Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland

Wright, Caroline F.; Campbell, Patrick; Eberhardt, Ruth Y; Aitken, Stuart; Perrett, Daniel; Brent, Simon; Danecek, Petr; Gardner, Eugene J.; Chundru, V. Kartik; Lindsay, Sarah; Andrews, Katrina; Hampstead, Juliet E.; Kaplanis, Joanna; Samocha, Kaitlin E.; Middleton, Anna; Foreman, Julia; Hobson, Rachel J.; Parker, Michael; Martin, Hilary C.; FitzPatrick, David R.; Hurles, Matthew E.; Firth, Helen V.

doi:10.1056/nejmoa2209046

Cited by 100 publications

(53 citation statements)

References 39 publications

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“…Outcomes data were recorded on 4,237 diagnosed DDD probands (47% female) by 24 Regional Genetics Services across the UK and Republic of Ireland (range = 42-316 per centre, Figure 1 ). Diagnoses included both small single-gene variants and large multigenic structural variants, with inheritance patterns[4] including autosomal dominant (68% de novo , 7% inherited from an affected parent, 8% with unknown inheritance), autosomal recessive (11%), X-linked (5%), as well as multiple diagnoses with different inheritance classes (1%). The average time from recruitment to result was 3.8 years (range: 1.1-9.4 years), at which point probands were an average of 12 years old (range: 1.8-55 years) and parents were an average of 44 years old (range: 20-90 years; Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…We further sought to compare phenotypes and outcomes between probands of different ages diagnosed with the same condition. In our dataset, 37 genes had diagnostic variants in > 20 probands, together accounting for 1218 (29%) of diagnoses[4]. Of these, we focused on three well-established exemplar genes: ANKRD11 (KBG syndrome; n=79)[14], which has the largest number of DDD diagnoses; CTNNB1 (neurodevelopmental disorder with spastic diplegia and visual defects, NEDSDV; n=30)[15], in which there is a clinical imperative for ophthalmic surveillance; and NSD1 (Sotos syndrome; n=20)[16], in which the highest proportion of DDD probands (65%) had medical interventions following a diagnosis.…”

Section: Resultsmentioning

confidence: 99%

“…Despite widespread use of genomic testing in children with developmental disorders (DD), relatively little has been documented about the outcomes following a genetic diagnosis in this group of patients [1]. Steady advances in genomic technologies, including DNA microarray analysis and exome/genome sequencing, have resulted in the identification of a monogenic cause in around half of individuals affected with a presumed genetic DD [2][3][4]. The value of a diagnosis to the family has been well documented (https://www.undiagnosed.org.uk/supportinformation/what-does-getting-a-genetic-diagnosis-mean/), including genetic counselling, accessing patient support groups and reproductive planning [5].…”

Section: Introductionmentioning

confidence: 99%

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Large-scale evaluation of outcomes following a genetic diagnosis in children with severe developmental disorders

Copeland,

Low,

Wynn

et al. 2023

Preprint

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Objective: We sought to evaluate outcomes for clinical management following a genetic diagnosis from the Deciphering Developmental Disorders (DDD) Study. Design: Individuals in the DDD study who had a pathogenic/likely pathogenic genotype in the DECIPHER database were selected for inclusion (n=5010). Clinical notes from regional clinical genetics services notes were reviewed to assess pre-defined clinical outcomes relating to interventions, prenatal choices, and information provision. Results: Outcomes were recorded for 4237 diagnosed probands (85% of those eligible) from all 24 recruiting centres across the UK and Ireland. Additional diagnostic or screening tests were performed in 903 (21%) probands through referral to a range of different clinical specialties, and stopped or avoided in a further 26 (0.6%). Disease-specific treatment was started in 85 (2%) probands, including seizure-control medications and dietary supplements, and contra-indicated medications were stopped/avoided as no longer necessary in a further 20 (0.5%). The option of prenatal/preimplantation genetic testing was discussed with 1204 (28%) families, despite the relatively advanced age of the parents at the time of diagnosis. Importantly, condition-specific information or literature was given to 3214 (76%) families, and 880 (21%) were involved in family support groups. In the most common condition (KBG syndrome; 79 (2%) probands), clinical interventions only partially reflected the temporal development of phenotypes, highlighting the importance of consensus management guidelines and patient support groups. Conclusions: Our results underscore the importance of achieving a clinico-molecular diagnosis to ensure timely onward referral of patients, enabling appropriate care and anticipatory surveillance, and for accessing relevant patient support groups.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large-scale evaluation of outcomes following a genetic diagnosis in children with severe developmental disorders

Copeland,

Low,

Wynn

et al. 2023

Preprint

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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%

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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“…skeletal anomalies, axis and laterality defects and eye disorders). Postnatal evaluation can provide further information regarding subtle dysmorphology, neurodevelopmental delay and seizures to establish a phenotypegenotype correlation 11,12 . Such postnatal findings may be difficult or impossible to predict prospectively using real-time prenatal ultrasound, a process that is further hampered by the fact that fetal phenotypes may evolve with advancing gestation 13,14 .…”

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confidence: 99%