Detection of breed-specific copy number variations in domestic chicken genome
Bibliographic record
Abstract
Copy number variations (CNVs) are important large-scale variants. They are widespread in the genome and may contribute to phenotypic variation. Detection and characterization of CNVs can provide new insights into the genetic basis of important traits. Here, we perform whole-genome short read sequence analysis to identify CNVs in two indigenous and commercial chicken breeds to evaluate the impact of the identified CNVs on breed-specific traits. After filtration, a total of 12 955 CNVs spanning (on average) about 9.42% of the chicken genome were found that made up 5467 CNV regions (CNVRs). Chicken quantitative trait loci (QTL) datasets and Ensembl gene annotations were used as resources for the estimation of potential phenotypic effects of our CNVRs on breed-specific traits. In total, 34% of our detected CNVRs were also detected in earlier CNV studies. These CNVRs partly overlap several previously reported QTL and gene ontology terms associated with some important traits, including shank length QTL in Creeper-specific CNVRs and body weight and egg production characteristics, as well as muscle and body organ growth, in the Arian commercial breed. Our findings provide new insights into the genomic structure of the chicken genome for an improved understanding of the potential roles of CNVRs in differentiating between breeds or lines.
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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 | 0.000 | 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".