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Record W3146523828 · doi:10.1002/ajmg.a.62124

Expanding the genotypic and phenotypic spectrum in a diverse cohort of 104 individuals with Wiedemann‐Steiner syndrome

2021· article· en· W3146523828 on OpenAlexaff
Sarah E. Sheppard, Ian M. Campbell, Margaret Harr, Nina B. Gold, Dong Li, Hans T. Björnsson, Julie S. Cohen, Jill A. Fahrner, Ali Fatemi, Jacqueline Harris, C. Nowak, Cathy A. Stevens, Katheryn Grand, Margaret Au, John M. Graham, Pedro A. Sanchez‐Lara, Miguel Del Campo, Marilyn C. Jones, Omar Abdul‐Rahman, Fowzan S. Alkuraya, Jennifer A. Bassetti, Katherine Bergstrom, Elizabeth Bhoj, Sarah Dugan, Julie Kaplan, Nada Derar, Karen W. Gripp, Natalie Hauser, A. Micheil Innes, Beth Keena, Neslida Kodra, Rebecca L. Miller, Beverly Nelson, Małgorzata J.M. Nowaczyk, Zuhair Rahbeeni, Shay Ben‐Shachar, Joseph T.C. Shieh, Anne Slavotinek, Andrew K. Sobering, Mary‐Alice Abbott, Dawn C. Allain, Louise Amlie‐Wolf, Ping Yee Billie Au, Emma Bedoukian, Geoffrey Beek, James S. Barry, Janet Berg, Jonathan A. Bernstein, Cheryl Cytrynbaum, Brian Hon‐Yin Chung, Sarah Donoghue, Naghmeh Dorrani, Alison Eaton, Josue Flores Daboub, Holly Dubbs, Carolyn A. Felix, Chin‐To Fong, Jasmine Lee Fong Fung, Balram Gangaram, Amy Goldstein, Rotem Greenberg, Thoa K. Ha, Joseph H. Hersh, Kosuke Izumi, Staci Kallish, Elijah Kravets, Pui–Yan Kwok, Rebekah Jobling, Amy E. Knight Johnson, Jessica D. Kushner, Bo Hoon Lee, Brooke Levin, Kristin Lindstrom, Kandamurugu Manickam, Rebecca Mardach, Elizabeth M. McCormick, D. Ross McLeod, Frank Mentch, Kelly Q. Minks, Colleen Muraresku, Stanley F. Nelson, Patrizia Porazzi, Pavel N. Pichurin, Nina Powell‐Hamilton, Zöe Powis, Alyssa Ritter, Caleb Rogers, Luis Rohena, Carey Ronspies, Audrey Schroeder, Zornitza Stark, Lois J. Starr, Joan M. Stoler, Pim Suwannarat, Milen Velinov, Rosanna Weksberg, Yael Wilnai, Neda Zadeh, Dina J. Zand, Marni J. Falk, Håkon Håkonarson, Elaine H. Zackai, Fabiola Quintero‐Rivera

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of AlbertaHospital for Sick ChildrenSickKids FoundationUniversity of TorontoMcMaster UniversityAlberta Children's HospitalUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institutes of HealthHartwell Foundation
KeywordsPhenotypeCohortGenotypeGeneticsBiologyDemographyMedicineGeneInternal medicineSociology

Abstract

fetched live from OpenAlex

Wiedemann-Steiner syndrome (WSS) is an autosomal dominant disorder caused by monoallelic variants in KMT2A and characterized by intellectual disability and hypertrichosis. We performed a retrospective, multicenter, observational study of 104 individuals with WSS from five continents to characterize the clinical and molecular spectrum of WSS in diverse populations, to identify physical features that may be more prevalent in White versus Black Indigenous People of Color individuals, to delineate genotype-phenotype correlations, to define developmental milestones, to describe the syndrome through adulthood, and to examine clinicians' differential diagnoses. Sixty-nine of the 82 variants (84%) observed in the study were not previously reported in the literature. Common clinical features identified in the cohort included: developmental delay or intellectual disability (97%), constipation (63.8%), failure to thrive (67.7%), feeding difficulties (66.3%), hypertrichosis cubiti (57%), short stature (57.8%), and vertebral anomalies (46.9%). The median ages at walking and first words were 20 months and 18 months, respectively. Hypotonia was associated with loss of function (LoF) variants, and seizures were associated with non-LoF variants. This study identifies genotype-phenotype correlations as well as race-facial feature associations in an ethnically diverse cohort, and accurately defines developmental trajectories, medical comorbidities, and long-term outcomes in individuals with WSS.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.269

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 head0.009
GPT teacher head0.262
Teacher spread0.252 · 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

Citations72
Published2021
Admission routes1
Has abstractyes

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