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Record W4410745157 · doi:10.1002/ajmg.a.64119

Truncating Variants in <scp><i>RREB1</i></scp> Cause a Novel <scp>RASopathy</scp> Syndrome of Congenital Heart Disease, Genitourinary Malformations, and Developmental Delay

2025· article· en· W4410745157 on OpenAlexaff
Alanna Strong, Caoimhe McKenna, Karen Stals, Antonio Vitobello, Mathilde Renaud, Claudine Rieubland, Michel Guipponi, Christophe Philippe, Paul B. Vrana, Alisa Gaskell, A. Micheil Innes, Alyssa L. Rippert, Rebecca C. Ahrens‐Nicklas, Elizabeth Bhoj, Kiersten N Keller, Bimal P. Chaudhari

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Institutes of Health
KeywordsHaploinsufficiencyPhenotypeGenitourinary systemFLNACostello syndromeNoonan syndromeDiseaseBiologyHeart diseaseGeneticsCancer researchMutationMedicineInternal medicineGene

Abstract

fetched live from OpenAlex

The interstitial 6p microdeletion syndrome is characterized by dysmorphic facies and structural heart, kidney, brain, and musculoskeletal differences. RREB1 haploinsufficiency and consequent abnormal RAS-MAPK pathway signaling have been proposed as a driver of the disease phenotype; however, apart from a single case report, the phenotype of intragenic RREB1 variants is unknown. Here we present a cohort of 6 individuals with truncating RREB1 variants. Phenotypes include mild dysmorphisms, congenital heart disease, genitourinary malformations, dental anomalies, and developmental delay. Our data support RREB1 as a currently under-recognized cause of a RASopathy phenotype with features that overlap with Noonan, Costello, and Cardiofaciocutaneous syndromes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2025
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part ASame topicProtein Tyrosine PhosphatasesFrench-language works237,207