Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory

Meng, Linyan; Attali, Ruben; Talmy, Tomer; Regev, Yakir; Mizrahi, Niv; Smirin‐Yosef, Pola; Vossaert, Liesbeth; Taborda, Christian; Santana, Michael Nadson Santos; Machol, Ido; Xiao, Rui; Dai, Hongzheng; Eng, Christine M.; Xia, Fan; Tzur, Shay

doi:10.1016/j.gim.2023.100830

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…As it stands, significant bioinformatic challenges persist in genomic medicine. The increase in sequencing capacity has accelerated its diagnostic power, but there is often little or inconclusive supporting functional evidence for the ever-increasing number of novel suspected pathogenic variants 61 . Indeed, it is also likely that the incremental yield of WGS over QF-PCR/CMA is similar to that of ES, as current bioinformatic tools for interpretation of non-coding areas of the genome and the implications of complex structural variants are not advanced enough to draw causative genotype-phenotype correlations.…”

Section: Discussionmentioning

confidence: 99%

Incremental yield of whole‐genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta‐analysis

Shreeve,

Sproule,

Choy

et al. 2024

Ultrasound in Obstet & Gyne

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ObjectivePrimarily to determine the incremental yield of Whole Genome Sequencing (WGS) over Chromosome Microarray Assessment (CMA) and/or Exome Sequencing (ES) in fetuses and infants with a congenital anomaly that was or could have been detectable on ultrasound prenatally. Secondly, to evaluate the turnaround time (TAT) and quantity of DNA required for testing using these pathways.MethodsOVID MEDLINE(R), EMBASE, Medline (Web of Science), Cochrane Library, and ClinicalTrials.gov databases were searched electronically (January 2010 to December 2022). Inclusion criteria were cohort studies including fetuses/infants with ≥n=3 cases of: (i) one or more congenital anomalies; (ii) an anomaly which was or would have been detectable on prenatal ultrasound and; (iii) exclusion of aneuploidy and pathogenic Copy Number Variants >50kb. Pooled incremental yield was determined using a random‐effects model and heterogeneity was assessed statistically using Higgins’ I2. Sub‐analyses were performed based on pre‐ or postnatal presentation, multi‐system anomalies and classification based upon NHS England prenatal (R21) ES inclusion criteria. PROSPERO 2022 CRD42022380483ResultsEighteen studies incorporating n=1284 cases were included, of which n=8 (44.4%) incorporating n=754 (58.7%) cases were prenatal cohorts and the remainder representing postmortem (postnatal evaluation), neonatal or infants with congenital structural anomalies. The incremental yield of WGS over QF‐PCR/CMA was 26% (95% CI 18‐36%, I2=86%), 16% (9‐24%, I2=85%) and 39% (95%CI 27‐51%, I2=53%) for all, prenatal and postnatal cases. The incremental yields were optimal where sequencing was performed in line with NHS England prenatal ES criteria; 32% (22%‐42%, I2=70%). The incremental yield of Variants of Uncertain Significance (VUS) was 18% (95% CI 7‐33%, I2=74%). The yield of WGS over ES was non‐significant at 1% (95% CI 0%‐4%, I2=47%). The pooled median TAT for WGS was 18 days (range: 1‐912 days) and the quantity of DNA required for WGS versus CMA and ES was 100ng (±0) and 350ng (±50) p=0.03 respectively.ConclusionWhilst WGS investigation for congenital anomaly holds great promise, for the future, its’ incremental yield over ES is yet to be demonstrated. However, the laboratory pathway for WGS (compared to sequential QF‐PCR/CMA/ES) requires less DNA with a potentially faster TAT. There was a relatively high rate of VUS.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Incremental yield of whole‐genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta‐analysis

Shreeve,

Sproule,

Choy

et al. 2024

Ultrasound in Obstet & Gyne

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“…These deep learning networks predict the pathogenicity of genetic variants from curated datasets and various genomic features, including experimental, population and clinical data, thereby assisting in the interpretation of genetic testing results. Mostly, an automated, streamlined process identifies a concise list of candidate genes for comprehensive evaluation, and reporting ( 64 , 65 ). The automation of genetic disease diagnosis potentially simplifies and expedites the interpretation of the vast numbers of genetic variants, leading to an increased diagnostic yield while reducing turnaround time and cost.…”

Section: Comprehensive Approaches For Analysis Of Genomic Datamentioning

confidence: 99%

Current genetic diagnostics in inborn errors of immunity

von Hardenberg,

Klefenz,

Steinemann

et al. 2024

Front. Pediatr.

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New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

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“…A proliferation of such examples of applied NLP in genomic medicine should be expected in the coming years. et al, 2013;Meng et al, 2023;O'Brien et al, 2022;Wright et al, 2023). These platforms have become faster and more accurate by incorporating improved genotype-phenotype annotations from the Human Phenotype Ontology, the Monarch Initiative, DisGeNET, and other research efforts (Köhler et al, 2021;Pilehvar et al, 2022;Piñero et al, 2020;Robinson et al, 2008Robinson et al, , 2014Shefchek et al, 2020); by refining the heuristics used to analyze WES (by mimicking the analysis processes used by experienced clinical laboratory geneticists); and by deploying NLP to mine published literature for phenotype and DNA variant information that could be relevant to identifying the molecular cause of an individual's clinical condition.…”

Section: Mining Published Literature or Electronic Health Recordsmentioning

confidence: 99%

“…Roughly a dozen years ago, clinical analysis of WES relied primarily on manual analysis due to a paucity of powerful software tools that could pull together diverse evidence types including genotype‐disease annotations, relevant published medical literature, genome sequence annotation resources, and predictions from in silico modeling algorithms. Various sophisticated proprietary software platforms for analyzing WES have since been developed, many of which incorporate AI (e.g., Invitae's Moon™, Fabric GEM™, Illumina's Emedgene™, FindZebra) (De La Vega et al, 2021; Dragusin et al, 2013; Meng et al, 2023; O'Brien et al, 2022; Wright et al, 2023). These platforms have become faster and more accurate by incorporating improved genotype–phenotype annotations from the Human Phenotype Ontology, the Monarch Initiative, DisGeNET, and other research efforts (Köhler et al, 2021; Pilehvar et al, 2022; Piñero et al, 2020; Robinson et al, 2008, 2014; Shefchek et al, 2020); by refining the heuristics used to analyze WES (by mimicking the analysis processes used by experienced clinical laboratory geneticists); and by deploying NLP to mine published literature for phenotype and DNA variant information that could be relevant to identifying the molecular cause of an individual's clinical condition.…”

Section: Correlating Genotypes and Phenotypes For Clinical Diagnosesmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

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The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre‐test and post‐test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

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Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory

Cited by 10 publications

References 15 publications

Incremental yield of whole‐genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta‐analysis

Incremental yield of whole‐genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta‐analysis

Current genetic diagnostics in inborn errors of immunity

Applications of artificial intelligence in clinical laboratory genomics

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