Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Bullich, Gemma; Matalonga, Leslie; Pujadas, Montserrat; Papakonstantinou, Anastasios; Piscia, Davide; Tonda, Raúl; Artuch, Rafael; Gallano, P.; Garrabou, Glòria; González, Juan R.; Grinberg, Daniel; Guitart, Míriam; Laurie, Steven S.; Lázaro, Conxi; Luengo, Cristina; Martí, Ramón; Milá, Montserrat; Ovelleiro, David; Parra, Genı́s; Pujol, Aurora; Tizzano, Eduardo F.; Macaya, Alfons; Palau, Francesc; Ribes, Antònia; Pérez-Jurado, Luís A.; Beltrán, Sergi

doi:10.1016/j.jmoldx.2022.02.003

Cited by 12 publications

(4 citation statements)

References 80 publications

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“…All patients underwent initial WES analysis and were analysed using ClinPrior. At first, the diagnostic rate counting only pathogenic and likely pathogenic variants was 53/135 (39%), which increased to 60/135 (44.4%) after subsequent reanalysis at 12 and 24 months, thus confirming the previously reported importance of case reanalysis [33,34]. The reanalysis included novel interactomes, novel disease associations and an improved variant calling procedure.…”

Section: Diagnostic Yield Of Wes and Wgs In The Hsp/ca Real-world Dis...supporting

confidence: 76%

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Schlüter,

Vélez-Santamaría,

Verdura

et al. 2023

Genome Med

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Background Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts. Methods We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient’s standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA). Results ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes. Conclusions ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses.

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Section: Diagnostic Yield Of Wes and Wgs In The Hsp/ca Real-world Dis...supporting

confidence: 76%

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Schlüter,

Vélez-Santamaría,

Verdura

et al. 2023

Genome Med

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“…Most articles describe an increase in the expected diagnostic yield as the main reason to consider implementation of systematic reanalysis. Almost all the studies that conducted reanalysis reported an increase in diagnostic yield [ 4 , 11 , 17 , 20 , 21 , 24 – 28 , 30 – 51 ], but the increase varied widely between the studies. One possible explanation for this variation is differences in the analytical or technical approaches used, which make it difficult to compare studies.…”

Section: Resultsmentioning

confidence: 99%

Systematic reanalysis of genomic data by diagnostic laboratories: a scoping review of ethical, economic, legal and (psycho)social implications

van der Geest,

Maeckelberghe,

van Gijn

et al. 2024

Eur J Hum Genet

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With the introduction of Next Generation Sequencing (NGS) techniques increasing numbers of disease-associated variants are being identified. This ongoing progress might lead to diagnoses in formerly undiagnosed patients and novel insights in already solved cases. Therefore, many studies suggest introducing systematic reanalysis of NGS data in routine diagnostics. Introduction will, however, also have ethical, economic, legal and (psycho)social (ELSI) implications that Genetic Health Professionals (GHPs) from laboratories should consider before possible implementation of systematic reanalysis. To get a first impression we performed a scoping literature review. Our findings show that for the vast majority of included articles ELSI aspects were not mentioned as such. However, often these issues were raised implicitly. In total, we identified nine ELSI aspects, such as (perceived) professional responsibilities, implications for consent and cost-effectiveness. The identified ELSI aspects brought forward necessary trade-offs for GHPs to consciously take into account when considering responsible implementation of systematic reanalysis of NGS data in routine diagnostics, balancing the various strains on their laboratories and personnel while creating optimal results for new and former patients. Some important aspects are not well explored yet. For example, our study shows GHPs see the values of systematic reanalysis but also experience barriers, often mentioned as being practical or financial only, but in fact also being ethical or psychosocial. Engagement of these GHPs in further research on ELSI aspects is important for sustainable implementation.

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“…Recently, a largescale reanalysis effort of exomes and genomes from undiagnosed disease families has been conducted within the Solve-RD consortium; this study illustrates that updated knowledge and improved variant identification and interpretation substantially increase diagnostic yield (S. Laurie, W. Steyaert, E. de Boer et al, Nat Med in revision). Other re-analysis efforts have resulted in similar increases in diagnostic yield (Bullich et al, 2022;Liu et al, 2019;. Despite this, most tested RD patients remain without a genetic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing

Steyaert,

Sagath,

Demidov

et al. 2024

Preprint

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Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilized 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without a clear molecular diagnosis. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including a MCF2/FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

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Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Cited by 12 publications

References 80 publications

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Systematic reanalysis of genomic data by diagnostic laboratories: a scoping review of ethical, economic, legal and (psycho)social implications

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing

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