Digenic Inheritance in Rare Disorders and Mitochondrial Disease-Crossing the Frontier to a More Comprehensive Understanding of Etiology.

Our understanding of rare disease genetics has been shaped by a monogenic disease model. While the traditional monogenic disease model has been successful in identifying numerous disease-associated genes and significantly enlarged our knowledge in the field of human genetics, it has limitations in explaining phenomena like phenotypic variability and reduced penetrance. Widening the perspective beyond Mendelian inheritance has the potential to enable a better understanding of disease complexity in rare disorders. Digenic inheritance is the simplest instance of a non-Mendelian disorder, characterized by the functional interplay of variants in two disease-contributing genes. Known digenic disease causes show a range of pathomechanisms underlying digenic interplay, including direct and indirect gene product interactions as well as epigenetic modifications. This review aims to systematically explore the background of digenic inheritance in rare disorders, the approaches and challenges when investigating digenic inheritance, and the current evidence for digenic inheritance in mitochondrial disorders.

An algorithm to identify patients aged 0-3 with rare genetic disorders.

BACKGROUND: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS).

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

The value of knowing: preferences for genetic testing to diagnose rare muscle diseases.

BACKGROUND: Genetic testing can offer early diagnosis and subsequent treatment of rare neuromuscular diseases. Options for these tests could be improved by understanding the preferences of patients for the features of different genetic tests, especially features that increase information available to patients. METHODS: We developed an online discrete-choice experiment using key attributes of currently available tests for Pompe disease with six test attributes: number of rare muscle diseases tested for with corresponding probability of diagnosis, treatment availability, time from testing to results, inclusion of secondary findings, necessity of a muscle biopsy, and average time until final diagnosis if the first test is negative. Respondents were presented a choice between two tests with different costs, with respondents randomly assigned to one of two costs. Data were analyzed using random-parameters logit. RESULTS: A total of 600 online respondents, aged 18 to 50 years, were recruited from the U.S. general population and included in the final analysis. Tests that targeted more diseases, required less time from testing to results, included information about unrelated health risks, and were linked to shorter time to the final diagnosis were preferred and associated with diseases with available treatment. Men placed relatively more importance than women on tests for diseases with available treatments. Most of the respondents would be more willing to get a genetic test that might return unrelated health information, with women exhibiting a statistically significant preference. While respondents were sensitive to cost, 30% of the sample assigned to the highest cost was willing to pay $500 for a test that could offer a diagnosis almost 2 years earlier. CONCLUSION: The results highlight the value people place on the information genetic tests can provide about their health, including faster diagnosis of rare, unexplained muscle weakness, but also the value of tests for multiple diseases, diseases without treatments, and incidental findings. An earlier time to diagnosis can provide faster access to treatment and an end to the diagnostic journey, which patients highly prefer.

Improving access to gene therapy for rare diseases.

Effective gene therapy approaches have been developed for many rare diseases, including inborn errors of immunity and metabolism, haemoglobinopathies and inherited blindness. Despite successful pre-clinical and clinical results, these gene therapies are not widely available, primarily for non-medical reasons. Lack of commercial interest in therapies for ultra-rare diseases, costs of development and complex manufacturing processes required for advanced therapy medicinal products (ATMPs) are some of the main problems that are restricting access. The complexities and costs of navigating the regulatory environments in different jurisdictions for treatments that affect small numbers of patients is a problem unique to ATMPS for rare and ultra-rare diseases. In this Perspective, we outline some of the challenges and potential solutions that, we hope, will improve access to gene therapy for rare diseases.

Patient preferences in genetic newborn screening for rare diseases: study protocol.

