MétaCan
Menu
Back to cohort
Record W2128161895 · doi:10.1186/2040-2392-3-9

Common genetic variants, acting additively, are a major source of risk for autism

2012· article· en· W2128161895 on OpenAlexafffund
Lambertus Klei, Stephan Sanders, Michael T. Murtha, Vanessa Hus, Jennifer K. Lowe, A. Jeremy Willsey, Daniel Moreno‐De‐Luca, Timothy W. Yu, Éric Fombonne, Daniel H. Geschwind, Dorothy E. Grice, David H. Ledbetter, Catherine Lord, Shrikant Mane, Donna M. Martin, Eric M. Morrow, Christopher A. Walsh, Pauline Chaste, James S. Sutcliffe, Matthew W. State, Edwin H. Cook, Kathryn Roeder, Bernie Devlin

Bibliographic record

VenueMolecular Autism · 2012
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersNational Institute of Neurological Disorders and StrokeNational Institute on AgingOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchFondation de FranceHospital for Sick ChildrenMinistero della SaluteFondation pour la Recherche MédicaleSimons Foundation Autism Research InitiativeJohns Hopkins UniversityAutism SpeaksSick Kids FoundationFondation OrangeNational Institutes of HealthFondation FondaMentalNODAI Genome Research Center, Tokyo University of AgricultureWellcome TrustKoninklijke Nederlandse Akademie van WetenschappenHussman FoundationUniversity of TorontoYale UniversityInstitut National de la Santé et de la Recherche MédicaleSimons FoundationDeutsche ForschungsgemeinschaftOntario Innovation Trust
KeywordsHeritabilityMissing heritability problemAutismGenetic variationGeneticsMultiplexAllelePsychologyBiologyDevelopmental psychologyGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

BACKGROUND: Autism spectrum disorders (ASD) are early onset neurodevelopmental syndromes typified by impairments in reciprocal social interaction and communication, accompanied by restricted and repetitive behaviors. While rare and especially de novo genetic variation are known to affect liability, whether common genetic polymorphism plays a substantial role is an open question and the relative contribution of genes and environment is contentious. It is probable that the relative contributions of rare and common variation, as well as environment, differs between ASD families having only a single affected individual (simplex) versus multiplex families who have two or more affected individuals. METHODS: By using quantitative genetics techniques and the contrast of ASD subjects to controls, we estimate what portion of liability can be explained by additive genetic effects, known as narrow-sense heritability. We evaluate relatives of ASD subjects using the same methods to evaluate the assumptions of the additive model and partition families by simplex/multiplex status to determine how heritability changes with status. RESULTS: By analyzing common variation throughout the genome, we show that common genetic polymorphism exerts substantial additive genetic effects on ASD liability and that simplex/multiplex family status has an impact on the identified composition of that risk. As a fraction of the total variation in liability, the estimated narrow-sense heritability exceeds 60% for ASD individuals from multiplex families and is approximately 40% for simplex families. By analyzing parents, unaffected siblings and alleles not transmitted from parents to their affected children, we conclude that the data for simplex ASD families follow the expectation for additive models closely. The data from multiplex families deviate somewhat from an additive model, possibly due to parental assortative mating. CONCLUSIONS: Our results, when viewed in the context of results from genome-wide association studies, demonstrate that a myriad of common variants of very small effect impacts ASD liability.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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

Citations432
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueMolecular AutismSame topicAutism Spectrum Disorder ResearchFrench-language works237,207