64, which have previously been highlighted in muscle-related studies. Adjusting for age, smoking, and blood cell heterogeneity did not alter significance of these associations. CONCLUSION This epigenome-wide study, testing longitudinally stable methylation sites, discovered and replicated a number of associations between DNA methylation at CpG loci and SMM. Four replicated signals were related to genes with potential muscle functions, suggesting that the methylome of whole blood may be informative of SMM variation.
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Abstract
Osteoporosis is characterized by low bone mass and an increased risk of fracture. Genetic factors, environmental factors and gene-environment interactions all contribute to a person's lifetime risk of developing an osteoporotic fracture. This Review summarizes key advances in understanding of the genetics of bone traits and their role in osteoporosis. Candidate-gene approaches dominated this field 20 years ago, but clinical and preclinical genetic studies published in the past 5 years generally utilize more-sophisticated and better-powered genome-wide association studies (GWAS). High-throughput DNA sequencing, large genomic databases and improved methods of data analysis have greatly accelerated the gene-discovery process. Linkage analyses of single-gene traits that segregate in families with extreme phenotypes have led to the elucidation of critical pathways controlling bone mass. For example, components of the Wnt-β-catenin signalling pathway have been validated (in both GWAS and functional studies) as contributing to various bone phenotypes. These notable advances in gene discovery suggest that the next decade will witness cataloguing of the hundreds of genes that influence bone mass and osteoporosis, which in turn will provide a roadmap for the development of new drugs that target diseases of low bone mass, including osteoporosis.
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Brotto M, Bonewald L. Bone and muscle: Interactions beyond mechanical. Bone 2015; 80:109-114. [PMID: 26453500 PMCID: PMC4600532 DOI: 10.1016/j.bone.2015.02.010] [Citation(s) in RCA: 193] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 01/27/2015] [Accepted: 02/08/2015] [Indexed: 02/08/2023]
Abstract
The musculoskeletal system is significantly more complex than portrayed by traditional reductionist approaches that have focused on and studied the components of this system separately. While bone and skeletal muscle are the two largest tissues within this system, this system also includes tendons, ligaments, cartilage, joints and other connective tissues along with vascular and nervous tissues. Because the main function of this system is locomotion, the mechanical interaction among the major players of this system is essential for the many shapes and forms observed in vertebrates and even in invertebrates. Thus, it is logical that the mechanical coupling theories of musculoskeletal development exert a dominant influence on our understanding of the biology of the musculoskeletal system, because these relationships are relatively easy to observe, measure, and perturb. Certainly much less recognized is the molecular and biochemical interaction among the individual players of the musculoskeletal system. In this brief review article, we first introduce some of the key reasons why the mechanical coupling theory has dominated our view of bone-muscle interactions followed by summarizing evidence for the secretory nature of bones and muscles. Finally, a number of highly physiological questions that cannot be answered by the mechanical theories alone will be raised along with different lines of evidence that support both a genetic and a biochemical communication between bones and muscles. It is hoped that these discussions will stimulate new insights into this fertile and promising new way of defining the relationships between these closely related tissues. Understanding the cellular and molecular mechanisms responsible for biochemical communication between bone and muscle is important not only from a basic research perspective but also as a means to identify potential new therapies for bone and muscle diseases, especially for when they co-exist. This article is part of a Special Issue entitled "Muscle Bone Interactions".
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Affiliation(s)
- Marco Brotto
- Muscle Biology Research Group-MUBIG, UMKC School of Nursing & Health Studies, 2464 Charlotte, USA; UMKC School of Medicine, 2464 Charlotte, USA
| | - Lynda Bonewald
- Bone Biology/Mineralized Tissue Research Program, Department of Oral and Craniofacial Sciences, UMKC School of Dentistry, 650 East 25th Street, Kansas City, MO 64108, USA
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Korostishevsky M, Steves CJ, Malkin I, Spector T, Williams FMK, Livshits G. Genomics and metabolomics of muscular mass in a community-based sample of UK females. Eur J Hum Genet 2015; 24:277-83. [PMID: 25898920 DOI: 10.1038/ejhg.2015.85] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 03/08/2015] [Accepted: 03/26/2015] [Indexed: 12/18/2022] Open
Abstract
The contribution of specific molecular-genetic factors to muscle mass variation and sarcopenia remains largely unknown. To identify endogenous molecules and specific genetic factors associated with appendicular lean mass (APLM) in the general population, cross-sectional data from the TwinsUK Adult Twin Registry were used. Non-targeted mass spec-based metabolomic profiling was performed on plasma of 3953 females (mostly dizygotic and monozygotic twins). APLM was measured using dual-energy X-ray absorptiometry (DXA) and genotyping was genome-wide (GWAS). Specific metabolites were used as intermediate phenotypes in the identification of single-nucleotide polymorphisms associated with APLM using GWAS. In all, 162 metabolites were found significantly correlated with APLM, and explained 17.4% of its variation. However, the top three of them (unidentified substance X12063, urate, and mannose) explained 11.1% (P ≤ 9.25 × 10(-26)) so each was subjected to GWAS. Each metabolite showed highly significant (P ≤ 9.28 × 10(-46)) associations with genetic variants in the corresponding genomic regions. Mendelian randomization using these SNPs found no evidence for a direct causal effect of these metabolites on APLM. However, using a new software platform for bivariate analysis we showed that shared genetic factors contribute significantly (P ≤ 4.31 × 10(-43)) to variance in both the metabolites and APLM--independent of the effect of the associated SNPs. There are several metabolites, having a clear pattern of genetic inheritance, which are highly significantly associated with APLM and may provide a cheap and readily accessible biomarker of muscle mass. However, the mechanism by which the genetic factor influences muscle mass remains to be discovered.
