scholarly journals Barriers and Considerations for Diagnosing Rare Diseases in Indigenous Populations

2020 ◽  
Vol 8 ◽  
Author(s):  
Carla S. D'Angelo ◽  
Azure Hermes ◽  
Christopher R. McMaster ◽  
Elissa Prichep ◽  
Étienne Richer ◽  
...  
Keyword(s):  
Rare Disease ◽  
Genetic Diseases ◽  
Disease Diagnosis ◽  
Indigenous Populations ◽  
Genomic Research ◽  
Medical Attention ◽  
Genomic Technologies ◽  
Rare Genetic Diseases ◽  
Research Consortium ◽  
Multi Stakeholder

Advances in omics and specifically genomic technologies are increasingly transforming rare disease diagnosis. However, the benefits of these advances are disproportionately experienced within and between populations, with Indigenous populations frequently experiencing diagnostic and therapeutic inequities. The International Rare Disease Research Consortium (IRDiRC) multi-stakeholder partnership has been advancing toward the vision of all people living with a rare disease receiving an accurate diagnosis, care, and available therapy within 1 year of coming to medical attention. In order to further progress toward this vision, IRDiRC has created a taskforce to explore the access barriers to diagnosis of rare genetic diseases faced by Indigenous peoples, with a view of developing recommendations to overcome them. Herein, we provide an overview of the state of play of current barriers and considerations identified by the taskforce, to further stimulate awareness of these issues and the passage toward solutions. We focus on analyzing barriers to accessing genetic services, participating in genomic research, and other aspects such as concerns about data sharing, the handling of biospecimens, and the importance of capacity building.

scholarly journals RDmap: A Map for Exploring Rare Diseases

2021 ◽  
Author(s):  
Jian Yang ◽  
Cong Dong ◽  
Huilong Duan ◽  
Qiang Shu ◽  
Haomin Li
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Diseases ◽  
Distance Matrix ◽  
Disease Diagnosis ◽  
Human Phenotype ◽  
Pathogenic Genes ◽  
Disease Information ◽  
Rare Genetic Diseases ◽  
Interactive Map

Abstract Background: The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases makes the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based on similarity will help clinicians and researchers understand and easily explore these diseases. Methods: A distance matrix of rare diseases included in Orphanet was measured by calculating the quantitative distance among phenotypes and pathogenic genes based on Human Phenotype Ontology (HPO) and Gene Ontology (GO), and each disease was mapped into Euclidean space. A rare disease map, enhanced by clustering classes and disease information, was developed based on ECharts. Results: A rare disease map called RDmap was published at http://rdmap.nbscn.org. Total 3,287 rare diseases are included in the phenotype-based map, and 3,789 rare genetic diseases are included in the gene-based map; 1,718 overlapping diseases are connected between two maps. RDmap works similarly to the widely used Google Map service and supports zooming and panning. The phenotype similarity base disease location function performed better than traditional keyword searches in an in silico evaluation, and 20 published cases of rare diseases also demonstrated that RDmap can assist clinicians in seeking the rare disease diagnosis. Conclusion: RDmap is the first user-interactive map-style rare disease knowledgebase. It will help clinicians and researchers explore the increasingly complicated realm of rare genetic diseases.

scholarly journals RDmap: a map for exploring rare diseases

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jian Yang ◽  
Cong Dong ◽  
Huilong Duan ◽  
Qiang Shu ◽  
Haomin Li
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Diseases ◽  
Distance Matrix ◽  
Disease Diagnosis ◽  
Human Phenotype ◽  
Pathogenic Genes ◽  
Disease Information ◽  
Rare Genetic Diseases ◽  
Interactive Map

Abstract Background The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases makes the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based on similarity will help clinicians and researchers understand and easily explore these diseases. Methods A distance matrix of rare diseases included in Orphanet was measured by calculating the quantitative distance among phenotypes and pathogenic genes based on Human Phenotype Ontology (HPO) and Gene Ontology (GO), and each disease was mapped into Euclidean space. A rare disease map, enhanced by clustering classes and disease information, was developed based on ECharts. Results A rare disease map called RDmap was published at http://rdmap.nbscn.org. Total 3287 rare diseases are included in the phenotype-based map, and 3789 rare genetic diseases are included in the gene-based map; 1718 overlapping diseases are connected between two maps. RDmap works similarly to the widely used Google Map service and supports zooming and panning. The phenotype similarity base disease location function performed better than traditional keyword searches in an in silico evaluation, and 20 published cases of rare diseases also demonstrated that RDmap can assist clinicians in seeking the rare disease diagnosis. Conclusion RDmap is the first user-interactive map-style rare disease knowledgebase. It will help clinicians and researchers explore the increasingly complicated realm of rare genetic diseases.

