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Busse E, Lee B, Nagamani SCS. Genetic Evaluation for Monogenic Disorders of Low Bone Mass and Increased Bone Fragility: What Clinicians Need to Know. Curr Osteoporos Rep 2024; 22:308-317. [PMID: 38600318 DOI: 10.1007/s11914-024-00870-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE OF REVIEW The purpose of this review is to outline the principles of clinical genetic testing and to provide practical guidance to clinicians in navigating genetic testing for patients with suspected monogenic forms of osteoporosis. RECENT FINDINGS Heritability assessments and genome-wide association studies have clearly shown the significant contributions of genetic variations to the pathogenesis of osteoporosis. Currently, over 50 monogenic disorders that present primarily with low bone mass and increased risk of fractures have been described. The widespread availability of clinical genetic testing offers a valuable opportunity to correctly diagnose individuals with monogenic forms of osteoporosis, thus instituting appropriate surveillance and treatment. Clinical genetic testing may identify the appropriate diagnosis in a subset of patients with low bone mass, multiple or unusual fractures, and severe or early-onset osteoporosis, and thus clinicians should be aware of how to incorporate such testing into their clinical practices.
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Affiliation(s)
- Emily Busse
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Houston, TX, USA.
| | - Sandesh C S Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
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Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis. Int J Mol Sci 2022; 23:ijms231710021. [PMID: 36077420 PMCID: PMC9456390 DOI: 10.3390/ijms231710021] [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: 07/08/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.740; OR (95% CI) = 2.9 (2.353–3.536)), as well as the formation of low BMD with efficacy of 79% (AUC = 0.790; OR (95% CI) = 3.94 (2.993–5.337)). In addition, we propose a model that predicts fracture risk and low BMD in a comorbid condition with 85% accuracy (AUC = 0.850; OR (95% CI) = 6.6 (4.411–10.608)) in postmenopausal women.
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Carey JJ, Chih-Hsing Wu P, Bergin D. Risk assessment tools for osteoporosis and fractures in 2022. Best Pract Res Clin Rheumatol 2022; 36:101775. [PMID: 36050210 DOI: 10.1016/j.berh.2022.101775] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Osteoporosis is one of the frequently encountered non-communicable diseases in the world today. Several hundred million people have osteoporosis, with many more at risk. The clinical feature is a fragility fracture (FF), which results in major reductions in the quality and quantity of life, coupled with a huge financial burden. In recognition of the growing importance, the World Health Organisation established a working group 30 years ago tasked with providing a comprehensive report to understand and assess the risk of osteoporosis in postmenopausal women. Dual-energy X-ray absorptiometry (DXA) is the most widely endorsed technology for assessing the risk of fracture or diagnosing osteoporosis before a fracture occurs, but others are available. In clinical practice, important distinctions are essential to optimise the use of risk assessments. Traditional tools lack specificity and were designed for populations to identify groups at higher risk using a 'one-size-fits-all' approach. Much has changed, though the purpose of risk assessment tools remains the same. In 2022, many tools are available to aid the identification of those most at risk, either likely to have osteoporosis or suffer the clinical consequence. Modern technology, enhanced imaging, proteomics, machine learning, artificial intelligence, and big data science will greatly advance a more personalised risk assessment into the future. Clinicians today need to understand not only which tool is most effective and efficient for use in their practice, but also which tool to use for which patient and for what purpose. A greater understanding of the process of risk assessment, deciding who should be screened, and how to assess fracture risk and prognosis in older men and women more comprehensively will greatly reduce the burden of osteoporosis for patients, society, and healthcare systems worldwide. In this paper, we review the current status of risk assessment, screening and best practice for osteoporosis, summarise areas of uncertainty, and make some suggestions for future developments, including a more personalised approach for individuals.