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Affiliation(s)
- Michael Korostishevsky
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ida Malkin
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Timothy Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Gregory Livshits
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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Kawao N, Kaji H. Interactions Between Muscle Tissues and Bone Metabolism. J Cell Biochem 2015; 116:687-95. [DOI: 10.1002/jcb.25040] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 12/15/2014] [Indexed: 12/21/2022]
Affiliation(s)
- Naoyuki Kawao
- Department of Physiology and Regenerative Medicine; Kinki University Faculty of Medicine; Osakasayama Japan
| | - Hiroshi Kaji
- Department of Physiology and Regenerative Medicine; Kinki University Faculty of Medicine; Osakasayama Japan
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Abstract
Osteoporosis is a skeletal disorder characterized by low bone mineral density (BMD) and an increased susceptibility to fractures. Evidence from genetic studies indicates that BMD, a complex quantitative trait with a normal distribution, is genetically controlled. Genome-wide association studies (GWAS) as well as studies using candidate gene approaches have identified single-nucleotide polymorphisms (SNPs) that are associated with BMD, osteoporosis and osteoporotic fractures. These SNPs have been mapped close to or within genes including those encoding WNT/β-catenin signaling proteins. Understanding the genetics of osteoporosis will help to identify novel candidates for diagnostic and therapeutic targets. Genetic factors are also important for the development of sarcopenia, which is characterized by a loss of lean body mass, and obesity, which is characterized by high fat mass. Hence, in this review, we discuss the genetic factors, identified by genetic studies, which regulate the body components related to osteoporosis, sarcopenia, and obesity.
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Affiliation(s)
- Tomohiko Urano
- Department of Geriatric Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
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Urano T, Shiraki M, Sasaki N, Ouchi Y, Inoue S. Large-scale analysis reveals a functional single-nucleotide polymorphism in the 5'-flanking region of PRDM16 gene associated with lean body mass. Aging Cell 2014; 13:739-43. [PMID: 24863034 PMCID: PMC4326941 DOI: 10.1111/acel.12228] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2014] [Indexed: 12/11/2022] Open
Abstract
Genetic factors are important for the development of sarcopenia, a geriatric disorder characterized by low lean body mass. The aim of this study was to search for novel genes that regulate lean body mass in humans. We performed a large-scale search for 250K single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD) using SNP arrays in 1081 Japanese postmenopausal women. We focused on an SNP (rs12409277) located in the 5′-flanking region of the PRDM16 (PRD1-BF-1-RIZ1 homologous domain containing protein 16) gene that showed a significant P value in our screening. We demonstrated that PRDM16 gene polymorphisms were significantly associated with total body BMD in 1081 postmenopausal Japanese women. The rs12409277 SNP affected the transcriptional activity of PRDM16. The subjects with one or two minor allele(s) had a higher lean body mass than the subjects with two major alleles. Genetic analyses uncovered the importance of the PRDM16 gene in the regulation of lean body mass.