The Importance of Hematology Working Groups for Rare Genetic Diseases

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 33-34
Author(s):  
Marwan Elbagoury ◽  
Ohoud F. Kashari
Keyword(s):  
Rare Disease ◽  
Genetic Diseases ◽  
Correct Diagnosis ◽  
Coordination Of Care ◽  
Gulf Region ◽  
Diagnostic Odyssey ◽  
Whole Exome ◽  
Rare Genetic Diseases ◽  
Working Groups

Rationale Around the globe, it is now understood that individuals with Rare genetic Diseases routinely face limitations to getting access to diagnosis. Plans have been created to improve the requirements of the patient's communities, including access to multidisciplinary care, and proposing new corrections or amendments to existing strategies. In the gulf region, numerous proposals have been established to tackle the diagnosis and management Rare genetic Diseases. Introduction and Background Rare genetic diseases are characterized as life-long, serious conditions that debilitate or compromise life. Almost 80% of Rare genetic diseases are diagnosed during the childhood. Absence of access to these assets affect patients and their families living with complex needs that may incorporate day in and day out observing, continuous serious physical and formative medicines, remaining in the training framework, and now and then costly strength meds1. The underlying etiology may stay obscure for many patients with rare genetic diseases despite multiple investigations. patients may be assigned an incorrect diagnosis and be referred to several specialties until a correct diagnosis can be made. A correct diagnosis of rare genetic diseases may impact not only the patient's care but may have further implications for management and/or counselling of family members as well2. Also, Early diagnosis leading to early treatment to prevent long-term damage. Global Landscape3 Rarity of diseases is most commonly defined based on prevalence and incidence within a jurisdiction, or in some cases by a combination of factors based on severity and the existence or feasibility of alternative therapeutic options. Globally, the following areas of focus aimed at improving the delivery of health care for the rare disease population: - Improve access to early diagnosis, timely intervention, coordinated care for rare genetic disease patients and developing referral pathways for rare genetic disease patients to facilitate efficient care deliver - Provide educational resources and knowledge exchange opportunities to health professionals to allow them to better identify, manage and treat rare disea - support integrated peer networks, patient organizations to ensure that rare disease patients, their family/caregivers and support them to make informed decisions about their condition. The importance of having working groups for Rare genetic Diseases in Gulf region 4 - Encourage improved coordination of care and access to particular information for rare genetic diseaseses. - Create a complete system services suppliers over Gulf states. Assets and Gaps analysis 1- Early Detection and Diagnostics 5 There are resources that assist the diagnostic capacity and early detection for rare genetic diseases. · Whole Exome sequencing are used mainly for research purposes, despite the fact that their use will reduced diagnostic odyssey. · Lack of the availability of testing is dependent on budget support in some hospitals. - Timely Access to Evidence-based care 6 - Family doctors may not be well equipped to meet the needs of patients with rare hematological genetic diseases, even after diagnosis. - Poor access supportive services for adult care. - Access to genetic counseling for patients and families outside major academic hospitals7. References 1. Sawyer, S. L. et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin. Genet.89, 275-284 (2016). 2. Undiagnosed Diseases Network Manual of Operations. (2018). 3. Richter, T. et al. Rare Disease Terminology and Definitions-A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group. (2015). doi:10.1016/j.jval.2015.05.008 4. International Rare Disease Research Consortium& GUIDELINES Long version. (2013). 5. Clinical Handbook for Sickle Cell Disease Vaso-occlusive Crisis Provincial Council for Maternal and Child Health & Ministry of Health and Long-Term Care. (2017). 6. Therrell, B. L. et al. Current status of newborn screening worldwide: 2015. Seminars in Perinatology39, 171-187 (2015). 7. Stille, C. J. & Antonelli, R. C. Coordination of care for children with special health care needs. Current Opinion in Pediatrics16, 700-705 (2004). Figure Disclosures No relevant conflicts of interest to declare.

