Fine Mapping of Polymorphic Alcohol‐Related Quantitative Trait Loci Candidate Genes Using Interval‐Specific Congenic Recombinant Mice
Bibliographic record
Abstract
BACKGROUND: The inbred long-sleep (ILS) and inbred short-sleep (ISS) strains of mice are widely studied as a model of initial sensitivity to alcohol. Recently, a large comparative DNA sequencing study of candidate genes located within the four Lore quantitative trait loci (QTLs) associated with the ethanol-induced loss of righting reflex in ILS and ISS mice has identified eight genes that contain coding region differences corresponding to amino acid changes. Here, recently developed interval-specific congenic recombinant mice (ISCRs) have been used to map these genes in relationship to newly narrowed QTL regions. METHODS: Regions of candidate genes containing DNA differences corresponding to previously identified amino acid changes between ISS and ILS mice were amplified from either genomic DNA or complementary DNA from ISCR mice using polymerase chain reaction. The products were purified and directly sequenced to determine the genotypes for each polymorphism. On the basis of these genotypic data, each candidate gene was determined to be located either within or outside of recently narrowed Lore QTL intervals. RESULTS: Of these eight candidates with protein-coding differences, five are now excluded from their respective Lore intervals. The other three (Znf142, Ptprn, and Znf133) have been localized to the narrowed QTL intervals. CONCLUSIONS: These three central nervous system genes (Znf142, Ptprn, and Znf133) represent promising candidates for involvement in the differential sensitivity to alcohol exhibited between ILS and ISS mice. This study also demonstrates how the combination of high-throughput comparative gene sequencing and concomitant genetic fine mapping of QTL regions with ISCRs can be an effective tool for accelerating the process of moving from QTL to gene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".