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Record W2128297440 · doi:10.1001/archneurol.2010.351

Resequencing of 29 Candidate Genes in Patients With Familial and Sporadic Amyotrophic Lateral Sclerosis

2011· article· en· W2128297440 on OpenAlexafffund
Hussein Daoud, Paul N. Valdmanis, François Gros‐Louis, Véronique Belzil, Dan Spiegelman, Édouard Henrion, Ousmane Diallo, Anne Desjarlais, Julie Gauthier, William Camu, Patrick A. Dion, Guy A. Rouleau

Bibliographic record

VenueArchives of Neurology · 2011
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversité LavalUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsNonsynonymous substitutionAmyotrophic lateral sclerosisMissense mutationGeneticsGeneBiologyCandidate geneMutationSynonymous substitutionExome sequencingDiseaseCoding regionMedicineGenomePathologyCodon usage bias

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify novel disease-causing genes for amyotrophic lateral sclerosis (ALS). DESIGN, SETTING, AND PATIENTS: We carried out a systematic mutation screening of the entire coding regions of 29 candidate genes encoding critically important proteins for proper differentiation and development of corticospinal motor neurons in 190 patients with familial and sporadic ALS. MAIN OUTCOME MEASURES: We focused our analysis on coding variants and evaluated the distribution of nonsynonymous and synonymous variants in our cohort of patients with ALS. RESULTS: We identified 40 novel nonsynonymous variants and showed a significant excess of unique nonsynonymous variants in our cohort of patients with ALS, which suggests the presence of ALS-predisposing mutations. CONCLUSIONS: Using a multifaceted approach based on the functional prediction of missense variants, the conservation of the altered amino acid, and the cosegregation of the variants identified in familial cases, we identified several promising novel genes for ALS such as LUM and CRYM. We have also highlighted the analytical challenges of large-scale sequencing screens to detect disease-causing variants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.395

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 studies0.0000.001
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 head0.030
GPT teacher head0.230
Teacher spread0.200 · 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

Citations50
Published2011
Admission routes2
Has abstractyes

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