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Affiliation(s)
- Tomohiko Urano
- Geriatric Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
- Anti‐Aging Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
| | - Masataka Shiraki
- Research Institute and Practice for Involutional Diseases Nagano Japan
| | - Noriko Sasaki
- Geriatric Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
- Anti‐Aging Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
| | - Yasuyoshi Ouchi
- Geriatric Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
| | - Satoshi Inoue
- Geriatric Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
- Anti‐Aging Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan
- Research Center for Genomic Medicine Saitama Medical School Saitama Japan
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Huang J, Hsu YH, Mo C, Abreu E, Kiel DP, Bonewald LF, Brotto M, Karasik D. METTL21C is a potential pleiotropic gene for osteoporosis and sarcopenia acting through the modulation of the NF-κB signaling pathway. J Bone Miner Res 2014; 29:1531-1540. [PMID: 24677265 PMCID: PMC4074268 DOI: 10.1002/jbmr.2200] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 01/31/2014] [Accepted: 02/04/2014] [Indexed: 01/06/2023]
Abstract
Sarcopenia and osteoporosis are important public health problems that occur concurrently. A bivariate genome-wide association study (GWAS) identified METTL21c as a suggestive pleiotropic gene for both bone and muscle. The METTL21 family of proteins methylates chaperones involved in the etiology of both myopathy and inclusion body myositis with Paget's disease. To validate these GWAS results, Mettl21c mRNA expression was reduced with siRNA in a mouse myogenic C2C12 cell line and the mouse osteocyte-like cell line MLO-Y4. At day 3, as C2C12 myoblasts start to differentiate into myotubes, a significant reduction in the number of myocytes aligning/organizing for fusion was observed in the siRNA-treated cells. At day 5, both fewer and smaller myotubes were observed in the siRNA-treated cells as confirmed by histomorphometric analyses and immunostaining with myosin heavy chain (MHC) antibody, which only stains myocytes/myotubes but not myoblasts. Intracellular calcium (Ca(2+)) measurements of the siRNA-treated myotubes showed a decrease in maximal amplitude peak response to caffeine, suggesting that less Ca(2+) is available for release due to the partial silencing of Mettl21c, correlating with impaired myogenesis. In siRNA-treated MLO-Y4 cells, 48 hours after treatment with dexamethasone there was a significant increase in cell death, suggesting a role of Mettl21c in osteocyte survival. To investigate the molecular signaling machinery induced by the partial silencing of Mettl21c, we used a real-time PCR gene array to monitor the activity of 10 signaling pathways. We discovered that Mettl21c knockdown modulated only the NF-κB signaling pathway (ie, Birc3, Ccl5, and Tnf). These results suggest that Mettl21c might exert its bone-muscle pleiotropic function via the regulation of the NF-κB signaling pathway, which is critical for bone and muscle homeostasis. These studies also provide rationale for cellular and molecular validation of GWAS, and warrant additional in vitro and in vivo studies to advance our understanding of role of METTL21C in musculoskeletal biology.
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Affiliation(s)
- Jian Huang
- Muscle Biology Research Group, Schools of Nursing & Health Studies, University of Missouri Kansas City, 2464 Charlotte Street, Kansas City, MO
| | - Yi-Hsiang Hsu
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Chenglin Mo
- Muscle Biology Research Group, Schools of Nursing & Health Studies, University of Missouri Kansas City, 2464 Charlotte Street, Kansas City, MO
| | - Eduardo Abreu
- Muscle Biology Research Group, Schools of Nursing & Health Studies, University of Missouri Kansas City, 2464 Charlotte Street, Kansas City, MO
| | - Douglas P. Kiel
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Lynda F. Bonewald
- Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri, Kansas City, MO, USA
| | - Maxrco Brotto
- Muscle Biology Research Group, Schools of Nursing & Health Studies, University of Missouri Kansas City, 2464 Charlotte Street, Kansas City, MO
| | - David Karasik
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
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Abstract
The musculoskeletal system is a complex organ comprised of the skeletal bones, skeletal muscles, tendons, ligaments, cartilage, joints, and other connective tissue that physically and mechanically interact to provide animals and humans with the essential ability of locomotion. This mechanical interaction is undoubtedly essential for much of the diverse shape and forms observed in vertebrates and even in invertebrates with rudimentary musculoskeletal systems such as fish. It makes sense from a historical point of view that the mechanical theories of musculoskeletal development have had tremendous influence of our understanding of biology, because these relationships are clear and palpable. Less visible to the naked eye or even to the microscope is the biochemical interaction among the individual players of the musculoskeletal system. It was only in recent years that we have begun to appreciate that beyond this mechanical coupling of muscle and bones, these 2 tissues function at a higher level through crosstalk signaling mechanisms that are important for the function of the concomitant tissue. Our brief review attempts to present some of the key concepts of these new concepts and is outline to present muscles and bones as secretory/endocrine organs, the evidence for mutual genetic and tissue interactions, pathophysiological examples of crosstalk, and the exciting new directions for this promising field of research aimed at understanding the biochemical/molecular coupling of these 2 intimately associated tissues.
