Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder characterized by marked clinical and molecular heterogeneity. Over more than two decades, molecular diagnosis has relied on Southern blotting to resolve the complex D4Z4 macrosatellite at chromosome 4q35. While this approach remains a reference standard, its technical constraints and the need for complementary layers of information have become more evident as FSHD diagnostics is increasingly required to support patient stratification, genotype-phenotype correlation, and clinical trial readiness. In this context, the review traces the evolution of FSHD diagnostics from classical molecular approaches to modern genome-scale technologies that enable direct characterization of the D4Z4 locus, improve interpretation of borderline and atypical cases, and support integrated diagnostic workflows. Beyond technical innovation, the review highlights the growing need for harmonized diagnostic algorithms, international collaboration, and federated data infrastructures to support consistent interpretation across populations and healthcare systems. It further emphasizes how emerging requirements for molecular stratification in clinical trials, together with persistent global disparities in access to genetic testing, are reshaping priorities in FSHD diagnostics, positioning FSHD as a model for how rare disease diagnostics can integrate classical expertise with next-generation technologies to support clinical care, trial readiness, and more equitable access to diagnosis worldwide.

Artificial intelligence (AI) is increasingly being applied to biomedical research and public healthcare. However, concerns regarding security are arising, due to risks such as data leaks. This study investigates the perspectives of neurological donor patients (dNP), healthy biobank donors (dHV), and non-donor volunteers (ndHV) on the use of their clinical, biochemical and genetic data in the context of AI-powered biomedical research. A total of 115 subjects were surveyed (21 dNP, 17 dHV and 77 ndHV) on self-reported digital literacy; willingness to authorize data use (clinical, biochemical, genetic) for AI; acceptance of AI support in healthcare in diagnostic and therapeutic settings; use of data for AI training in medical research, by using supervised or non-supervised methods, inside or outside the European data space. Across all groups, high familiarity with digital technologies, low knowledge of AI risks, and strong optimism towards potential outcomes of AI were observed, together with high concern regarding data misuse among dNP with respect to ndHV and dHV (65, 48, and 20%, respectively; p < 0.05). A strong correlation between high digital competence and both increased risk awareness (p = 0.0003) and marked optimism regarding research outcomes (p = 0.0009) were observed. Most participants (92% of dNP, 94% of dHV and 82% of ndHV) would disclose the use of at least one kind of data, but ndHV were significantly less prone regarding the use of all types of data (28%) with a significant difference for only genetic (1%), with willingness to share clinical data significantly increased with higher level of digitalization (p = 0.008). In clinical setting, dNP are statistically favorable towards authorizing data for the adoption of AI for diagnostic (p = 0.04) and therapeutic (p = 0.02) decisions. Furthermore, ndHV were less likely to authorize data use for AI training, particularly for unsupervised AI. Results show the attitudes in authorizing the use of own data for AI based medical research seems to be linked to digital competence, but also to trust and clinical engagement. To facilitate the transition towards a healthcare system that leverages AI, health policies should not be limited to bridging the digital divide, but also actively invest in strengthening the doctor-patient relationship and ensuring transparency in care processes and data use in research.

This scientific commentary refers to ‘Benchmarking long-read sequencing approaches to resolve facioscapulohumeral dystrophy locus complexity’ by Tardy et al. (https://doi.org/10.1093/brain/awag029).

Inherited myopathies are genetic disorders characterised by declining motor function due to progressive muscle weakening and wasting. Recently, pathogenic variants in PAX7, the master transcriptional regulator of muscle stem cells, have been associated with myopathies of variable severity, arguing for impaired satellite cell function as the main pathogenic driver. Here, we report the characterisation of two missense PAX7 variants in a patient with asymmetric, progressive muscle weakness affecting facial, upper and lower body muscles, and myopathic changes on muscle pathology. Despite this disorder closely phenocopying the clinical presentation of Facioscapulohumeral muscular dystrophy (FSHD), genetic, epigenetic and transcriptomic profiling indicated that FSHD was unlikely. However, exome sequencing revealed two heterozygous variants in PAX7: c.335 C > T, (p.Pro112Leu) and c.1328 G > A (p.Cys443Tyr). Modelling these PAX7 variants in human myoblasts resembled the transcriptomic findings found in the muscle biopsy from the patient. Specifically, these PAX7 variants caused upregulation of splicing factors, an increase in mitochondrial reactive oxygen species levels and reduced cell proliferation. The phenotypic cell changes caused by the PAX7 variants support a pathomechanism whereby diminished satellite cell function impairs muscle homoeostasis. Together, multimodal investigation suggests that these variants in PAX7 are likely causative of an FSHD-like autosomal recessive myopathy and expand the spectrum of neuromuscular disorders originating from impaired satellite cell function.