GeneBreaker - Variant simulation to improve the diagnosis of Mendelian rare genetic diseases

2020 ◽  
Author(s):  
Phillip A. Richmond ◽  
Tamar V. Av-Shalom ◽  
Oriol Fornes ◽  
Bhavi Modi ◽  
Alison M. Elliott ◽  
...  
Keyword(s):  
Rare Disease ◽  
Genetic Diseases ◽  
Genomic Medicine ◽  
Real Life ◽  
Variant Calling ◽  
Pathogenic Variants ◽  
Rare Genetic Diseases ◽  
Diagnosis Rate ◽  
Effective Use ◽  
Potential Pathogenicity

AbstractMendelian rare genetic diseases affect 5-10% of the population, and with over 5,300 genes responsible for ~7,000 different diseases, they are challenging to diagnose. The use of whole genome sequencing (WGS) has bolstered the diagnosis rate significantly. Effective use of WGS relies upon the ability to identify the disrupted gene responsible for disease phenotypes. This process involves genomic variant calling and prioritization, and is the beneficiary of improvements to sequencing technology, variant calling approaches, and increased capacity to prioritize genomic variants with potential pathogenicity. As analysis pipelines continue to improve, careful testing of their efficacy is paramount. However, real-life cases typically emerge anecdotally, and utilization of clinically sensitive and identifiable data for testing pipeline improvements is regulated and limiting. We identified the need for a gene-based variant simulation framework which can create mock rare disease scenarios, utilizing known pathogenic variants or through the creation of novel gene-disrupting variants. To fill this need, we present GeneBreaker, a tool which creates synthetic rare disease cases with utility for benchmarking variant calling approaches, testing the efficacy of variant prioritization, and as an educational mechanism for training diagnostic practitioners in the expanding field of genomic medicine. GeneBreaker is freely available at http://GeneBreaker.cmmt.ubc.ca.

scholarly journals Genomics of rare genetic diseases—experiences from India

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Sridhar Sivasubbu ◽  
◽  
Vinod Scaria
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Collaborative Research ◽  
Disease Burden ◽  
Genetic Diseases ◽  
Community Services ◽  
Health Concern ◽  
Healthy Population ◽  
Rare Genetic Diseases ◽  
Research Initiative

Abstract Home to a culturally heterogeneous population, India is also a melting pot of genetic diversity. The population architecture characterized by multiple endogamous groups with specific marriage patterns, including the widely prevalent practice of consanguinity, not only makes the Indian population distinct from rest of the world but also provides a unique advantage and niche to understand genetic diseases. Centuries of genetic isolation of population groups have amplified the founder effects, contributing to high prevalence of recessive alleles, which translates into genetic diseases, including rare genetic diseases in India. Rare genetic diseases are becoming a public health concern in India because a large population size of close to a billion people would essentially translate to a huge disease burden for even the rarest of the rare diseases. Genomics-based approaches have been demonstrated to accelerate the diagnosis of rare genetic diseases and reduce the socio-economic burden. The Genomics for Understanding Rare Diseases: India Alliance Network (GUaRDIAN) stands for providing genomic solutions for rare diseases in India. The consortium aims to establish a unique collaborative framework in health care planning, implementation, and delivery in the specific area of rare genetic diseases. It is a nation-wide collaborative research initiative catering to rare diseases across multiple cohorts, with over 240 clinician/scientist collaborators across 70 major medical/research centers. Within the GUaRDIAN framework, clinicians refer rare disease patients, generate whole genome or exome datasets followed by computational analysis of the data for identifying the causal pathogenic variations. The outcomes of GUaRDIAN are being translated as community services through a suitable platform providing low-cost diagnostic assays in India. In addition to GUaRDIAN, several genomic investigations for diseased and healthy population are being undertaken in the country to solve the rare disease dilemma. In summary, rare diseases contribute to a significant disease burden in India. Genomics-based solutions can enable accelerated diagnosis and management of rare diseases. We discuss how a collaborative research initiative such as GUaRDIAN can provide a nation-wide framework to cater to the rare disease community of India.

PP154 Funding Of Treatments For Rare Diseases In Singapore

2020 ◽  
Vol 36 (S1) ◽  
pp. 17-18
Author(s):  
Fiona Pearce ◽  
Liang Lin ◽  
Kwong Ng
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Diseases ◽  
Evaluation Process ◽  
Public Healthcare ◽  
Clinical Group ◽  
Eligibility Criteria ◽  
Number Of Patients ◽  
Emerging Treatments ◽  
Multi Stakeholder

