Reanalysis of clinical exome identifies the second variant in two individuals with recessive disorders

Li, Qifei; Agrawal, Rohan; Schmitz-Abe, Klaus; Genetti, Casie A.; Fernandes, Melissa; Fryou, Noah L.; Madden, Jill A.; Brownstein, Catherine A.; Smith, Edward C.; Rajabi, Farrah; Beggs, Alan H.; Agrawal, Pankaj B.

doi:10.1038/s41431-023-01291-2

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…The availability of better bioinformatic tools, accumulation of genetic information over time, and periodic reanalysis of genomic data can improve the diagnostic rates of clinical exome sequencing (CES) or genome sequencing over time (Li et al, 2023; Schmitz‐Abe et al, 2019). Oftentimes, variants in novel genes or variants in known genes with the potential for phenotypic expansion also emerge after initial clinical sequencing or after reanalysis of the sequenced data.…”

Section: Introductionmentioning

confidence: 99%

High number of candidate gene variants are identified as disease‐causing in a period of 4 years

Hills,

Li,

Madden

et al. 2023

American J of Med Genetics Pt A

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Advances in bioinformatic tools paired with the ongoing accumulation of genetic knowledge and periodic reanalysis of genomic sequencing data have led to an improvement in genetic diagnostic rates. Candidate gene variants (CGVs) identified during sequencing or on reanalysis but not yet implicated in human disease or associated with a phenotypically distinct condition are often not revisited, leading to missed diagnostic opportunities. Here, we revisited 33 such CGVs from our previously published study and determined that 16 of them are indeed disease‐causing (novel or phenotype expansion) since their identification. These results emphasize the need to focus on previously identified CGVs during sequencing or reanalysis and the importance of sharing that information with researchers around the world, including relevant functional analysis to establish disease causality.

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Section: Introductionmentioning

confidence: 99%

High number of candidate gene variants are identified as disease‐causing in a period of 4 years

Hills,

Li,

Madden

et al. 2023

American J of Med Genetics Pt A

Self Cite

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“…What are the implications for patients who were sequenced in 2016? Evidence such as this has led to the widely held belief that since variant annotations change over time, there is a need for periodic variant reanalysis 12‐14 …”

Section: Introductionmentioning

confidence: 99%

“…Evidence such as this has led to the widely held belief that since variant annotations change over time, there is a need for periodic variant reanalysis. 12 , 13 , 14 …”

Section: Introductionmentioning

confidence: 99%

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Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR‐formatted genomic variants

Dolin,

Heale,

Gupta

et al. 2023

Learning Health Systems

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IntroductionVariant annotation is a critical component in next‐generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision‐making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out‐of‐date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up‐to‐date annotations to clinicians.MethodsThe work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR‐encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification.ResultsPrimary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed.ConclusionsWe found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real‐time decision support, based on current knowledge, to clinicians at the point of care.

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“…On the laboratory side of things, genomic reanalysis and DNA methylation studies emerged as key developments. Li and colleagues report that reanalysis of exome data for a the missing second variant in recessive genes can aid diagnosis [33]. Reanalysis of exome data 5-years after initial reporting resulted in an 18% diagnostic uplift (Bartolomaeus et al [34]).…”

mentioning

confidence: 99%

2023 in the European Journal of Human Genetics

McNeill

2024

Eur J Hum Genet

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Reanalysis of clinical exome identifies the second variant in two individuals with recessive disorders

Cited by 4 publications

References 15 publications

High number of candidate gene variants are identified as disease‐causing in a period of 4 years

High number of candidate gene variants are identified as disease‐causing in a period of 4 years

Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR‐formatted genomic variants

2023 in the European Journal of Human Genetics

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