Recommendations from the IRDiRC Working Group on methodologies to assess the impact of diagnoses and therapies on rare disease patients

Zanello, Galliano; Chan, Chun‐Hung; Pearce, David A.

doi:10.1186/s13023-022-02337-2

Cited by 23 publications

(26 citation statements)

References 12 publications

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“…Using a mother-child cohort that links children to mothers’ genetic carrier screening status, we have identified that PheIndex Scores are lower at one or two years of follow-up in children whose mothers received CS-L relative to CS-S. We believe that our PheIndex algorithm will address an unmet need to identify children with rare genetic disorders and potentially help overcome well-known obstacles such as underdiagnosis and delayed diagnosis. 20…”

Section: Discussionmentioning

confidence: 99%

An algorithm to identify patients with rare genetic disorders and its real-world data application

Webb

Lau²,

Tsevdos

et al. 2023

Preprint

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Objectives Develop a digital phenotyping algorithm (PheIndex) using electronic medical records (EMR) data to identify children aged 0-3 who have been diagnosed with genetic disorders or present with illness with an increased risk for genetic disorders from a mother-child cohort. Methods We established 13 criteria for the algorithm where two metrics — a quantified score and a classification — were derived. The criteria and the classification were validated by chart review from a pediatrician and clinical geneticist. To demonstrate the utility of our algorithm in real-world evidence applications, we examined the association between size of carrier screening panel (small/≤4 genes [CS-S] vs large/≥100genes [CS-L]) undertaken by mothers prior to delivery, and children classified as presenting with illness with an increased risk for genetic disorders by our algorithm. Results The PheIndex algorithm identified 1,088 such children out of 93,154 live births and achieved 90% sensitivity, 97% specificity, and 94% accuracy by chart review. We found that children whose mothers received CS-L were less likely to be classified as presenting with illness with an increased risk for genetic disorders and a decreased need to have multiple specialist visits and multiple ER visits, compared to children whose mothers received CS-S. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, and has the potential to improve healthcare delivery by alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist or other specialists.

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Section: Discussionmentioning

confidence: 99%

An algorithm to identify patients with rare genetic disorders and its real-world data application

Webb

Lau²,

Tsevdos

et al. 2023

Preprint

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“…The International Rare Disease Research Consortium (IRDiRC) roadmap 2017-2027 challenged the community with three goals, of which Goal 1: All patients coming to medical attention with a suspected rare disease will be diagnosed within one year if their disorder is known in the medical literature; all currently undiagnosable individuals will enter a globally coordinated diagnostic and research pipeline 3 . These "currently undiagnosable individuals" are de ned in this project as those having undergone an inconclusive WES.…”

Section: Discussionmentioning

confidence: 99%

“…Data from January 2021 show that 4,480 out of 6,171 RD 2 are genetic RD (72,5%), and 1,588 of them (35%) do not yet have a clearly identi ed causative gene. 3 Rare diseases are characterised by the di culty in obtaining a correct and timely diagnosis, because of their rarity, scarcity of patients and inequalities in access to expertise. 4 During their diagnostic journey, patients can receive a clinical diagnosis 5 (i.e.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report

Lebreton

Matalonga

Hongnat

et al. 2023

Preprint

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Rare diseases (RD) have a prevalence of not more than 1/2000 in the European population, and are characterised by the difficulty of obtaining a correct and timely diagnosis. According to Orphanet, 72,5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)’s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases amongst them and with known RD in Orphanet. A 3-step programmatic cascade of phenotypic similarity calculations using The Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) was developed; genomics data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplar cases, discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity algorithms, formulating diagnostic hypotheses that were validated in 42.1% of them and need further explorations in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort in Solve-RD.

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“…One of the vexing challenges in rare diseases is that we are only halfway towards the declared goal of granting every patient with a rare disease their right to an accurate molecular diagnosis within a year of presentation 23,24 . The fact that the diagnostic odyssey continues unabated even after whole-genome sequencing suggests that new approaches need to be implemented and the use of LRS represents one such approach.…”

Section: Discussionmentioning

confidence: 99%

NanoRanger enables rapid single base-pair resolution of genomic disorders

Zhang,

Bi,

Nadeef

et al. 2023

Preprint

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Rare diseases affect around 350 million individuals globally, yet at least half of those with suspected Mendelian disorders remain without a precise molecular diagnosis despite advanced genetic testing using short read sequencing (SRS). Long-read sequencing (LRS) holds a promise in addressing this diagnostic gap although its clinical application is hampered by its complicated workflow, demanding sample requirements, and exorbitant cost. Genomic disorders represent an opportunity to demonstrate the unique capability of LRS. This study addresses the challenges of identifying missing disease-causing breakpoints in complex genomic disorders by employing multiple LRS strategies, including a novel strategy named nanopore-based rapid acquisition of neighboring genomic regions (NanoRanger). NanoRanger requires neither detailed prior genetic mapping nor large amounts of ultrahigh-molecular-weight DNA, and it stands out for its ease of use and ultra-rapid acquisition of large genomic regions of interest with deep coverage. We describe a cohort of 13 families, each harboring a different homozygous disease-causing genomic rearrangement that defied breakpoint determination by SRS and Optical Genome Mapping (OGM). In each case, NanoRanger identified the breakpoints with a single base-pair resolution. This has enabled the accurate determination of the carrier status of unaffected family members as well as the founder nature of these genomic lesions and their frequency in the local population. It has also enabled an unprecedented analysis of the DNA motifs to discern the mechanism that predisposed to these recessive rearrangements. Our data suggest that NanoRanger can greatly accelerate the clinical adoption of LRS and expand its access for the benefit of patients with rare diseases.

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Recommendations from the IRDiRC Working Group on methodologies to assess the impact of diagnoses and therapies on rare disease patients

Cited by 23 publications

References 12 publications

An algorithm to identify patients with rare genetic disorders and its real-world data application

An algorithm to identify patients with rare genetic disorders and its real-world data application

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report

NanoRanger enables rapid single base-pair resolution of genomic disorders

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