PSIII-16 Genome-wide association mapping and functional analysis of body weight, feed intake and walking ability in turkeys
Bibliographic record
Abstract
Abstract The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Over the last few years, genome-wide association studies (GWAS) became the de facto approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with growth traits, feed intake and walking ability in a breeding turkey line. This was followed by studying the functional evidence that may support the impact of those regions on the economic traits in turkeys. A total of 31,950 phenotypic records for body weight, feed intake and walking ability with genomic (56,393 SNP) data were provided by Hybrid Turkeys, Kitchener, Canada. The analysis was carried out using a mixed linear model with hatch-week-year and sex fitted as fixed effects and the accumulated effect of all markers captured by the genomic relationship matrix fitted as random polygenic effects. Significant markers were observed on several chromosomes across the turkey genome. For example, COL8A1 and RBPMS2 genes were identified on chromosome 1 and 12, respectively, and associated with body weight. Furthermore, a gene-set enrichment analysis was performed for each trait. These functional and positional analyses uncovered a number of gene ontology functional terms, Reactome pathways and Medical Subject Headings that showed significant enrichment of genes associated with the studied traits. Many of the observed gene ontology functional terms (e.g., skeletal muscle tissue growth, regulation of digestive system process, and adult walking behavior) are known to be related to body growth, feed intake and walking ability. The results of this study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".