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Genes
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Advances in Genetic Research on Neuromuscular and Neurodevelopmental Disorders
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Advances in Genetic Research on Neuromuscular and Neurodevelopmental Disorders

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 (25 February 2025) | Viewed by 914

Special Issue Editor

Dr. Giorgia Ceravolo

E-Mail Website
Guest Editor
UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
Interests: neuromuscular disease; genetic; neurodevelopmental disorder
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to collect and disseminate the latest advanced research in the field of genetics applied to neuromuscular and neurodevelopmental disorders. We seek to explore both basic genetic studies and clinical applications, with a focus on how these discoveries can impact the diagnosis, treatment and understanding of the underlying mechanisms of these disorders.

We aim to provide a platform for publishing research articles, reviews and case studies addressing the latest findings in the genetics of neuromuscular and neurodevelopmental disorders. The topics of interest include, but are not limited to, the following:

  • Molecular and cellular genetics of neuromuscular disorders;
  • Genomic and bioinformatic approaches to understanding neurodevelopmental diseases;
  • New genetic editing techniques and their clinical applications;
  • Genome-wide association studies (GWAS) and the identification of new associated genes;
  • Animal and cellular models for studying genetic disorders;
  • Gene–environment interactions and their impact on neuromuscular and neurodevelopmental disorders.

Dr. Giorgia Ceravolo
Guest Editor

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

  • neuromuscular genetics
  • neurodevelopmental disorders
  • molecular genetics
  • GWAS
  • bioinformatics
  • genetic editing
  • animal models
  • gene–environment interactions

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

12 pages, 1057 KiB  
Article
Analysis of the DYNC1H1 Gene Polymorphic Variants’ Association with ASD Occurrence and Clinical Phenotype of Affected Children
by Anna Balcerzyk-Matić, Tomasz Iwanicki, Alicja Jarosz, Tomasz Nowak, Ewa Emich-Widera, Beata Kazek, Agnieszka Kapinos-Gorczyca, Maciej Kapinos, Joanna Iwanicka, Katarzyna Gawron, Wirginia Likus and Paweł Niemiec
Genes 2025, 16(5), 510; https://doi.org/10.3390/genes16050510 - 28 Apr 2025
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Abstract
Objectives: To analyze potential associations between three polymorphisms (rs3818188, rs941793, rs2403015) of the DYNC1H1 gene and the occurrence of autism spectrum disorder as well as the clinical phenotype of affected individuals. Methods: This family-based study included 206 children diagnosed with ASD and 364 [...] Read more.
Objectives: To analyze potential associations between three polymorphisms (rs3818188, rs941793, rs2403015) of the DYNC1H1 gene and the occurrence of autism spectrum disorder as well as the clinical phenotype of affected individuals. Methods: This family-based study included 206 children diagnosed with ASD and 364 of their biological parents. To examine the potential association between three polymorphisms of the DYNC1H1 gene and ASD occurrence, a transmission disequilibrium test was performed. Additionally, associations between the studied polymorphisms and the clinical phenotype of affected individuals were analyzed using the χ2 test. Results: None of the polymorphisms studied showed an association with ASD in the overall patient group. However, an association between the rs3818188 polymorphic variant and ASD was observed in a subgroup of girls, with the G allele being transmitted more than 2.5 times as frequently as the A allele. Moreover, several associations between the tested variants and features related to neuromotor development, communication, and social skills were observed in univariate analysis. However, after correction for multiple comparisons, only the association between the rs2403015 polymorphism and transient increase in muscle tone during infancy remained statistically significant. Conclusions: This study demonstrated an association between the rs3818188 polymorphism and ASD in a subgroup of girls. Additionally, the rs2403015 polymorphism was found to be associated with transient increase in muscle tone during infancy. Full article
(This article belongs to the Special Issue Advances in Genetic Research on Neuromuscular and Neurodevelopmental Disorders)
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15 pages, 1533 KiB  
Article
Development of a k-Nearest Neighbors Model for the Prediction of Late-Onset Alzheimer’s Risk by Combining Polygenic Risk Scores and Phenotypic Variables
by Sandra Ferreiro López, Rosana Ferrero, Jorge Blom-Dahl, Marta Alonso-Bernáldez, Adán González, Guillermo Pérez-Solero and Jair Tenorio-Castano
Genes 2025, 16(4), 377; https://doi.org/10.3390/genes16040377 - 26 Mar 2025
Viewed by 316
Abstract
Introduction: Alzheimer’s disease (AD), and more specifically late-onset Alzheimer’s disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients’ quality of life. The current challenge [...] Read more.
Introduction: Alzheimer’s disease (AD), and more specifically late-onset Alzheimer’s disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients’ quality of life. The current challenge is to accurately identify at-risk individuals before the manifestations of the first symptoms of AD. Methods and results: Here, we present an improved model for LOAD risk prediction, which applies the k-nearest neighbors (KNN) algorithm. We have achieved a sensitivity of 0.80 and an area under the curve (AUC) of 0.71, which represents a high performance especially when compared to an AUC of 0.66 reported previously in 2019 using a KNN model. Discussion: The application of a mathematical model that combines genetic and clinical covariates showed a good prediction of the AD/LOAD risk, with the higher weight being the polygenic genetic risk, APOE haplotype, and age. Compared to previous studies, our model integrates and correlates genetic prediction together with phenotypic information by fine-tuning the parameters of the model in order to achieve the best performance. This algorithm can be used in the general population and does not require the manifestation of any symptoms for its effective application. Thus, we present here an advanced model for risk prediction of LOAD. Full article
(This article belongs to the Special Issue Advances in Genetic Research on Neuromuscular and Neurodevelopmental Disorders)
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