INTRODUCTION: Rare diseases (RDs) collectively impact over 30 million people in Europe. Most individual conditions have a low prevalence which has resulted in a lack of research and expertise in this field, especially regarding genetic newborn screening (gNBS). There is increasing recognition of the importance of incorporating patients' needs and general public perspectives into the shared decision-making process regarding gNBS. This study is part of the Innovative Medicine Initiative project Screen4Care which aims at shortening the diagnostic journey for RDs by accelerating diagnosis for patients living with RDs through gNBS and the use of digital technologies, such as artificial intelligence and machine learning. Our objective will be to assess expecting parent's perspectives, attitudes and preferences regarding gNBS for RDs in Italy and Germany. METHODS AND ANALYSIS: A mixed method approach will assess perspectives, attitudes and preferences of (1) expecting parents seeking genetic consultation and (2) 'healthy' expecting parents from the general population in two countries (Germany and Italy). Focus groups and interviews using the nominal group technique and ranking exercises will be performed (qualitative phase). The results will inform the treatment of attributes to be assessed via a survey and a discrete choice experiment (DCE). The total recruitment sample will be 2084 participants (approximatively 1000 participants in each country for the online survey). A combination of thematic qualitative and logit-based quantitative approaches will be used to analyse the results of the study. ETHICS AND DISSEMINATION: This study has been approved by the Erlangen University Ethics Committee (22-246_1-B), the Freiburg University Ethics Committee (23-1005 S1-AV) and clinical centres in Italy (University of FerraraCE: 357/2023/Oss/AOUFe and Hospedale Bambino Gesu: No.2997 of 2 November 2023, Prot. No. _902) and approved for data storage and handling at the Uppsala University (2022-05806-01). The dissemination of the results will be ensured via scientific journal publication (open access).

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Genomic Answers for Kids: Toward more equitable access to genomic testing for rare diseases in rural populations.

Next-generation sequencing has revolutionized the speed of rare disease (RD) diagnoses. While clinical exome and genome sequencing represent an effective tool for many RD diagnoses, there is room to further improve the diagnostic odyssey of many RD patients. One recognizable intervention lies in increasing equitable access to genomic testing. Rural communities represent a significant portion of underserved and underrepresented individuals facing additional barriers to diagnosis and treatment. Primary care providers (PCPs) at local clinics, though sometimes suspicious of a potential benefit of genetic testing for their patients, have significant constraints in pursuing it themselves and rely on referrals to specialists. Yet, these referrals are typically followed by long waitlists and significant delays in clinical assessment, insurance clearance, testing, and initiation of diagnosis-informed care management. Not only is this process time intensive, but it also often requires multiple visits to urban medical centers for which distance may be a significant barrier to rural families. Therefore, providing early, "direct-to-provider" (DTP) local access to unrestrictive genomic testing is likely to help speed up diagnostic times and access to care for RD patients in rural communities. In a pilot study with a PCP clinic in rural Kansas, we observed a minimum 5.5 months shortening of time to diagnosis through the DTP exome sequencing program as compared to rural patients receiving genetic testing through the "traditional" PCP-referral-to-specialist scheme. We share our experience to encourage future partnerships beyond our center. Our efforts represent just one step in fostering greater diversity and equity in genomic studies.

Between hope and reality: treatment of genetic diseases through nucleic acid-based drugs.

Rare diseases (RD) affect a small number of people compared to the general population and are mostly genetic in origin. The first clinical signs often appear at birth or in childhood, and patients endure high levels of pain and progressive loss of autonomy frequently associated with short life expectancy. Until recently, the low prevalence of RD and the gatekeeping delay in their diagnosis have long hampered research. The era of nucleic acid (NA)-based therapies has revolutionized the landscape of RD treatment and new hopes arise with the perspectives of disease-modifying drugs development as some NA-based therapies are now entering the clinical stage. Herein, we review NA-based drugs that were approved and are currently under investigation for the treatment of RD. We also discuss the recent structural improvements of NA-based therapeutics and delivery system, which overcome the main limitations in their market expansion and the current approaches that are developed to address the endosomal escape issue. We finally open the discussion on the ethical and societal issues that raise this new technology in terms of regulatory approval and sustainability of production.

The impact of rare cancer and early-line treatments on the benefit of comprehensive genome profiling-based precision oncology.