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Affiliation(s)
- Marco Brotto
- Muscle Biology Research Group-MUBIG, UMKC School of Nursing & Health Studies and School of Medicine, 2464 Charlotte Street, Kansas City, MO, 64108, USA,
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Badenhorst CPS, Erasmus E, van der Sluis R, Nortje C, van Dijk AA. A new perspective on the importance of glycine conjugation in the metabolism of aromatic acids. Drug Metab Rev 2014; 46:343-61. [PMID: 24754494 DOI: 10.3109/03602532.2014.908903] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
A number of endogenous and xenobiotic organic acids are conjugated to glycine, in animals ranging from mosquitoes to humans. Glycine conjugation has generally been assumed to be a detoxification mechanism, increasing the water solubility of organic acids in order to facilitate urinary excretion. However, the recently proposed glycine deportation hypothesis states that the role of the amino acid conjugations, including glycine conjugation, is to regulate systemic levels of amino acids that are also utilized as neurotransmitters in the central nervous systems of animals. This hypothesis is based on the observation that, compared to glucuronidation, glycine conjugation does not significantly increase the water solubility of aromatic acids. In this review it will be argued that the major role of glycine conjugation is to dispose of the end products of phenylpropionate metabolism. Furthermore, glucuronidation, which occurs in the endoplasmic reticulum, would not be ideal for the detoxification of free benzoate, which has been shown to accumulate in the mitochondrial matrix. Glycine conjugation, however, prevents accumulation of benzoic acid in the mitochondrial matrix by forming hippurate, a less lipophilic conjugate that can be more readily transported out of the mitochondria. Finally, it will be explained that the glycine conjugation of benzoate, a commonly used preservative, exacerbates the dietary deficiency of glycine in humans. Because the resulting shortage of glycine can negatively influence brain neurochemistry and the synthesis of collagen, nucleic acids, porphyrins, and other important metabolites, the risks of using benzoate as a preservative should not be underestimated.
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Genome-wide copy number variation study and gene expression analysis identify ABI3BP as a susceptibility gene for Kashin–Beck disease. Hum Genet 2014; 133:793-9. [DOI: 10.1007/s00439-014-1418-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Accepted: 01/05/2014] [Indexed: 11/25/2022]
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Estimation of sex from the upper limb measurements of Sudanese adults. J Forensic Leg Med 2013; 20:1041-7. [PMID: 24237816 DOI: 10.1016/j.jflm.2013.09.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Revised: 08/26/2013] [Accepted: 09/29/2013] [Indexed: 11/23/2022]
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Shah K, Majeed Z, Jonason J, O'Keefe RJ. The role of muscle in bone repair: the cells, signals, and tissue responses to injury. Curr Osteoporos Rep 2013; 11:130-5. [PMID: 23591779 PMCID: PMC3698863 DOI: 10.1007/s11914-013-0146-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Bone repair is a complicated process that includes many types of cells, signaling molecules, and growth factors. Fracture healing involves a temporally and spatially regulated biologic process that involves recruitment of stem cells to the injury site, tissue specific differentiation, angiogenesis, and remodeling. In light of its proximity to bone and abundant vascularity, muscle is an important potential source of cells and signals for bone healing. More complete understanding of the role of muscle in bone formation and repair will provide new therapeutic approaches to enhance fracture healing. Recent studies establish that muscle-derived stem cells are able to differentiate into cartilage and bone and can directly participate in fracture healing. The role of muscle-derived stem cells is particularly important in fractures associated with more severe injury to the periosteum. Sarcopenia is a serious consequence of aging, and studies show a strong association between bone mass and lean muscle mass. Muscle anabolic agents may improve function and reduce the incidence of fracture with aging.
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Affiliation(s)
- Krupa Shah
- Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Box 665, 601 Elmwood Avenue, Rochester, NY 14692, USA
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Badenhorst CPS, van der Sluis R, Erasmus E, van Dijk AA. Glycine conjugation: importance in metabolism, the role of glycine N-acyltransferase, and factors that influence interindividual variation. Expert Opin Drug Metab Toxicol 2013; 9:1139-53. [PMID: 23650932 DOI: 10.1517/17425255.2013.796929] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Glycine conjugation of mitochondrial acyl-CoAs, catalyzed by glycine N-acyltransferase (GLYAT, E.C. 2.3.1.13), is an important metabolic pathway responsible for maintaining adequate levels of free coenzyme A (CoASH). However, because of the small number of pharmaceutical drugs that are conjugated to glycine, the pathway has not yet been characterized in detail. Here, we review the causes and possible consequences of interindividual variation in the glycine conjugation pathway. AREAS COVERED The authors review the importance of CoASH in metabolism, formation and toxicity of xenobiotic acyl-CoAs, and mechanisms for restoring levels of CoASH. They focus on GLYAT, glycine conjugation, how genetic variation in the GLYAT gene could influence glycine conjugation, and the emerging roles of glycine metabolism in cancer and musculoskeletal development. EXPERT OPINION The substrate selectivity of GLYAT and its variants needs to be further characterized, as organic acids can be toxic if the corresponding acyl-CoA is not a substrate for glycine conjugation. GLYAT activity affects mitochondrial ATP production, glycine availability, CoASH availability, and the toxicity of various organic acids. Therefore, variation in the glycine conjugation pathway could influence liver cancer, musculoskeletal development, and mitochondrial energy metabolism.
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