Rare Disease Research Research Articles

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.

Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions.

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.

PFMNet: Prototype-based feature mapping network for few-shot domain adaptation in medical image segmentation

Lack of data is one of the biggest hurdles for rare disease research using deep learning. Due to the lack of rare-disease images and annotations, training a robust network for automatic rare-disease image segmentation is very challenging. To address this challenge, few-shot domain adaptation (FSDA) has emerged as a practical research direction, aiming to leverage a limited number of annotated images from a target domain to facilitate adaptation of models trained on other large datasets in a source domain. In this paper, we present a novel prototype-based feature mapping network (PFMNet) designed for FSDA in medical image segmentation. PFMNet adopts an encoder–decoder structure for segmentation, with the prototype-based feature mapping (PFM) module positioned at the bottom of the encoder–decoder structure. The PFM module transforms high-level features from the target domain into the source domain-like features that are more easily comprehensible by the decoder. By leveraging these source domain-like features, the decoder can effectively perform few-shot segmentation in the target domain and generate accurate segmentation masks. We evaluate the performance of PFMNet through experiments on three typical yet challenging few-shot medical image segmentation tasks: cross-center optic disc/cup segmentation, cross-center polyp segmentation, and cross-modality cardiac structure segmentation. We consider four different settings: 5-shot, 10-shot, 15-shot, and 20-shot. The experimental results substantiate the efficacy of our proposed approach for few-shot domain adaptation in medical image segmentation.

E014 Migration of the large complex dataset of the UK Juvenile Dermatomyositis Cohort Biomarker Study: challenges and lessons

Abstract Background/Aims In rare disease research, longitudinal prospective cohort studies are important tools to generate invaluable real-world evidence to inform treatment choices and understand outcomes. The Juvenile Dermatomyositis Cohort Biomarker Study & Repository (JDCBS) is a multi-centre, prospective longitudinal cohort study with linked biobank, established in 2000, with 17 active UK-Centres contributing data and samples. It supported over 60 projects generating new evidence, treatments and biomarkers in the field of juvenile myositis. The database in use until March 2023 used aged technology which did not sufficiently meet current needs of the study. REDCap is secure web application for hosting online databases, able to support online longitudinal data capture for multi-centre research studies. The aim of this project was to build and deliver a future-proofed environment and migrate the large JDCBS dataset into the REDCap platform. Methods Clinical, laboratory and outcome data at clinic visits were previously captured through a web interface which loaded data into an MS-Access back-end database. In this project, an internationally agreed set of data elements for use in research in juvenile myositis was adopted and a new data entry platform built, using REDCap and a Trusted Research Environment hosted at Great Ormond Street Digital Research Environment. A multidisciplinary team was formed to deliver migration, including clinicians, study investigators, systems analysts, and health informatics data scientists. Careful comparison of fields in the existing database to those in the internationally agreed dataset before delivery of the new REDCap database and migration of the legacy data took place. Results Key steps to deliver the REDCap interface and migrate the data included: 1. Agreement of data elements to be included, dropped or added, compared to the legacy dataset; 2. Design of study forms and fields within REDCap; 3. Testing new interface; 4. Script design and testing for migration; 5. Data cleaning of legacy dataset; 6. Training 15 collaborators and REDCap accounts management for 63 collaborators; 6. Migration of legacy dataset and quality control of migrated data; 7. Post-migration data checking and feedback on use of the new platform; 8. Summary reports of process, data quality and audit trail of project. In total, 670 patient records and 7431 clinical forms, with data from 338 data fields, were successfully migrated to the new REDCap database. A median value of 11 clinical forms (IQR: 6-18), and 3,718 data points (IQR: 2,028-6084) were migrated per patient. Conclusion Migration of a multi-dimensional longstanding study data set is a complex multi-step task requiring multi-disciplinary engagement of stakeholders. Key elements to achieve success include early agreement of data fields for inclusion, strict definitions of fields, regular engagement with database users, providing robust training, enabling feedback, and acceptance that research datasets should strive to be parsimonious in fields collected. Disclosure D. Cancemi: None. R. Hamilton: None. S. Varakliotis: None. H.D. Nguyen: None. N. Sabire: None. N. Martin: None. L. McCann: None. C. Papadopoulou: None. L. Wedderburn: Other; LW has non-renumerated collaboration with Novartis Inc, relevant to this work.

A complement C4-derived glycopeptide is a biomarker for PMM2-CDG.

BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).

