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The Genetic Landscapes of Autism Spectrum Disorders

2013· review· en· W2154511345 on OpenAlexfundno aff
Guillaume Huguet, Elodie Ey, Thomas Bourgeron

Bibliographic record

VenueAnnual Review of Genomics and Human Genetics · 2013
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheInstitut national de la recherche scientifique
KeywordsGeneticsBiologyGeneAutismAutism spectrum disorderCopy-number variationEpigeneticsGenetic heterogeneityPopulationNeuroscienceEvolutionary biologyGenomePhenotypePsychologyMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

The autism spectrum disorders (ASD) are characterized by impairments in social interaction and stereotyped behaviors. For the majority of individuals with ASD, the causes of the disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Chromosomal rearrangements as well as rare and de novo copy-number variants are present in ∼10-20% of individuals with ASD, compared with 1-2% in the general population and/or unaffected siblings. Rare and de novo coding-sequence mutations affecting neuronal genes have also been identified in ∼5-10% of individuals with ASD. Common variants such as single-nucleotide polymorphisms seem to contribute to ASD susceptibility, but, taken individually, their effects appear to be small. Despite a heterogeneous genetic landscape, the genes implicated thus far-which are involved in chromatin remodeling, metabolism, mRNA translation, and synaptic function-seem to converge in common pathways affecting neuronal and synaptic homeostasis. Animal models developed to study these genes should lead to a better understanding of the diversity of the genetic landscapes of ASD.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.331
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations463
Published2013
Admission routes1
Has abstractyes

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