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Record W4382344150 · doi:10.1002/ajmg.a.63247

Genomic analyses in Cornelia de Lange Syndrome and related diagnoses: Novel candidate genes, <scp>genotype–phenotype</scp> correlations and common mechanisms

2023· article· en· W4382344150 on OpenAlexaff
Maninder Kaur, Justin Blair, Batsal Devkota, Sierra Fortunato, Dinah Clark, Audrey Lawrence, Jiwoo Kim, Wonwook Do, Benjamin Semeo, Olivia Katz, Devanshi Mehta, Nobuko Yamamoto, Emma Schindler, Zayd Al Rawi, Nina Wallace, Jonathan J. Wilde, Jennifer McCallum, Jinglan Liu, Dongbin Xu, Marie Jackson, Stefan Rentas, Ahmad Abou Tayoun, Zhe Zhang, Omar Abdul‐Rahman, Bill Allen, Moris A. Angula, Kwame Anyane‐Yeboa, Jesús Argente, Pamela Arn, Linlea Armstrong, Lina Basel‐Salmon, Gareth Baynam, Lynne M. Bird, Daniel E. Bruegger, Gaik‐Siew Ch'ng, David Chitayat, Robin D. Clark, Gerald F. Cox, Usha Dave, Elfrede DeBaere, Michael Field, John M. Graham, Karen W. Gripp, Robert M. Greenstein, Randy Heidenreich, Jodi D. Hoffman, Robert J. Hopkin, Kenneth Lyons Jones, Marilyn C. Jones, Ariana Kariminejad, Jillene Kogan, Baiba Lāce, J. G. Leroy, Sally Ann Lynch, Marie McDonald, Kirsten Meagher, Nancy J. Mendelsohn, Ieva Mičule, John B. Moeschler, Sheela Nampoothiri, Kaoru Ohashi, Cynthia M. Powell, Subhadra Ramanathan, Salmo Raskin, Elizabeth Roeder, Marlène Rio, Alan F. Rope, Karan Sangha, Angela E. Scheuerle, Adele Schneider, Stavit A. Shalev, Victoria Mok Siu, Rosemarie Smith, Cathy A. Stevens, Tinatin Tkemaladze, John Toimie, Helga V. Toriello, Anne‐Marie W. Turner, Patricia G. Wheeler, Susan M. White, Terri L. Young, Kathleen M. Loomes, Mary Pipan, Ann T. Harrington, Elaine H. Zackai, Ramakrishnan Rajagopalan, Laura K. Conlin, Matthew A. Deardorff, Deborah McEldrew, Juan Pié, Feliciano J. Ramos, Antonio Musio, Antonie D. Kline, Kosuke Izumi, Sarah E. Raible, Ian D. Krantz

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsLondon Health Sciences CentreWestern UniversityUniversity of TorontoSickKids FoundationMount Sinai HospitalB.C. Women's Hospital & Health CentreUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthCornelia de Lange Syndrome Foundation
KeywordsCornelia de Lange SyndromeBiologyGeneticsPhenotypeCohesinGeneCandidate geneChromosome

Abstract

fetched live from OpenAlex

Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features. Pathogenic variants in genes encoding cohesin complex structural subunits and regulatory proteins (NIPBL, SMC1A, SMC3, HDAC8, and RAD21) are the major pathogenic contributors to CdLS. Heterozygous or hemizygous variants in the genes encoding these five proteins have been found to be contributory to CdLS, with variants in NIPBL accounting for the majority (>60%) of cases, and the only gene identified to date that results in the severe or classic form of CdLS when mutated. Pathogenic variants in cohesin genes other than NIPBL tend to result in a less severe phenotype. Causative variants in additional genes, such as ANKRD11, EP300, AFF4, TAF1, and BRD4, can cause a CdLS-like phenotype. The common role that these genes, and others, play as critical regulators of developmental transcriptional control has led to the conditions they cause being referred to as disorders of transcriptional regulation (or "DTRs"). Here, we report the results of a comprehensive molecular analysis in a cohort of 716 probands with typical and atypical CdLS in order to delineate the genetic contribution of causative variants in cohesin complex genes as well as novel candidate genes, genotype-phenotype correlations, and the utility of genome sequencing in understanding the mutational landscape in this population.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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

Citations41
Published2023
Admission routes1
Has abstractyes

Explore more

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