Methodological advances in patient-centered rare disease research: the UTHealth Houston Turner Syndrome Society of the United States research registry

BackgroundMany different clinical specialists provide care to patients with Turner syndrome (TS), who have highly variable clinical manifestations. Therefore, a national TS registry is essential to inform a cohesive approach to healthcare and research. In 2015, the Turner Syndrome Society of the United States (TSSUS) created the Turner Syndrome Research Registry (TSRR) to engage directly with community participants who voluntarily provide longitudinal data about their experiences with TS. TSRR projects are collaborative partnerships between people with TS, TSSUS, and researchers.ResultsTo ensure that registry workflows conform to the data privacy choices of participants, TSSUS collaborated with UTHealth Houston in 2021 to create a new version of the TSRR that completely separates participant health data (stored at UTHealth) and personal identifiers (maintained at TSSUS). We developed an innovative Visual Basic (VB) script that, when embedded into Microsoft Outlook, redirects REDCap surveys through TSSUS to participants by matching registry IDs to participant email addresses. Additionally, the utilization of REDCap allows for portability of data as it is an open source platform.ConclusionIn this report, we will highlight three recent changes that more closely align the TSRR with this mission: a unique and equal collaborative partnership between UTHealth and TSSUS, an open-source platform, REDCap, that ensures data portability and compatibility across institutions, and an innovative survey routing system that retains participant confidentiality without sacrificing REDCap survey distribution capabilities to connect researchers with thousands of participants.

Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations

BackgroundThe conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches.MethodsIn very small populations, methodological challenges exacerbate. iSTORE’s ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence.ResultsThe methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias.ConclusionThrough its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

Designing an accessible and equitable conference and the evaluation of the barriers to research inclusion for rare disease communities

Introduction: The United Kingdom Rare Diseases Framework, published in 2021, addresses the lack of diversity in rare disease research, particularly in clinical trials, due to intersecting issues related to rare disease symptoms and health inequalities. The framework outlines the government's commitment to improving the lives of the 3.5 million people living with rare conditions in the United Kingdom.Objective: The primary objective is to advance equality, diversity, and inclusion in research by developing innovative methods to engage with communities, establish trust, and enhance the participation and voices of underrepresented and underserved communities.Methods: The conference was held in May 2023, bringing together voices with lived experiences from rare and diverse communities to meet and discuss with established researchers, policy influencers and advocates. The evaluation design was developed using the Program Logic Model and utilised a conference evaluation form on barriers to inclusion.Results: The Rare Disease Community identifies disability as the primary obstacle to inclusion. Social Care and Public Health Communities and Clinical Research Communities identified a need for more cultural competency. The Diverse Community selected psychosocial issues, and the Academic Community identified funding as the most significant barrier.Conclusions: The Community of Practice workshops showed the variety of lived experiences and potential barriers people confront for inclusion in research. Listening to individual viewpoints was crucial to creating or repairing community trust. The Academic Community felt that research funders and ethics review boards must adapt their institutional practices to include financial resources for outreach and participation in research design.

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

The rare genetic disease research landscape in India

The rare genetic disease research landscape in India

Advancing diagnosis and research for rare genetic diseases in Indigenous peoples.

Advancing diagnosis and research for rare genetic diseases in Indigenous peoples.

Does gene therapies clinical research in rare diseases reflects the competitivity of the country: Example of France

Rare diseases are chronic, serious and generally genetic conditions affecting a small number of people, and their therapeutic management is a real challenge. They represent a considerable burden for patients, caregivers and society alike. Compared with existing symptomatic treatments, gene therapies represent a promising new approach aimed at treating these diseases by replacing a defective gene, or by abolishing or reviving a gene-derived function. France is considered one of the leading countries in the research and development of drugs for rare diseases, yet the position of French public and private stakeholders in the research and development of gene therapies for rare diseases at global and European level remains unclear. To answer this question, we used the GENOTRIAL FR database developed by OrphanDev to clarify France's involvement and competitiveness in this field. The results show that France is actively involved in gene therapy clinical trials, with a dense international collaboration network and solid expertise. However, the French medical infrastructure is mainly involved in clinical research on gene therapy candidates sponsored by several foreign countries. To a lesser extent, French public and private entities are also developing their own gene therapy candidates for various rare diseases, some of which have already reached advanced clinical phases. In conclusion, a number of technical and financial challenges need to be overcome if France is to maintain its position as a European and world leader and increase its contribution to reducing the economic and social burden of rare diseases by developing revolutionary and effective new therapies.

Collaborative research in myositis-related disorders: MIHRA, a global shared community model.

