2021
DOI: 10.1038/s41436-021-01297-5
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Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis

Abstract: Purpose Genome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease. Methods GS data from 114 probands with craniosynostosis and their relatives (314 samples), negat… Show more

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Cited by 17 publications
(22 citation statements)
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References 37 publications
(68 reference statements)
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“…Clearly, many new diagnoses remain to be found. A recent analysis of 100KGP data in the context of craniosynostosis found that expert-led review more than doubled diagnostic yields compared to the standard pipeline 3 . An important factor is that the “virtual panels “ applied to variant calls are outdated and do not include recently discovered disease genes.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Clearly, many new diagnoses remain to be found. A recent analysis of 100KGP data in the context of craniosynostosis found that expert-led review more than doubled diagnostic yields compared to the standard pipeline 3 . An important factor is that the “virtual panels “ applied to variant calls are outdated and do not include recently discovered disease genes.…”
Section: Discussionmentioning
confidence: 99%
“…Diagnostic yields in individuals with rare diseases remain below 50%, despite extensive investigations including whole-genome sequencing 2 . The accurate interpretation of genomic variants in existing sequencing data presents an important opportunity to narrow the diagnostic gap 3 . Splicing is the process by which introns are removed from a pre-mRNA primary transcript.…”
Section: Introductionmentioning
confidence: 99%
“…Clinical interpretation of genomic variants requires the standard classification guidelines and workflows, as well as considering the consistency between a variant and a disease phenotype by enough solid evidence. Research-based analysis performed by the Clinical Genetics Group, Oxford (CGG) identified additionally over one-fold P/LP variants than the GE/GMC pipeline for rare diseases, which demonstrated the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical genome sequencing (Hyder et al, 2021). Our study combined the CMM pipeline following the ACMG guideline and the RP as a supplement.…”
Section: Candidate Variants Identified By Different Pipelinesmentioning
confidence: 92%
“…Although I do not use AI or machine learning in my research, I regularly use bioinformatics approaches in my analysis of sequencing data and variant prioritization, examples of which can be found in our recent paper, published in Genetics in Medicine . 1 …”
Section: Main Textmentioning
confidence: 99%
“…Although I do not use AI or machine learning in my research, I regularly use bioinformatics approaches in my analysis of sequencing data and variant prioritization, examples of which can be found in our recent paper, published in Genetics in Medicine. 1 Which of the current trends in data science seem the most interesting to you? In your opinion, what are the most pressing questions for the data science community?…”
mentioning
confidence: 99%