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Record W2610557320 · doi:10.1038/s41525-017-0019-2

The clinical impact of copy number variants in inherited bone marrow failure syndromes

2017· article· en· W2610557320 on OpenAlexafffundabout
Nicolas Waespe, Santhosh Dhanraj, Manju Wahala, Elena Tsangaris, Tom Enbar, Bozana Zlateska, Hongbing Li, Robert J. Klaassen, Conrad V. Fernandez, Geoff D.E. Cuvelier, John K. Wu, Yves Pastore, Mariana Silva, Jeffrey H. Lipton, Josée Brossard, Bruno Michon, Sharon Abish, MacGregor Steele, Roona Sinha, Mark Belletrutti, Vicky R. Breakey, Lawrence Jardine, Lisa Goodyear, Liat Kofler, Michaela Cada, Lillian Sung, Mary Shago, Stephen W. Scherer, Yigal Dror

Bibliographic record

Venuenpj Genomic Medicine · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsSickKids FoundationJaneway Children's Health and Rehabilitation CentreLondon Health Sciences CentreMcMaster UniversityUniversity of AlbertaCentre hospitalier universitaire de QuébecCentre Hospitalier Universitaire de SherbrookeCentre Hospitalier Universitaire Sainte-JustinePopulation Health Research InstituteChildren's Hospital of Eastern OntarioCancerCare ManitobaBC Children's HospitalRoyal University HospitalMontreal Children's HospitalUniversity of TorontoPrincess Margaret Cancer CentreKingston General HospitalUniversity of ManitobaAlberta Children's HospitalIzaak Walton Killam Health CentreHospital for Sick Children
FundersGarron Family Cancer CentreCanadian Institutes of Health ResearchChildhood Cancer CanadaC17 Council
KeywordsCopy-number variationBiologyBone marrowGeneticsBone marrow failureGenotypeMyelodysplastic syndromesComparative genomic hybridizationHaematopoiesisGeneGenomeImmunologyStem cell

Abstract

fetched live from OpenAlex

Abstract Inherited bone marrow failure syndromes comprise a genetically heterogeneous group of diseases with hematopoietic failure and a wide array of physical malformations. Copy number variants were reported in some inherited bone marrow failure syndromes. It is unclear what impact copy number variants play in patients evaluated for a suspected diagnosis of inherited bone marrow failure syndromes. Clinical and genetic data of 323 patients from the Canadian Inherited Marrow Failure Registry from 2001 to 2014, who had a documented genetic work-up, were analyzed. Cases with pathogenic copy number variants (at least 1 kilobasepairs) were compared to cases with other mutations. Genotype-phenotype correlations were performed to assess the impact of copy number variants. Pathogenic nucleotide-level mutations were found in 157 of 303 tested patients (51.8%). Genome-wide copy number variant analysis by single-nucleotide polymorphism arrays or comparative genomic hybridization arrays revealed pathogenic copy number variants in 11 of 67 patients tested (16.4%). In four of these patients, identification of copy number variant was crucial for establishing the correct diagnosis as their clinical presentation was ambiguous. Eight additional patients were identified to harbor pathogenic copy number variants by other methods. Of the 19 patients with pathogenic copy number variants, four had compound-heterozygosity of a copy number variant with a nucleotide-level mutation. Pathogenic copy number variants were associated with more extensive non-hematological organ system involvement ( p = 0.0006), developmental delay ( p = 0.006) and short stature ( p = 0.04) compared to nucleotide-level mutations. In conclusion, a significant proportion of patients with inherited bone marrow failure syndromes harbor pathogenic copy number variants which were associated with a more extensive non-hematological phenotype in this cohort. Patients with a phenotype suggestive of inherited bone marrow failure syndromes but without identification of pathogenic nucleotide-level mutations should undergo specific testing for copy number variants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.357
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2017
Admission routes3
Has abstractyes

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