Myositis International Health and Research Collaborative Alliance (MIHRA) is a newly formed purpose-built non-profit charitable research organization dedicated to accelerating international clinical trial readiness, global professional and lay education, career development and rare disease advocacy in IIM-related disorders. In its long form, the name expresses the community's scope of engagement and intent. In its abbreviation, MIHRA, conveys linguistic roots across many languages, that reflects the IIM community's spirit with meanings such as kindness, community, goodness, and peace. MIHRA unites the global multi-disciplinary community of adult and pediatric healthcare professionals, researchers, patient advisors and networks focused on conducting research in and providing care for pediatric and adult IIM-related disorders to ultimately find a cure. MIHRA serves as a resourced platform for collaborative efforts in investigator-initiated projects, consensus guidelines for IIM assessment and treatment, and IIM-specific career development through connecting research networks.MIHRA's infrastructure, mission, programming and operations are designed to address challenges unique to rare disease communities and aspires to contribute toward transformative models of rare disease research such as global expansion and inclusivity, utilization of community resources, streamlining ethics and data-sharing policies to facilitate collaborative research. Herein, summarises MIHRA operational cores, missions, vision, programming and provision of community resources to sustain, accelerate and grow global collaborative research in myositis-related disorders.

Developing a community-led rare disease ELSI research agenda.

Research priorities are best defined through engagement with communities who will be impacted by the research and have lived experience of the topics to be studied. We aimed to establish a pediatric rare disease community stakeholder group and empower them in (1) eliciting perspectives from affected families in the wider region and (2) synthesizing collective ideas into a research agenda focused on shared ethical, legal, and social implications (ELSI) across rare disease. This two-year project utilized a community-centered approach to engage rare disease community members as equal partners in developing a research agenda for ELSI in rare disease. We established "Rare Voices" (RV), a 22-member stakeholder group of patients, parents, clinicians and researchers. Following capacity-building trainings, RV designed and conducted listening sessions with teen patients and parents of children with rare diseases to explore challenges, positive experiences, and ethical concerns. Listening session findings were synthesized and contextualized into research topics, which RV members further refined and prioritized. We used established measures to assess RV member engagement and satisfaction. From 14 listening sessions with parents (n = 52) and teen patients (n = 13), RV identified eight core research topics as most important for future rare disease research: coordinating care, communication, accessing resources and care, impact on family unit, community and support in society, mental health and identity, ethical aspects of care, and uncertainty. RV members were highly engaged throughout the two-year project and reported high levels of satisfaction with the experience and research agenda. Through capacity-building and authentic engagement, this project resulted in a community-led rare disease research agenda to guide future rare disease ELSI research that aligns with patients' and families' priorities. An environment of equal partnership and respect created a space for mutual learning where community members were empowered to shape the research agenda based on their collective experiences. The agenda recognizes the shared psychosocial and healthcare experiences of rare disease and offers practical areas of research to address patient and family needs.

Making progress against rare cancers: A case study on neuroendocrine tumors.

In April 2023, the National Cancer Institute offered a roadmap for cancer research to achieve Cancer Moonshot goals. To reach these goals requires making progress for all cancers, not just those that are most common. Achieving progress against rare cancers, as well as common cancers, requires involvement of large clinical research networks. In 2020, the Patient-Centered Outcomes Research Institute (PCORI) launched an initiative on Conducting Rare Disease Research using PCORnet, the National Patient-Centered Clinical Research Network. The purpose of this commentary is to introduce the broader community of cancer researchers to the PCORnet NET-PRO study (comparing the effects of different treatment approaches for neuroendocrine tumors on patient-reported outcomes) thereby demonstrating how researchers can use the PCORnet infrastructure to conduct large-scale patient-centered studies of rare cancers.

Perry Disease: Bench to Bedside Circulation and a Team Approach.

With technological applications, especially in genetic testing, new diseases have been discovered and new disease concepts have been proposed in recent years; however, the pathogenesis and treatment of these rare diseases are not as well established as those of common diseases. To demonstrate the importance of rare disease research, in this paper we focus on our research topic, Perry disease (Perry syndrome). Perry disease is a rare autosomal dominant neurodegenerative disorder clinically characterized by parkinsonism, depression/apathy, weight loss, and respiratory symptoms including central hypoventilation and central sleep apnea. The pathological classification of Perry disease falls under TAR DNA-binding protein 43 (TDP-43) proteinopathies. Patients with Perry disease exhibit DCTN1 mutations, which is the causative gene for the disease; they also show relatively uniform pathological and clinical features. This review summarizes recent findings regarding Perry disease from both basic and clinical perspectives. In addition, we describe technological innovations and outline future challenges and treatment prospects. We discuss the expansion of research from rare diseases to common diseases and the importance of collaboration between clinicians and researchers. Here, we highlight the importance of researching rare diseases as it contributes to a deeper understanding of more common diseases, thereby opening up new avenues for scientific exploration.

Clinical evaluation of code-based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases.

Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code-based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H-specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well-defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best-performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code-based PAH algorithms to their research question and demonstrate the importance of including well-defined temporal components in the algorithms.

