Genetical Genomics: Combining Gene Expression with Marker Genotypes in Poultry
Bibliographic record
Abstract
Microarrays have been widely implemented across the life sciences, although there is still debate on the most effective uses of such transcriptomics approaches. In genetical genomics, gene expression measurements are treated as quantitative traits, and genome regions affecting expression levels are denoted as expression QTL (eQTL). The detected eQTL can represent a locus that lies close to the gene that is being controlled (cis-acting) or one or more loci that are unlinked to the gene that is being controlled (trans-acting). One powerful outcome of genetical genomics is the reconstruction of genetic pathways underlying complex trait variation. Because of the modest size of experiments to date, genetical genomics may fall short of its promise to unravel genetic networks. We propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. Among livestock species, poultry is well placed to embrace this technology with the availability of the chicken genome sequence, microarrays for various platforms, as well as experimental populations in which QTL have been mapped. In the buildup toward full-blown eQTL studies, we can study the effects of known candidate genes or marked QTL at the gene expression level in more focused studies. To demonstrate the potential of genetical genomics, we have identified the cis and trans effects for a functional BW QTL on chicken chromosome 4 in breast tissue samples from chickens with contrasting QTL genotypes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".