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Affiliation(s)
- John J Carey
- National University of Ireland Galway, 1007, Clinical Sciences Institute, Galway, H91 V4AY, Ireland.
| | - Paulo Chih-Hsing Wu
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Taiwan; Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Director, Obesity/Osteoporosis Special Clinic, 138 Sheng-Li Road, Tainan, 70428, Taiwan
| | - Diane Bergin
- National University of Ireland Galway, 1007, Clinical Sciences Institute, Galway, H91 V4AY, Ireland; Galway University Hospitals, Ireland
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Chimusa ER, Defo J. Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis. Front Genet 2022; 13:838518. [PMID: 35664319 PMCID: PMC9159898 DOI: 10.3389/fgene.2022.838518] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e−14) and myometrial relaxation and contraction (p = 3e−08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.
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Sotnikova EA, Kiseleva AV, Meshkov AN, Ershova AI, Ivanova AA, Kolchina MA, Kutsenko VA, Skripnikova IA, Drapkina OM. Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2021-3045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.
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Affiliation(s)
- E. A. Sotnikova
- National Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Russian National Research Medical University
| | - A. I. Ershova
- National Research Center for Therapy and Preventive Medicine
| | - A. A. Ivanova
- National Research Center for Therapy and Preventive Medicine
| | - M. A. Kolchina
- National Research Center for Therapy and Preventive Medicine
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - I. A. Skripnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Clark K, Leung YY, Lee WP, Voight B, Wang LS. Polygenic Risk Scores in Alzheimer's Disease Genetics: Methodology, Applications, Inclusion, and Diversity. J Alzheimers Dis 2022; 89:1-12. [PMID: 35848019 PMCID: PMC9484091 DOI: 10.3233/jad-220025] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The success of genome-wide association studies (GWAS) completed in the last 15 years has reinforced a key fact: polygenic architecture makes a substantial contribution to variation of susceptibility to complex disease, including Alzheimer's disease. One straight-forward way to capture this architecture and predict which individuals in a population are most at risk is to calculate a polygenic risk score (PRS). This score aggregates the risk conferred across multiple genetic variants, ultimately representing an individual's predicted genetic susceptibility for a disease. PRS have received increasing attention after having been successfully used in complex traits. This has brought with it renewed attention on new methods which improve the accuracy of risk prediction. While these applications are initially informative, their utility is far from equitable: the majority of PRS models use samples heavily if not entirely of individuals of European descent. This basic approach opens concerns of health equity if applied inaccurately to other population groups, or health disparity if we fail to use them at all. In this review we will examine the methods of calculating PRS and some of their previous uses in disease prediction. We also advocate for, with supporting scientific evidence, inclusion of data from diverse populations in these existing and future studies of population risk via PRS.
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Affiliation(s)
- Kaylyn Clark
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Translational Medicine and Therapeutics, Perelman School or Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes. Heredity (Edinb) 2021; 127:485-497. [PMID: 34689168 PMCID: PMC8626474 DOI: 10.1038/s41437-021-00479-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 10/13/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.
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Nguyen TV. Personalized fracture risk assessment: where are we at? Expert Rev Endocrinol Metab 2021; 16:191-200. [PMID: 33982611 DOI: 10.1080/17446651.2021.1924672] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Introduction: Osteoporotic fracture imposes a significant health care burden globally. Personalized assessment of fracture risk can potentially guide treatment decisions. Over the past decade, a number of risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX®, have been developed and implemented in clinical practice. Areas covered: This article reviews recent development and validation results concerning the prognostic performance of the two tools. The main areas of review are the need for personalized fracture risk prediction, purposes of risk prediction, predictive performance in terms of discrimination and calibration, concordance between the Garvan and FRAX tools, genetic profiling for improving predictive performance, and treatment thresholds. In some validation studies, FRAX tended to underestimate fracture by as high as 50%. Studies have shown that the predicted risk from the Garvan tool is highly concordant with clinical decision. Expert opinion: Although there are some discrepancy in fracture risk prediction between Garvan and FRAX, both tools are valid and can aid patients and doctors communicate about risk and make informed decision. The ideal of personalized risk assessment for osteoporosis patients will be realized through the incorporation of genetic profiling into existing fracture risk assessment tools.
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Affiliation(s)
- Tuan V Nguyen
- Healthy Ageing Theme, Garvan Institute of Medical Research Darlinghurst Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney Australia
- School of Biomedical Engineering, University of Technology Sydney Sydney Australia
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