GeNepher data- and biobank for patients with (suspected) genetic kidney disease: Rationale, design and status update

BackgroundClinical research on monogenic kidney disease (MKD) is thriving and the need for large cohorts, prospective data collection and biobanking is increasing. We aim to create a sustainable large MKD biobank with a vast amount of uniformly collected high-quality data that is readily available for future research, with an infrastructure that allows for recontacting participants. MethodsThe GeNepher data- and biobank is an ongoing data- and sample collection that includes patients and family members with known and/or suspected MKD. With a tiered approach participants can give broad consent for including their 1) available medical data (including genetic testing results), 2) inclusion of massively parallel sequencing data for add-on analysis, and 3) additional biobank sampling (e.g. urine for tubuloids, skin biopsy for fibroblasts). Recontacting is possible for additional data collection, novel research opportunities and return of relevant findings. DiscussionThe GeNepher data- and biobank collects prospective and retrospective data from kidney disease patients and their relatives. The broad consent allows for research that extends beyond one specific research question. Herewith, this biobank aims to 1) increase the scientific knowledge based on disease mechanisms including (novel) monogenic causes, 2) study modifiers, 3) improve care, including reproduction related research questions. Furthermore, it facilitates recontacting for opportunities in treatment development or when diagnose specific trials are started or specific treatment is approved. ConclusionThe GeNepher biobank is designed to support a wide range of research projects by providing access to a diverse population of patients with (suspected) MKD and has the potential to make a significant contribution to the field of rare kidney disease research.

Advancing Treatments for Rare Renal Diseases: New Hopes and Opportunities to Address a High Unmet Need

Introduction: The etiology of chronic kidney disease (CKD) is varied and complex. Diabetes and hypertension comprise 2/3 of cases and rare conditions, including inherited genetic diseases, comprise 1/3. We previously reported a 54% increase in clinical studies in CKD in the last 10 years. Hypothesizing a greater increase in rare renal disease studies, we undertook further analysis of metadata from Clinicaltrials.gov. Methods: CT.gov was searched for 49 conditions determined to be rare renal diseases posted between Jan-2003 and Dec-2022. Studies were divided into 2 time periods: P1 (2003–2012) and P2 (2013–2022) and analyzed by study type, phase, indication, primary endpoint, population, and funding. Results: Studies increased significantly in P2 versus P1 (123%, p < 0.001) with the greatest rise in observational studies (283%, p < 0.001). Interventional studies increased 93% (p < 0.01), with the greatest rise in early phases (205%, p < 0.001). The most frequent indications were LN, ADPKD, and IgA nephropathy; all increased 77–166% in P2 (p < 0.05 – p < 0.001). Proteinuria was the most frequent primary endpoint, which increased 63% (p = 0.054). Studies with pediatric populations increased 78% (p < 0.01). Most studies were nonindustry funded; however, industry-funded studies increased by 225% (p < 0.001). Conclusion: Clinical research in rare renal diseases has increased significantly in the last 10 years, particularly in glomerular diseases (GDs) and ADPKD. Proteinuria correlates with outcomes in GD, which explains the high percentage of studies with this primary endpoint. Rare renal diseases disproportionately affect children and the rise in studies with pediatric populations is encouraging. The rise in observational studies may signal an increased focus on understanding the natural course and pathophysiology of disease, which may lead to new potential therapeutic targets and future interventional studies. The increase in industry-funded studies suggests basic science is translating into industry-sponsored research.

Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders-an important consideration for resource-constrained settings.

Exome sequencing (ES) is a recommended first-tier diagnostic test for many rare monogenic diseases. It allows for the detection of both single-nucleotide variants (SNVs) and copy number variants (CNVs) in coding exonic regions of the genome in a single test, and this dual analysis is a valuable approach, especially in limited resource settings. Single-nucleotide variants are well studied; however, the incorporation of copy number variant analysis tools into variant calling pipelines has not been implemented yet as a routine diagnostic test, and chromosomal microarray is still more widely used to detect copy number variants. Research shows that combined single and copy number variant analysis can lead to a diagnostic yield of up to 58%, increasing the yield with as much as 18% from the single-nucleotide variant only pipeline. Importantly, this is achieved with the consideration of computational costs only, without incurring any additional sequencing costs. This mini review provides an overview of copy number variant analysis from exome data and what the current recommendations are for this type of analysis. We also present an overview on rare monogenic disease research standard practices in resource-limited settings. We present evidence that integrating copy number variant detection tools into a standard exome sequencing analysis pipeline improves diagnostic yield and should be considered a significantly beneficial addition, with relatively low-cost implications. Routine implementation in underrepresented populations and limited resource settings will promote generation and sharing of CNV datasets and provide momentum to build core centers for this niche within genomic medicine.