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Genetic variation in CRTh2 influences development of allergic phenotypes

2009· article· en· W2022906409 on OpenAlexaff
Lisa Cameron, Martin Depner, Michael Kormann, N. Klopp, Thomas Illig, Erika von Mutius, Michael Kabesch

Bibliographic record

VenueAllergy · 2009
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSingle-nucleotide polymorphismHaplotypeImmunologyAllergyImmunoglobulin ESNPGenetic variationPopulationPhenotypeAsthmaMedicineGenetic predispositionGenetic variabilityBiologyGenotypeGeneticsGeneAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Allergic disorders are characterized by an increase in the Th2 cytokines IL-4, IL-5 and IL-13, produced primarily by Th2 cells. These cells are marked by the expression of CRTh2 (chemoattractant receptor-homologous molecule expressed on Th2 cells), a receptor for prostaglandin D(2). As genetic variation plays a significant role in the predisposition for allergic disorders, we investigated the influence of single nucleotide polymorphisms (SNPs) in CRTh2. METHODS: In a large study population of German children (n = 4264) from the International Study of Asthma and Allergy in Children (ISAAC II), six polymorphisms in CRTh2 were genotyped. Statistical analyses were performed using single SNP and haplotype analyses. RESULTS: Uncorrected associations among -6373G>A, +1431G>C and +1538A>G were observed with a number of allergic phenotypes (P < 0.05). After correction, association between +1431C and specific IgE to food allergens remained significant (P = 0.04). Associations of haplotype (H)3 (containing +1538G) with reduced risk for asthma and H2 (containing +1431C) with increased risk for specific IgE to food allergens also remained significant after correction for multiple testing (P = 0.004). CONCLUSIONS: Genetic variation within CRTh2 modifies the development of allergic sensitization and asthma in a population of German children.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.237

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 head0.012
GPT teacher head0.254
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 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

Citations21
Published2009
Admission routes1
Has abstractyes

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