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Ran S, Lin X, Wang S, Li Z, Liu B. Multi-trait Genome-Wide Analysis Identified 20 Novel Loci for Sarcopenia-Related Traits in UK Biobank. Calcif Tissue Int 2025; 116:10. [PMID: 39751833 DOI: 10.1007/s00223-024-01312-2] [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: 07/01/2024] [Accepted: 11/16/2024] [Indexed: 01/04/2025]
Abstract
This study aims to identify novel loci associated with sarcopenia-related traits in UK Biobank (UKB) through multi-trait genome-wide analysis. To identify novel loci associated with sarcopenia, we integrated the genome-wide association studies (GWAS) of usual walking pace (UWP) and hand grip strength (HGS) to conduct a joint association study known as multi-trait analysis of GWAS (MTAG). We performed a transcriptome-wide association study (TWAS) to analyze the results of MTAG in relation to mRNA expression data for genes identified in skeletal muscle. Additionally, we utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to explore the relationships between the identified genes and hub genes related to sarcopenia. We identified 15 novel loci associated with UWP and 5 novel loci associated with HGS at the genome wide significance level (GWS, p < 5 × 10 - 8 ). After TWAS (p TWAS < 6.659 × 10 - 6 , 0.05 / 7509 ), we found two significant genes: PPP1R3A, located at 7q31.1 and associated with HGS, and ZBTB38, located at 3q23 and associated with UWP. 11 identified genes associated with hub genes for sarcopenia were obtained through WGCNA. Our findings offer new insights into biological mechanisms underlying sarcopenia and identify several novel genes related to sarcopenia that warrant in-depth study.
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Affiliation(s)
- Shu Ran
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China.
- Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, People's Republic of China.
| | - XiTong Lin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - SiQi Wang
- First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China
| | - ZhuoQi Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - BaoLin Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
- Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, People's Republic of China
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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] [MESH Headings] [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: 24] [Impact Index Per Article: 12.0] [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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Integrated Analyses of DNA Methylation and Gene Expression of Rainbow Trout Muscle under Variable Ploidy and Muscle Atrophy Conditions. Genes (Basel) 2022; 13:genes13071151. [PMID: 35885934 PMCID: PMC9319582 DOI: 10.3390/genes13071151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Rainbow trout, Oncorhynchus mykiss, is an important cool, freshwater aquaculture species used as a model for biological research. However, its genome reference has not been annotated for epigenetic markers affecting various biological processes, including muscle growth/atrophy. Increased energetic demands during gonadogenesis/reproduction provoke muscle atrophy in rainbow trout. We described DNA methylation and its associated gene expression in atrophying muscle by comparing gravid, diploid females to sterile, triploid females. Methyl Mini-seq and RNA-Seq were simultaneously used to characterize genome-wide DNA methylation and its association with gene expression in rainbow trout muscle. Genome-wide enrichment in the number of CpGs, accompanied by depleted methylation levels, was noticed around the gene transcription start site (TSS). Hypermethylation of CpG sites within ±1 kb on both sides of TSS (promoter and gene body) was weakly/moderately associated with reduced gene expression. Conversely, hypermethylation of the CpG sites in downstream regions of the gene body +2 to +10 kb was weakly associated with increased gene expression. Unlike mammalian genomes, rainbow trout gene promotors are poor in CpG islands, at <1% compared to 60%. No signs of genome-wide, differentially methylated (DM) CpGs were observed due to the polyploidy effect; only 1206 CpGs (0.03%) were differentially methylated, and these were primarily associated with muscle atrophy. Twenty-eight genes exhibited differential gene expression consistent with methylation levels of 31 DM CpGs. These 31 DM CpGs represent potential epigenetic markers of muscle atrophy in rainbow trout. The DM CpG-harboring genes are involved in apoptosis, epigenetic regulation, autophagy, collagen metabolism, cell membrane functions, and Homeobox proteins. Our study also identified genes explaining higher water content and modulated glycolysis previously shown as characteristic biochemical signs of rainbow trout muscle atrophy associated with sexual maturation. This study characterized DNA methylation in the rainbow trout genome and its correlation with gene expression. This work also identified novel epigenetic markers associated with muscle atrophy in fish/lower vertebrates.