IntroductionA national multi-stakeholder charity fund has been established in Singapore to provide targeted support to patients with rare genetic diseases whose treatment costs remain unaffordable despite government subsidies and insurance. This presentation will provide an overview of the evaluation, price-setting, and stakeholder engagement processes established to inform the first list of drugs eligible for funding under the Rare Disease Fund (RDF).MethodsThe local prevalence of “rare” and “ultra-rare” conditions was defined in line with international rates (≤4 in 10,000 and <2 in 50,000, respectively) to facilitate an analysis of the rare disease landscape in Singapore, and to identify patients most likely to benefit from the RDF. Public healthcare institutions proposed drugs for consideration, which underwent technical evaluation and were then assessed in line with eligibility criteria by an expert clinical group and prioritized by decision makers for funding.ResultsThe number of patients with select rare diseases in Singapore was lower than global estimates contextualized to the local setting. Supporting clinical evidence, funding decisions from overseas health technology assessment agencies, reference pricing considerations, and local budget impact analyses informed the first tranche of drugs (n = 5) recommended. Extensive engagement with pharmaceutical companies was needed to negotiate fair drug prices relative to overseas countries. Additional treatments will be included in the RDF once sufficient funds are raised.ConclusionsAs the evaluation process evolves, wider considerations of disease and treatment experiences from a multi-stakeholder standpoint should be included to inform RDF listings. There is also a need to balance the sustainability of the fund in the longer term with the number of emerging treatments that may require coverage in the future.

scholarly journals Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

2021 ◽  
Author(s):  
Francisco M. De La Vega ◽  
Shimul Chowdhury ◽  
Barry Moore ◽  
Erwin Frise ◽  
Jeanette McCarthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Genetic Variants ◽  
Genetic Disease ◽  
Genetic Diseases ◽  
Disease Diagnosis ◽  
Case Review ◽  
Support Tool ◽  
Rare Genetic Diseases ◽  
New Findings

Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed interpretation by comprehensively evaluating genetic variants for pathogenicity in the context of the growing knowledge of genetic disease. We assess the diagnostic performance of GEM, a new, AI-based, clinical decision support tool, compared with expert manual interpretation. We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole genome sequencing (WGS) at Rady Children's Hospital. We also performed a replication study in a separate cohort of 60 cases diagnosed at five additional academic medical centers. For comparison, we also analyzed these cases with commonly used variant prioritization tools (Phevor, Exomiser, and VAAST). Included in the comparisons were WGS and whole exome sequencing (WES) as trios, duos, and singletons. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted either manually or by automated clinical natural language processing (CNLP) from clinical notes. Finally, 14 previously unsolved cases were re-analyzed. GEM ranked >90% of causal genes among the top or second candidate, using manually curated or CNLP derived phenotypes, and prioritized a median of 3 genes for review per case. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top or second candidate irrespective of whether SV calls where provided or inferred ab initio by GEM when absent. Analysis of 14 previously unsolved cases provided novel findings in one, candidates ultimately not advanced in 3, and no new findings in 10, demonstrating the utility of GEM for reanalysis. GEM enables automated diagnostic interpretation of WES and WGS for all types of variants, including SVs, nominating a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing the cost and speeding case review.

scholarly journals RDmap: A Map for Exploring Rare Diseases

2020 ◽  
Author(s):  
Jian Yang ◽  
Cong Dong ◽  
Huilong Duan ◽  
Qiang Shu ◽  
Haomin Li
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Keyword Search ◽  
Genetic Diseases ◽  
Distance Matrix ◽  
Pathogenic Genes ◽  
Disease Information ◽  
Rare Genetic Diseases ◽  
Interactive Map ◽  
Location Function

Abstract Background: The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases make the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based on similarity will help clinicians and researchers understand and easily explore these diseases. Methods: By defining the quantitative distance among phenotypes and pathogenic genes based on corresponding ontology systems, the distance matrix of rare diseases included in Orphanet was calculated and mapped into Euclidean space. Enhanced by clustering classes and disease information, a rare disease map was developed based on ECharts. Results: The rare disease map called RDmap was published at http://rdmap.nbscn.org. The phenotype-based map comprises 3,287 rare diseases and the gene-based map comprises 3,789 rare genetic diseases and they were bridged by 1,718 overlapping diseases. RDmap works similar to the widely used Google map and supports zooming and panning. The phenotype similarity base disease location function performed better than traditional keyword search in an in-silico evaluation and 20 published cases of rare diseases also demonstrated that RDmap can be used by clinicians to improve diagnosis. Conclusion: RDmap is the first user-interactive map-style rare disease knowledgebase. It will help clinicians and researchers explore the increasing complicated rare genetic diseases.

scholarly journals Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Francisco M. De La Vega ◽  
Shimul Chowdhury ◽  
Barry Moore ◽  
Erwin Frise ◽  
Jeanette McCarthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Candidate Genes ◽  
Language Processing ◽  
Genetic Disease ◽  
Genetic Diseases ◽  
Disease Diagnosis ◽  
Case Review ◽  
Genome Interpretation ◽  
Rare Genetic Diseases ◽  
New Findings

Abstract Background Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. Methods We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. Results GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. Conclusions GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

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