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Confirmation and Genetic Dissection of a Major Quantitative Trait Locus for Alcohol Preference Drinking

2004· article· en· W1970087199 on OpenAlexfundno aff
Cathy Ruf, Phyllis Carosone‐Link, Justin Springett, Beth Bennett

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory HealthNational Institute on Alcohol Abuse and Alcoholism
KeywordsQuantitative trait locusCongenicGeneticsBiologyBackcrossingLocus (genetics)TraitPhenotypeCandidate geneGeneGenetic linkageChromosomeChromosome 4Family-based QTL mappingGene mappingComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: In previous work, we created congenic strains that carry the DBA/2IBG (D2) region for alcohol preference on chromosome 2, on an otherwise C57BL/6IBG (B6) background. Here, we report construction and testing of interval-specific congenic recombinant strains (ISCRSs) for the purpose of narrowing the quantitative trait loci (QTL) interval. METHODS: ISCRSs were derived by identifying mice that carry recombination events in the D2 interval, during the backcrossing for congenics. Recombinant mice were backcrossed to B6, and progeny that carry the reduced chromosome 2 region were tested for its effect on the alcohol preference phenotype. RESULTS: We developed multiple ISCR strains, which spanned the QTL interval. Three of these showed the D2 phenotype of reduced alcohol consumption. The overlap of two of these strains reduced the QTL interval from 66.8 to 3.5 Mb. A third positive ISCRS suggests the possibility of a second, linked QTL. CONCLUSIONS: Use of ISCRSs can narrow a QTL region to a few Mb. This reduced interval size will facilitate identification of candidate genes, through bioinformatics, gene expression, and DNA sequencing strategies. Potential difficulties, including reduced power as a result of variable phenotypes or small effect size, are discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.296

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 head0.218
GPT teacher head0.451
Teacher spread0.233 · 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 designBench or experimental
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

Citations10
Published2004
Admission routes1
Has abstractyes

Explore more

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