BACKGROUND:Mutations in the FBXO7 gene (PARK15) cause an autosomal recessive, early-onset neurodegenerative disorder typically presenting as Parkinsonian-Pyramidal Syndrome (PPS). Despite its recognition, the high phenotypic variability often delays diagnosis. Here, we report a novel Italian family and synthesize data from all published cases to date, offering an updated clinical and molecular overview of the disease. METHODS:We performed clinical and molecular characterization of a newly identified family. Furthermore, we conducted a systematic literature review (from 2008 to 2026) to aggregate clinical, genetic, and geographic data of all reported PARK15 cases. RESULTS:Two siblings presented with a complex phenotype including early-onset parkinsonism, cognitive decline, psychiatric symptoms, and aphasia-type speech disorders. Genetic analyses identified two novel likely pathogenic variants: a missense substitution in the UBL domain (p.Ile74Met) and a frameshift indel (p.Val233GlufsTer8). The literature review (incorporating clinical data from Europe, Asia, and South America) confirms a high prevalence of postural instability (87.5%), bradykinesia (83.3%), and pyramidal signs (~60%). We observed a distinct distribution of variants: missense mutations cluster in the N-terminal UBL and F-box domains, while truncating variants are more common in the C-terminal region. DISCUSSION:Our findings expand the FBXO7 mutational landscape and underscore the "atypical" clinical markers, such as pyramidal signs and cognitive decline, that distinguish PARK15 from other recessive forms of parkinsonism like PARK2 and PARK6. The dual role of FBXO7 in mitochondrial quality control and proteasomal assembly suggests a broad disruption of cellular homeostasis. These observations refine genotype-phenotype correlations and may guide variant interpretation in routine diagnostic settings.

Background/Objectives: Centralizing genetic sequencing in specialized facilities is pivotal for reducing the costs associated with diagnostic testing. These centers must be able to verify data quality and ensure sample integrity. This study aims at developing a protocol for tracking NGS-analyzed samples to prevent errors and mix-ups, ensuring proper quality control, accuracy, and reliability in genetic testing procedures. To this purpose, a protocol based on the genotyping of a panel of 60 single-nucleotide polymorphisms (SNPs) by OpenArrayTM technology was employed. Methods: The protocol was initially tested on a cohort of 758 samples and subsequently validated on a cohort of 100 samples. Furthermore, its ability to accurately detect identical and different samples was evaluated through a simulation test conducted on an additional 100 samples. Results: In total, 55 probes achieved a call rate ≥90% and were subjected to the sample matching process performed by an R tool specifically developed. The SNP panel achieved a random match probability of 3.29 × 10−15, proving its suitability for efficiently tracking samples and rapidly identifying any errors or mix-up during the analytical processing. Conclusions: The features of OpenArrayTM technology, cost-effectiveness, rapid analysis, and high discriminative power make it a suitable tool for sample tracking. In conclusion, this method represents a valuable example for promoting laboratory centralization and minimizing the risks related to different laboratory procedures and the management of a high number of samples.

Background/Objectives: Pathogenic variants in the PRPH2 gene are implicated in a wide spectrum of Inherited Retinal Dystrophies (IRDs), which show significant phenotypic heterogeneity. This study combines genomic, clinical, and instrumental data, including BCVA, OCT, ERG, and visual field testing, using a multimodal approach to identify known and novel PRPH2 variants, with the aim of refine genotype–phenotype correlations and improving the diagnosis of IRDs. Methods: A total of 830 Italian subjects diagnosed with IRDs by the multimodal clinical approach underwent WES on the Illumina® Next-Seq 550 system. Genetic variants were evaluated by considering type, frequency, and pathogenicity using dedicated databases and bioinformatics tools. Results: WES analysis led to the identification of three novel PRPH2 variants (c.653C>G, c.700T>C, c.121del) and seven previously reported variants (c.424C>T, c.458A>G, c.461_463del, c.493T>C, c.499G>A, c.612C>G, c.734dup) documented in public databases and the scientific literature. Conclusions: Our data confirm the wide spectrum of IRDs associated with PRPH2 genetic variants and highlight the importance of integrating genetic, clinical, and instrumental data. This strategy enhances diagnostic accuracy and strengthens genotype–phenotype correlations, ultimately improving clinical decision-making and personalized patient management.