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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: 34] [Impact Index Per Article: 11.3] [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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Park S. Association between polygenetic risk scores related to sarcopenia risk and their interactions with regular exercise in a large cohort of Korean adults. Clin Nutr 2021; 40:5355-5364. [PMID: 34560606 DOI: 10.1016/j.clnu.2021.09.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND & AIMS Sarcopenia elevates metabolic disorders in the elderly, and genetic and environmental factors influence the risk of sarcopenia. The purpose of the study was to examine the hypothesis that polygenetic variants for sarcopenic risk had interactions with metabolic disorders and lifestyles associated with sarcopenia risk in adults >50 years in a large urban hospital cohort. METHODS Sarcopenia was defined as an appendicular skeletal muscle mass/body weight (SMI) < 29.0% for men and <22.8% for women estimated from participants aged 18-39 years in the KNHANES 2009-2010. Genetic variants were selected using a genome-wide association study for sarcopenia (sarcopenia, n = 1368; control, n = 15,472). The best model showing the gene-gene interactions was selected using a generalized multifactor dimensionality reduction. The polygenic risk scores (PRS) were generated by summing the selected SNP risk alleles in the best model. RESULTS SMI was much higher in the control subjects than the sarcopenia subjects in both genders, and the fat mass index was opposite the SMI. The five-single nucleotide polymorphisms (SNPs) model included FADS2_rs97384, MYO10_rs31574 KCNQ5_rs6453647, DOCK5_rs11135857, and LRP1B_ rs74659977. Sarcopenia risk was positively associated with the PRS of the five-SNP model (ORs = 1.977, 95% CI = 1.634-2.393). The PRS interacted with age (P < 0.0001), metabolic syndrome (P = 0.01), grip strength (P = 0.007), and serum total cholesterol concentrations (P = 0.005) for the sarcopenia risk. There were no interactions of PRS with the lifestyle components except for exercise. CONCLUSION The genetic impact may be offset in the elderly, having metabolic syndrome, high serum total cholesterol concentrations, and high grip strength, but only exercise in the lifestyle factors can overcome the genetic effect. Middle-aged and elderly participants with a genetic risk for sarcopenia may require regular exercise to maintain high grip strength and prevent metabolic syndrome.
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Affiliation(s)
- Sunmin Park
- Dept. of Food and Nutrition, Obesity/Diabetes Research Center, Hoseo University, Asan, South Korea.
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Feng GJ, Wei XT, Zhang H, Yang XL, Shen H, Tian Q, Deng HW, Zhang L, Pei YF. Identification of pleiotropic loci underlying hip bone mineral density and trunk lean mass. J Hum Genet 2021; 66:251-260. [PMID: 32929176 PMCID: PMC7880826 DOI: 10.1038/s10038-020-00835-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/15/2020] [Accepted: 08/24/2020] [Indexed: 11/09/2022]
Abstract
Bone mineral density (BMD) and lean body mass (LBM) not only have a considerable heritability each, but also are genetically correlated. However, common genetic determinants shared by both traits are largely unknown. In the present study, we performed a bivariate genome-wide association study (GWAS) meta-analysis of hip BMD and trunk lean mass (TLM) in 11,335 subjects from 6 samples, and performed replication in estimated heel BMD and TLM in 215,234 UK Biobank (UKB) participants. We identified 2 loci that nearly attained the genome-wide significance (GWS, p < 5.0 × 10-8) level in the discovery GWAS meta-analysis and that were successfully replicated in the UKB sample: 11p15.2 (lead SNP rs12800228, discovery p = 2.88 × 10-7, replication p = 1.95 × 10-4) and 18q21.32 (rs489693, discovery p = 1.67 × 10-7, replication p = 1.17 × 10-3). The above 2 pleiotropic loci may play a pleiotropic role for hip BMD and TLM development. So our findings provide useful insights that further enhance our understanding of genetic interplay between BMD and LBM.
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Affiliation(s)
- Gui-Juan Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China
| | - Xin-Tong Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China
| | - Hong Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China
| | - Xiao-Lin Yang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China
| | - Hui Shen
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Qing Tian
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
| | - Lei Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China.
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China.
| | - Yu-Fang Pei
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, Jiangsu, People's Republic of China.
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, People's Republic of China.
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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: 0.5] [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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Ran S, Zhang YX, Liu L, Jiang ZX, He X, Liu Y, Shen H, Tian Q, Pei YF, Deng HW, Zhang L. 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: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Shu Ran
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Yu-Xue Zhang
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Lu Liu
- Kunshan Hospital of Traditional Chinese Medicine, Jiangsu, PR China
| | - Zi-Xuan Jiang
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Xiao He
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Yu Liu
- School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Hui Shen
- Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Qing Tian
- Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Yu-Fang Pei
- Department of Epidemiology and Statistics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Hong-Wen Deng
- Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Lei Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China.
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China.
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