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 A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes

2021 ◽  
Author(s):  
Chenjie Zeng ◽  
Lisa A Bastarache ◽  
Ran Tao ◽  
Eric Venner ◽  
Scott Hebbring ◽  
...  
Keyword(s):  
Hereditary Cancer ◽  
Meta Analysis ◽  
Genetic Disorders ◽  
Genomic Medicine ◽  
Risk Scores ◽  
Cancer Genes ◽  
Pathogenic Variants ◽  
Emerge Network ◽  
Uncertain Significance ◽  
Potential Pathogenicity

Knowledge of the clinical spectrum of rare genetic disorders helps in disease management and variant pathogenicity interpretation. Leveraging electronic health record (EHR)-linked genetic testing data from the eMERGE network, we determined the associations between a set of 23 hereditary cancer genes and 3017 phenotypes in 23544 individuals. This phenome-wide association study replicated 45% (184/406) of known gene-phenotype associations (P = 5.1 ×10-125). Meta-analysis with an independent EHR-derived cohort of 3242 patients confirmed 14 novel associations with phenotypes in the neoplastic, genitourinary, digestive, congenital, metabolic, mental and neurologic categories. Phenotype risk scores (PheRS) based on weighted aggregations of EHR phenotypes accurately predicted variant pathogenicity for at least 50% of pathogenic variants for 8/23 genes. We generated a catalog of PheRS for 7800 variants, including 5217 variants of uncertain significance, to provide empirical evidence of potential pathogenicity. This study highlights the potential of EHR data in genomic medicine.

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.

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 Whole exome sequencing uncovered highly penetrant recessive mutations for a spectrum of rare genetic pediatric diseases in Bangladesh

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hosneara Akter ◽  
Mohammad Shahnoor Hossain ◽  
Nushrat Jahan Dity ◽  
Md. Atikur Rahaman ◽  
K. M. Furkan Uddin ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Genetic Diseases ◽  
Leigh Syndrome ◽  
Genetic Profile ◽  
Compound Heterozygous ◽  
Pathogenic Variants ◽  
Whole Exome ◽  
Rare Genetic Diseases ◽  
Compound Heterozygous Variants

AbstractCollectively, rare genetic diseases affect a significant number of individuals worldwide. In this study, we have conducted whole-exome sequencing (WES) and identified underlying pathogenic or likely pathogenic variants in five children with rare genetic diseases. We present evidence for disease-causing autosomal recessive variants in a range of disease-associated genes such as DHH-associated 46,XY gonadal dysgenesis (GD) or 46,XY sex reversal 7, GNPTAB-associated mucolipidosis II alpha/beta (ML II), BBS1-associated Bardet–Biedl Syndrome (BBS), SURF1-associated Leigh Syndrome (LS) and AP4B1-associated spastic paraplegia-47 (SPG47) in unrelated affected members from Bangladesh. Our analysis pipeline detected three homozygous mutations, including a novel c. 863 G > C (p.Pro288Arg) variant in DHH, and two compound heterozygous variants, including two novel variants: c.2972dupT (p.Met991Ilefs*) in GNPTAB and c.229 G > C (p.Gly77Arg) in SURF1. All mutations were validated by Sanger sequencing. Collectively, this study adds to the genetic heterogeneity of rare genetic diseases and is the first report elucidating the genetic profile of (consanguineous and nonconsanguineous) rare genetic diseases in the Bangladesh population.

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.

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.

scholarly journals Genetic burden linked to founder effects in Saguenay-Lac-Saint-Jean illustrates the importance of genetic screening test availability

2021 ◽  
pp. jmedgenet-2021-107809
Author(s):  
Mbarka Bchetnia ◽  
Luigi Bouchard ◽  
Jean Mathieu ◽  
Philippe Campeau ◽  
Charles Morin ◽  
...  
Keyword(s):  
Genetic Diseases ◽  
Founder Mutations ◽  
Rare Disorders ◽  
Population Database ◽  
Inherited Disorders ◽  
Sequencing Technologies ◽  
Pathogenic Variants ◽  
Rare Genetic Diseases ◽  
Causal Genes ◽  
Region Data

The Saguenay–Lac-Saint-Jean (SLSJ) region located in the province of Quebec was settled in the 19th century by pioneers issued from successive migration waves starting in France in the 17th century and continuing within Quebec until the beginning of the 20th century. The genetic structure of the SLSJ population is considered to be the product a triple founder effect and is characterised by a higher prevalence of some rare genetic diseases. Several studies were performed to elucidate the historical, demographic and genetic background of current SLSJ inhabitants to assess the origins of these rare disorders and their distribution in the population. Thanks to the development of new sequencing technologies, the genes and the variants responsible for the most prevalent conditions were identified. Combined with other resources such as the BALSAC population database, identifying the causal genes and the pathogenic variants allowed to assess the impacts of some of these founder mutations on the population health and to design precision medicine public health strategies based on carrier testing. Furthermore, it stimulated the establishment of many public programmes.We report here a review and an update of a subset of inherited disorders and founder mutations in the SLSJ region. Data were collected from published scientific sources. This work expands the knowledge about the current frequencies of these rare disorders, the frequencies of other rare genetic diseases in this population, the relevance of the carrier tests offered to the population, as well as the current available treatments and research about future therapeutic avenues for these inherited disorders.

scholarly journals VarSight: prioritizing clinically reported variants with binary classification algorithms

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
James M. Holt ◽  
◽  
Brandon Wilk ◽  
Camille L. Birch ◽  
Donna M. Brown ◽  
...  
Keyword(s):  
Rare Disease ◽  
Binary Classification ◽  
Genomic Medicine ◽  
Disease Patient ◽  
Classification Algorithms ◽  
Variant Prioritization ◽  
Single Measure ◽  
Pathogenic Variants ◽  
Manual Curation ◽  
Rare Disease Patient

Abstract Background When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient’s phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. Methods We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. Results We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. Conclusions We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

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