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Genetic Risks and Molecular Epidemiology of Osteoarthritis
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Genetic Risks and Molecular Epidemiology of Osteoarthritis

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 5162

Special Issue Editors

Prof. Dr. Sui-Lung Su

E-Mail Website
Guest Editor
School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan
Interests: osteoarthritis; osteoporosis; sarcopenia; molecular epidemiology meta-analysis; artificial intelligence; bioinformatics; functional analysis
Dr. Chin Lin

E-Mail Website
Guest Editor
School of Medicine, National Defense Medical Center, Taipei, Taiwan
Interests: statistics; artificial intelligence; machine learning; deep learning; electrocardiography; computer vision; natural language processing; algorithm development
Special Issues, Collections and Topics in MDPI journals
Dr. Wen-Hui Fang

E-Mail Website
Guest Editor
Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Interests: artificial intelligence; osteoarthritis; osteoporosis; epidemiology; molecular epidemiology; sarcopenia

Special Issue Information

Dear Colleagues,

Osteoarthritis is a degenerative disease of articular cartilage, which is majorly contributed to by genetic risk factors. Current evidence has revealed approximately 50% heritability in osteoarthritis, but a state-of-the-art genome-wide association study only explained less than 5% of the proportion of osteoarthritis by genetic components. This phenomenon is referred to as missing heritability. This Special Issue, Genetic Risks and Molecular Epidemiology of Osteoarthritis, invites modern genetic epidemiology studies exploring the association between genetic factors and osteoarthritis, and welcomes all related manuscripts, including case–control studies, cohort studies, and meta-analyses. Genetic factors may be identified by linkage analysis, traditional candidate gene process, genome-wide screening, functional-based bioinformatics analysis, and literature reviews. We also welcome manuscripts using state-of-the-art technology to analyse related issues, such as artificial intelligence, machine learning, and deep learning algorithms.

Prof. Dr. Sui-Lung Su
Dr. Chin Lin
Dr. Wen-Hui Fang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • osteoarthritis
  • epidemiology
  • genetic factors
  • artificial intelligence
  • bioinformatics
  • functional analysis

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Published Papers (2 papers)

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10 pages, 1629 KiB  
Article
Strategy to Estimate Sample Sizes to Justify the Association between MMP1 SNP and Osteoarthritis
by Chung-Cheng Kao, Hsiang-En Hsu, Jen-Chieh Lai, Hsiang-Cheng Chen, Su-Wen Chuang and Meng-Chang Lee
Genes 2022, 13(6), 1084; https://doi.org/10.3390/genes13061084 - 17 Jun 2022
Cited by 4 | Viewed by 2052
Abstract
Background: the impact of knee osteoarthritis (OA) poses a formidable challenge to older adults. Studies have reported that genetic factors, such as MMP1, are one of important risk factors for knee OA. Although the relationship between the genetic polymorphism of MMP1 rs1799750 [...] Read more.
Background: the impact of knee osteoarthritis (OA) poses a formidable challenge to older adults. Studies have reported that genetic factors, such as MMP1, are one of important risk factors for knee OA. Although the relationship between the genetic polymorphism of MMP1 rs1799750 and the risk of knee OA has been explored, conclusions have been nonunanimous and pending due to research sample sizes, one of determinants in studying genetic polymorphisms associated with disease. Objective: to establish a model to assess whether the genetic polymorphism of MMP1 rs1799750 is associated with knee OA based on an estimation of sample sizes. Methods: samples were collected from a case–control and meta-analysis study. In the case–control study, patients who underwent knee X-ray examinations based on the Kellgren–Lawrence Grading System (KL) as diagnostic criteria were recruited at the Health Examination Center of the Tri-Service General Hospital from 2015 to 2019. Gene sequencing was conducted using iPLEX Gold. Those with unsuccessful gene sequencing were excluded. Finally, there were 569 patients in the knee OA group (KL ≥ 2) and 534 participants in the control group (KL < 2). In the meta-analysis, we used the databases PubMed, EMBASE, and Cochrane to search for studies on the relationship between MMP1 rs1799750 and knee OA. Next, we adopted the trial sequential analysis (TSA) method to assess whether sample sizes were sufficient or not to determine the risk of the genetic polymorphism of MMP1 rs1799750 on knee OA in Caucasians and Asians. Results: in Caucasians, the MMP1 rs1799750 was not significantly associated with knee OA with an odds ratios (OR) of 1.10 (95% confidence interval, CI: 0.45–2.68). Some extra 8559 samples were needed to conclude this relationship in Caucasians by the TSA model. In Asians, neither our case–control study results (n = 1103) nor a combination of samples from the case–control and meta-analysis results showed an association between MMP1 rs1799750 and knee OA. The OR (95% CI) was 1.10 (0.81–1.49) in a combination of Asian samples. Some extra 5517 samples were needed to justify this relationship in Asians by the TSA model. Conclusions: this research shows that an extra 8559 and 5517 samples are needed in Caucasians and Asians, respectively, in order to justify the association between MMP1 rs1799750 and knee OA. Full article
(This article belongs to the Special Issue Genetic Risks and Molecular Epidemiology of Osteoarthritis)
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Review

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14 pages, 1126 KiB  
Review
Chemokine Regulation in Temporomandibular Joint Disease: A Comprehensive Review
by Yusen Qiao, Jun Li, Catherine Yuh, Frank Ko, Louis G. Mercuri, Jad Alkhudari, Robin Pourzal and Chun-do Oh
Genes 2023, 14(2), 408; https://doi.org/10.3390/genes14020408 - 4 Feb 2023
Cited by 4 | Viewed by 2581
Abstract
Temporomandibular joint disorders (TMDs) are conditions that affect the muscles of mastication and joints that connect the mandible to the base of the skull. Although TMJ disorders are associated with symptoms, the causes are not well proven. Chemokines play an important role in [...] Read more.
Temporomandibular joint disorders (TMDs) are conditions that affect the muscles of mastication and joints that connect the mandible to the base of the skull. Although TMJ disorders are associated with symptoms, the causes are not well proven. Chemokines play an important role in the pathogenesis of TMJ disease by promoting chemotaxis inflammatory cells to destroy the joint synovium, cartilage, subchondral bone, and other structures. Therefore, enhancing our understanding of chemokines is critical for developing appropriate treatment of TMJ. In this review, we discuss chemokines including MCP-1, MIP-1α, MIP-3a, RANTES, IL-8, SDF-1, and fractalkine that are known to be involved in TMJ diseases. In addition, we present novel findings that CCL2 is involved in β-catenin-mediated TMJ osteoarthritis (OA) and potential molecular targets for the development of effective therapies. The effects of common inflammatory factors, IL-1β and TNF-α, on chemotaxis are also described. In conclusion, this review aims to provide a theoretical basis for future chemokine-targeted therapies for TMJ OA. Full article
(This article belongs to the Special Issue Genetic Risks and Molecular Epidemiology of Osteoarthritis)
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