BACKGROUND: Comprehensive genome profiling (CGP) serves as a guide for suitable genomically matched therapies for patients with cancer. However, little is known about the impact of the timing and types of cancer on the therapeutic benefit of CGP. MATERIALS AND METHODS: A single hospital-based pan-cancer prospective study (TOP-GEAR; UMIN000011141) was conducted to examine the benefit of CGP with respect to the timing and types of cancer. Patients with advanced solid tumors (>30 types) who either progressed with or without standard treatments were genotyped using a single CGP test. The subjects were followed up for a median duration of 590 days to examine therapeutic response, using progression-free survival (PFS), PFS ratio, and factors associated with therapeutic response. RESULTS: Among the 507 patients, 62 (12.2%) received matched therapies with an overall response rate (ORR) of 32.3%. The PFS ratios (≥1.3) were observed in 46.3% (19/41) of the evaluated patients. The proportion of subjects receiving such therapies in the rare cancer cohort was lower than that in the non-rare cancer cohort (9.6% and 17.4%, respectively; P = 0.010). However, ORR of the rare cancer patients was higher than that in the non-rare cancer cohort (43.8% and 20.0%, respectively; P = 0.046). Moreover, ORR of matched therapies in the first or second line after receiving the CGP test was higher than that in the third or later lines (62.5% and 21.7%, respectively; P = 0.003). Rare cancer and early-line treatment were significantly and independently associated with ORR of matched therapies in multivariable analysis (P = 0.017 and 0.004, respectively). CONCLUSION: Patients with rare cancer preferentially benefited from tumor mutation profiling by increasing the chances of therapeutic response to matched therapies. Early-line treatments after profiling increase the therapeutic benefit, irrespective of tumor types.

Undiagnosed rare disease clinic identifies a novel UBE3A variant in two sisters with Angelman syndrome: The end of a diagnostic odyssey.

Angelman syndrome (AS, MIM #105830) is a neurodevelopmental disorder characterized by severe intellectual disability, profound developmental delay, movement or balance problems, an excessively cheerful disposition, and seizures. AS results from inadequate expression of the maternal UBE3A gene (MIM #601623), which encodes an E3 ligase in the ubiquitin-proteasome pathway. Here we present the case of two sisters with features consistent with AS who had negative methylation analyses. An autism/intellectual disability expanded panel revealed a maternally inherited novel UBE3A (NM_001354506.2) variant c.2443C>T p.(Pro815Ser) in both patients that was initially classified as a variant of uncertain significance. The patients were enrolled in Indiana University's Undiagnosed Rare Disease Clinic (URDC) to further investigate the variant. Additional data, including deep phenotyping, familial segregation analysis, and in silico studies, suggest that the variant is likely pathogenic. 3D modeling studies based on the available crystal structure revealed that the Pro815Ser variant can introduce more flexibility into the protein and alter its enzymatic activity. Recent literature confirms the pathogenic nature of the variant. Reanalysis of the UBE3A variant has heightened existing knowledge of AS and has offered this family an end to their diagnostic odyssey.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders.

BACKGROUND: In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. RESULTS: We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. CONCLUSIONS: 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis.

Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA's superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.

Using team-based precision medicine to advance understanding of rare genetic brain disorders.

We describe a multidisciplinary teamwork approach known as "Operation IDD Gene Team" developed by the Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (RFK IDDRC) at the Albert Einstein College of Medicine. This initiative brings families affected by rare genetic diseases that cause intellectual and developmental disability together with physicians, basic scientists, and their trainees. At team meetings, family members share their child's medical and personal history, physicians describe the broader clinical consequences of the condition, and scientists provide accessible tutorials focused on the fundamental biology of relevant genes. When appropriate, possible treatment approaches are also discussed. The outcomes of team meetings have been overwhelmingly positive, with families not only expressing deep gratitude, but also becoming empowered to establish foundations dedicated to their child's specific condition. Physicians, and in particular the scientists and their trainees, have gained a deeper understanding of challenges faced by affected families, broadening their perspective on how their research can extend beyond the laboratory. Remarkably, research by the scientists following the Gene Team meetings have often included focus on the actual gene variants exhibited by the participating children. As these investigations progress and newly created foundations expand their efforts, national as well as international collaborations are forged. These developments emphasize the importance of rare diseases as windows into previously unexplored molecular and cellular processes, which can offer fresh insights into both normal function as well as more common diseases. Elucidating the mechanisms of and treatments for rare and ultra-rare diseases thus has benefits for all involved-families, physicians, and scientists and their trainees, as well as the broader medical community. While the RFK IDDRC's Operation IDD Gene Team program has focused on intellectual disabilities affecting children, we believe it has the potential to be applied to rare genetic diseases impacting individuals of any age and encompassing a wide variety of developmental disorders affecting multiple organ systems.

