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Record W2914654999 · doi:10.1016/j.gene.2019.02.020

Curation and bioinformatic analysis of strabismus genes supports functional heterogeneity and proposes candidate genes with connections to RASopathies

2019· article· en· W2914654999 on OpenAlexafffund
Xin Ye, Robin van der Lee, Wyeth W. Wasserman

Bibliographic record

VenueGene · 2019
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsBiologyGenePhenotypeGeneticsCandidate geneComputational biologyStrabismus

Abstract

fetched live from OpenAlex

Strabismus refers to the misalignment of the eyes and occurs in 2–4% of individuals. The low-resolution label “strabismus” covers a range of heterogeneous defects, which makes it challenging to unravel this condition. Consequently a coherent understanding of the causes is lacking. Here, we attempt to gain a better understanding of the underlying genetics by combining gene curation, diverse bioinformatic analyses (including gene ontology, pathway mapping, expression and network-based methods) and literature review. Through a phenotype-based curation process, we identify high-confidence and permissive sets of 54 and 233 genes potentially involved in strabismus. These genes can be grouped into 10 modules that together span a heterogeneous set of biological and molecular functions, and can be linked to clinical sub-phenotypes. Multiple lines of evidence associate retina and cerebellum biology with the strabismus genes. We further highlight a potential role of the Ras-MAPK pathway. Independently, sets of 11 genes and 15 loci tied to strabismus with definitive genetic basis have been compiled from the literature. We identify strabismus candidate genes for 5 of the 15 reported loci ( CHD7; SLC9A6; COL18A1 , COL6A2; FRY , BRCA2 , SPG20; PARK2 ). Finally, we synthesize a Strabismus Candidate Gene Collection, which together with our curated gene sets will serve as a resource for future research. The results of this informatics study support the heterogeneity and complexity of strabismus and point to specific biological pathways and brain regions for future focus. • Bioinformatics-driven analysis of genes potentially contributing to strabismus • Curated sets of 54 stringent, 233 permissive, and 18 candidate strabismus genes • PARK2 identified as candidate gene for published locus 6q24.2-6q25.1 • Expression analyses highlight ties to retina and cerebellum. • Pathway analysis highlights the Ras-MAPK pathway and RASopathy genes.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.009
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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
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

Citations11
Published2019
Admission routes2
Has abstractyes

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