MétaCan
Menu
Back to cohort
Record W2147283608 · doi:10.1038/oby.2006.185

A Quantitative Trait Locus for Body Fat on Chromosome 1q43 in French Canadians: Linkage and Association Studies

2006· article· en· W2147283608 on OpenAlexafffundabout
Brahim Aı̈ssani, Louis Përusse, Gilles Lapointe, Luigi Bouchard, Brandon Walts, Claude Bouchard

Bibliographic record

VenueObesity · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsQuantitative trait locusLocus (genetics)GeneticsGenetic linkageBiologyLinkage disequilibriumTransmission disequilibrium testGenetic associationAssociation mappingMicrosatelliteSingle-nucleotide polymorphismGeneAlleleGenotype

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore a quantitative trait locus (QTL) on human chromosome 1q affecting BMI, adiposity, and fat-free mass phenotypes in the Quebec Family Study cohort. RESEARCH METHODS AND PROCEDURES: Non-parametric sibpair and variance component linkage analyses and family-based association studies were performed with a dense set of chromosome 1q43 microsatellites and single-nucleotide polymorphism markers in 885 adult individuals. RESULTS: Linkage was observed between marker D1S184 and BMI (p = 0.0004) and with body fat mass or percentage body fat (p < or = 0.0003), but no linkage was detected with fat-free mass. Furthermore, significant linkages (p < 0.0001) were achieved with subsamples of sibpairs at both ends of phenotype distributions. Association studies with quantitative transmission disequilibrium tests refined the linkage to a region overlapping the regulator of G-protein signaling 7 (RGS7) gene and extending to immediate upstream gene loci. DISCUSSION: The present study indicates that the QTL on chromosome 1q43 specifically affects total adiposity and provides a genetic mapping framework for the dissection of this adiposity locus.

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 score0.356
Threshold uncertainty score0.402

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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

Citations26
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueObesitySame topicGenetic Mapping and Diversity in Plants and AnimalsFrench-language works237,207