Difficulties in disclosing secondary findings by facilities performing comprehensive germline genetic testing for rare diseases in Japan.

In Japan, a limited number of laboratories perform comprehensive genetic testing for rare diseases; this study investigated the attitudes of these laboratories toward the disclosure of secondary finding (SF). Following a preliminary survey, we identified laboratories conducting comprehensive genetic testing for participation. Subsequently, an online survey involving 20 selected facilities was conducted. The response rate was 80% (16/20). Of the 14 facilities, 71.4% had SFs. While 42.9% of them had a policy to disclose SFs with clinical utility, only 14.3% actively searched for actionable variants that could be included in the American College of Medical Genetics and Genomics list. Japan was less enthusiastic than the USA regarding SF disclosure. With regard to the reasons for not disclosing SFs, the factors "the thought that participants may have a low desire for SFs" and "uncertainty regarding their wish" were considered more important than in the USA. A content analysis of what was sought as a solution to this difficulty revealed a need to improve databases on pathogenicity and actionability and collect public thoughts on the issue. The factor "to promote entry in research" was not considered a critical reason for disclosing SFs, indicating that the thirst for information was not possibly due to anxiety but rather due to scientific interest. Japanese medical professionals may not be confident that society requires the disclosure of SFs. To improve the environment, it is necessary to survey the public regarding their thoughts on SF disclosure and discuss this issue in society.

Diagnosis of alpha-Mannosidosis: Practical approaches to reducing diagnostic delays in this ultra-rare disease.

Alpha-mannosidosis is an ultra-rare lysosomal disease that is caused by variants of the MAN2B1 gene on chromosome 19p13. These variants result in faulty or absent alpha-mannosidase in lysosomes, which leads to intracellular accumulation of mannose-containing oligosaccharides. Diagnosis of alpha-mannosidosis is often delayed, in part because of the rarity of the disease, its gradual onset and heterogeneity of presentation, but also because of the similarity of many signs and symptoms of the disease to those of other lysosomal diseases. Treatment of alpha-mannosidosis was previously limited to hematopoietic stem cell transplantation, but outcomes are variable and not all patients are eligible or have a suitable donor. Recently, an enzyme replacement therapy, recombinant human alpha-mannosidase (velmanase alfa), was approved for the treatment of non-neurological manifestations in adult and pediatric patients with alpha-mannosidosis. Treatment with velmanase alfa reduces serum levels of oligosaccharides, increases levels of immunoglobulin G, and improves patients' functional capacity and quality of life, although it is not effective for the neurologic phenotype because it does not cross the blood-brain barrier. Since the effects of velmanase alfa are more marked in children than adults, early diagnosis to allow early initiation of treatment has become more important. To support this, patient, parent/caregiver, and clinician awareness and education is imperative. A number of approaches can be taken to meet this goal, such as the development of disease registries, validated diagnostic algorithms, and screening tools, improved under-/post-graduate clinician education, easily accessible and reliable information for patients/families (such as that made available on the internet), and the formation of patient advocacy groups. Such approaches may raise awareness of alpha-mannosidosis, reduce the diagnostic delay and thus improve the lives of those affected.

Next-generation therapeutics for rare genetic disorders.

The therapeutic potential of the human genome has been explored through the development of next-generation therapeutics, which have had a high impact on treating genetic disorders. Classical treatments have traditionally focused on common diseases that require repeated treatments. However, with the recent advancements in the development of nucleic acids, utilizing DNA and RNA to modify or correct gene expression in genetic disorders, there has been a paradigm shift in the treatment of rare diseases, offering more potential one-time cure options. Advanced technologies that use CRISPR-Cas 9, antisense oligonucleotides, siRNA, miRNA, and aptamers are promising tools that have achieved successful breakthroughs in the treatment of various genetic disorders. The advancement in the chemistry of these molecules has improved their efficacy, reduced toxicity, and expanded their clinical use across a wide range of tissues in various categories of human disorders. However, challenges persist regarding the safety and efficacy of these advanced technologies in translating into clinical practice. This review mainly focuses on the potential therapies for rare genetic diseases and considers how next-generation techniques enable drug development to achieve long-lasting curative effects through gene inhibition, replacement, and editing.