Facioscapulo-humeral muscular dystrophy is characterized by a distinctive phenotype, although a wide range of clinical expressions is observed, possibly reflecting different disease progression rates or complex genetic mechanisms. To date, the diagnostic criteria for FSHD rely on identifying the genetic signature of the disease (reduced D4Z4 allele, permissive 4q allele, hypomethylation, and in some cases variants in modifier genes). However, interpreting genetic data requires careful correlation with the phenotype, especially in atypical cases. The study included a cohort of 42 patients with a D4Z4 contraction or belonging to a pedigree in which DRAs segregated but who were selected due to presenting atypical clinical features or an unexpected disease severity according to the Comprehensive Clinical Evaluation Form (CCEF). The 42 underwent 4q subtype analysis, DNA methylation assessment, whole-exome sequencing (WES) and segregation analysis. In 24 cases, WES identified likely pathogenic or pathogenic variants in genes associated with different neuromuscular disorders, in some cases possibly compatible with the observed phenotype. Methylation analysis proved useful in distinguishing asymptomatic and atypical cases, prompting differential diagnosis. Our results emphasize the importance of a detailed phenotypic characterization of patients with a suspicion of FSHD and, in the case of atypical phenotypes, the combination of D4Z4 sizing with other procedures such as WES.

The increasing burden of cancer globally necessitates innovative approaches for diagnosis, prognosis, and treatment. This article explores the transformative impact of genomics and artificial intelligence (AI) in precision oncology, addressing how their convergence is reshaping cancer care and its challenges. Methods: This review synthesizes current research on the applications of genomics, including next-generation sequencing, and AI, such as machine learning and deep learning, across the cancer care continuum. It examines their roles in identifying genetic variants, assessing cancer risk, guiding targeted therapies and immunotherapy, predicting treatment response, and enabling early detection through liquid biopsies. Results: Genomics and AI are revolutionizing oncology by enabling personalized treatment strategies, improving early detection, and overcoming drug resistance. AI enhances the interpretation of complex genomic data, facilitates drug repurposing, and accelerates the development of novel therapeutics. However, challenges remain regarding data standardization, interpretability, bias in AI algorithms, and ethical considerations. Conclusions: The integration of genomics and AI holds immense potential to advance precision oncology, offering more effective, equitable, and sustainable cancer care. Addressing current challenges and fostering interdisciplinary training will be crucial to fully harness these technologies and redefine oncology practice.

BACKGROUND:Variants in the Kinesin-family member 5A (KIF5A) gene are associated with a range of motor diseases, and a strong correlation between the protein domains (motor, stalk and tail) and the clinical phenotype has been proposed. However, several studies have reported exceptions contributing to a complex genotype-phenotype correlation in recent years. Further studies are needed to improve our knowledge about the prevalence of KIF5A variants and their genotype-phenotype correlation. METHODS:390 patients (220 hereditary spastic paraplegia, 80 Charcot-Marie-Tooth disease type 2 and 90 amyotrophic lateral sclerosis) have been selected for next-generation sequencing Clinical Exome. RESULTS:Five patients have been found to carry causative variants in the KIF5A gene. Of these, three are familiar cases, and two are sporadic. Segregation analysis was performed on the familiar probands. The five patients with pathogenic variants represent 4% of the studied population, and the clinical and genetic analysis of these five families allowed us to examine different scenarios.Some of these data support the hypothesis of a complex correlation between domains and disease. CONCLUSION:These data confirm the complex genotype-phenotype correlation, both in terms of clinical heterogeneity associated with a specific domain and variability within the members of the same family, but also suggest a strong genotype-phenotype correlation, both intrafamiliar and interfamiliar, produced by a few variants.

The integration of pharmacogenetics into personalized medicine enables the optimization of drug selection and dosage, maximizing therapeutic benefits while minimizing the risk of adverse drug reactions. The association between APOE alleles and ARIA, a known adverse reaction in Alzheimer’s disease patients treated with anti-amyloid monoclonal antibodies, has led to the inclusion of APOE genotyping among conventional pharmacogenetic tests. Given the dual role of APOE alleles, the widespread implementation of this genetic test requires caution and should be accompanied by appropriate genetic counselling. APOE genotyping is uniquely positioned at the intersection of pharmacogenetics and germline testing: it provides insight not only into drug safety (specifically the risk of Amyloid-Related Imaging Abnormalities) but also into familial risk for developing Alzheimer’s disease. Carriers of risk alleles, especially homozygotes, face the highest risk and require close monitoring. While APOE genotyping can inform treatment decisions, it also raises ethical concerns due to the broader implications of disclosing genetic risk information for neurodegenerative diseases. Identifying a high-risk APOE genotype in a patient substantially impacts family members. Therefore, patients considered for treatment with anti-amyloid monoclonal antibodies should receive comprehensive pre- and post-test genetic counseling that goes beyond traditional standards, as currently provided for other peculiar tests. Such counseling ensures that patients are adequately informed about potential outcomes, psychological impacts, and familial implications. It also supports ethical decision-making and facilitates truly informed consent, helping to prevent deterministic or overly simplistic interpretations of genetic risk.

