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Record W2538540958 · doi:10.1186/s13073-016-0359-z

Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics

2016· article· en· W2538540958 on OpenAlexaff
Maria Nicla Loviglio, Christine R. Beck, Janson J. White, Marion Leleu, Tamar Harel, Nicolas Guex, Anne Niknejad, Weimin Bi, Edward S. Chen, Isaac Crespo, Jiong Yan, Wu‐Lin Charng, Shen Gu, Ping Fang, Zeynep Coban‐Akdemir, Chad A. Shaw, Shalini N. Jhangiani, Donna M. Muzny, Richard A. Gibbs, Jacques Rougemont, Ioannis Xénarios, James R. Lupski, Alexandre Reymond

Bibliographic record

VenueGenome Medicine · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBaylor-Hopkins Center for Mendelian GenomicsSimons Foundation Autism Research InitiativeJohns Hopkins UniversityNational Science FoundationDamon Runyon Cancer Research FoundationHoward Hughes Medical InstituteCancer Prevention and Research Institute of TexasSimons Foundation
KeywordsExome sequencingIdentification (biology)GenomicsComputational biologyHuman geneticsExomeDiseaseTranscriptomeMedicineSystems biologyDNA sequencingBioinformaticsBiologyGeneticsGenomeGeneInternal medicineMutation

Abstract

fetched live from OpenAlex

Smith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from haploinsufficiency of RAI1. It is characterized by distinctive facial features, brachydactyly, sleep disturbances, and stereotypic behaviors. We investigated a cohort of 15 individuals with a clinical suspicion of SMS who showed neither deletion in the SMS critical region nor damaging variants in RAI1 using whole exome sequencing. A combination of network analysis (co-expression and biomedical text mining), transcriptomics, and circularized chromatin conformation capture (4C-seq) was applied to verify whether modified genes are part of the same disease network as known SMS-causing genes. Potentially deleterious variants were identified in nine of these individuals using whole-exome sequencing. Eight of these changes affect KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C, and POGZ, known to be associated with Kabuki syndrome 1, Mowat-Wilson syndrome, cardiofaciocutaneous syndrome, glycine encephalopathy, mental retardation and microcephaly with pontine and cerebellar hypoplasia, X-linked mental retardation 13, X-linked mental retardation Claes-Jensen type, and White-Sutton syndrome, respectively. The ninth individual carries a de novo variant in JAKMIP1, a regulator of neuronal translation that was recently found deleted in a patient with autism spectrum disorder. Analyses of co-expression and biomedical text mining suggest that these pathologies and SMS are part of the same disease network. Further support for this hypothesis was obtained from transcriptome profiling that showed that the expression levels of both Zeb2 and Map2k2 are perturbed in Rai1 –/– mice. As an orthogonal approach to potentially contributory disease gene variants, we used chromatin conformation capture to reveal chromatin contacts between RAI1 and the loci flanking ZEB2 and GLDC, as well as between RAI1 and human orthologs of the genes that show perturbed expression in our Rai1 –/– mouse model. These holistic studies of RAI1 and its interactions allow insights into SMS and other disorders associated with intellectual disability and behavioral abnormalities. Our findings support a pan-genomic approach to the molecular diagnosis of a distinctive disorder.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.319

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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 designBench or experimental
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

Citations31
Published2016
Admission routes1
Has abstractyes

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