Selective elimination of early pathological TAU species may be a promising therapeutic strategy to reduce the accumulation of TAU, which contributes to neurodegeneration and is a hallmark of Alzheimer’s disease (AD). Pathological hyper-phosphorylated TAU can be degraded through selective autophagy, and NDP52/CALCOCO2 is one of the autophagy receptors involved in this process. In 2021, we discovered a variant of NDP52, called NDP52GE (rs550510), that is more efficient at promoting autophagy. We here anticipate that this variant could be a powerful factor that could eliminate pathological forms of TAU better than its WT form (NDP52WT). Indeed, we provide evidence that in in vitro systems and in a Drosophila melanogaster model of TAU-induced AD, the NDP52GE variant is much more effective than the NDP52WT in reducing the accumulation of pathological forms of TAU through the autophagic process and rescues typical neurodegenerative phenotypes induced by hTAU toxicity. Mechanistically, we showed that NDP52WT and NDP52GE bind pTAU with comparable efficiency, but that NDP52GE binds the autophagic machinery (LC3C and LC3B) more efficiently than NDP52WT does, which could explain its greater efficiency in removing pTAU. Finally, by performing a genetic analysis of a cohort of 435 AD patients, we defined the NDP52GE variant as a protective factor for AD. Overall, our work highlights the variant NDP52GE as a resilience factor in AD that shows a robust effectiveness in driving pathological TAU degradation.

Background/Objectives: Cancer risk-reducing strategies in Ashkenazi women carrying founder variants have a cost-effective effect on reducing cancer morbidity and mortality. The British and US guidelines recommend BRCA1/2 (BRCA) screening among Ashkenazi Jewish people to identify high-risk individuals. BRCA status has not been investigated yet in the Jewish community of Rome. Methods: Patients were selected from the Family Cancer Clinic of the Umberto I University Hospital of Rome, and 38 unrelated families (28 of Roman Jewish and 10 of Libyan Jewish origin) were enrolled, comprising 44 subjects diagnosed with breast and/or ovarian cancer. Genetic counseling and germline BRCA testing were conducted. Haplotype analysis was performed. Results: Of the probands, 26.5% (9/34) from 7/28 unrelated families (25%) in the Jewish community of Rome harbored the known BRCA2 c.7007G>C, p. (Arg2336Pro) variant (rs28897743). Genetic analysis of the four unrelated carriers revealed a shared haplotype, indicating a potential founder effect. The length of the haplotype might confirm the Roman community to be the oldest among Jewish communities in Europe. Conclusions: This study indicates the BRCA2 c.7007G>C variant found in the Jewish community of Rome to be a founder variant. Finally, we underline a pressing need to address the increased risk of carrying BRCA mutations among individuals with Jewish heritage, and to enhance genetic counseling and screening efforts in ethnic minorities that are not otherwise routinely reached.

Artificial Intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use ChatGPT holds a prom-inent position using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for gen-erating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT in-tegration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex data sets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

Facioscapulohumeral dystrophy (FSHD) is a myopathy characterized by the loss of repressive epigenetic features affecting the D4Z4 locus (4q35). The assessment of DNA methylation at two regions (DUX4-PAS and DR1) of D4Z4 locus proved to be an effective method to detect epigenetic signatures compatible with FSHD. The present study aims at validating the employment of this method into clinical practice and improving the protocol by refining the classification thresholds of 4qA/4qA patients. To this purpose, 218 subjects with clinical suspicion of FSHD collected in 2022–2023 were analyzed. Each participant underwent in parallel the traditional FSHD molecular testing (D4Z4 sizing) and the proposed methylation assay. The results provided by both analyses were compared to evaluate the concordance and calculate the performance metrics of the methylation test. Among the 218 subjects, the 4q variant type distribution was 54

Rare diseases are heterogeneous diseases characterized by various symptoms and signs. Due to the low prevalence of such conditions (less than 1 in 2000 people), medical expertise is limited, knowledge is poor and patients’ care provided by medical centers is inadequate. An accurate diagnosis is frequently challenging and ongoing research is also insufficient, thus complicating the understanding of the natural progression of the rarest disorders. This review aims at presenting the multimodal approach supported by the integration of multiple analyses and disciplines as a valuable solution to clarify complex genotype–phenotype correlations and promote an in-depth examination of rare disorders. Taking into account the literature from large-scale population studies and ongoing technological advancement, this review described some examples to show how a multi-skilled team can improve the complex diagnosis of rare diseases. In this regard, Facio-Scapulo-Humeral muscular Dystrophy (FSHD) represents a valuable example where a multimodal approach is essential for a more accurate and precise diagnosis, as well as for enhancing the management of patients and their families. Given their heterogeneity and complexity, rare diseases call for a distinctive multidisciplinary approach to enable diagnosis and clinical follow-up.

Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.