Genetic architecture of white striping in turkeys (Meleagris gallopavo)
Bibliographic record
Abstract
White striping (WS) is a myopathy of growing concern to the turkey industry. It is rising in prevalence and has negative consequences for consumer acceptance and the functional properties of turkey meat. The objective of this study was to conduct a genome-wide association study (GWAS) and functional analysis on WS severity. Phenotypic data consisted of white striping scored on turkey breast fillets (N = 8422) by trained observers on a 0-3 scale (none to severe). Of the phenotyped birds, 4667 genotypic records were available using a proprietary 65 K single nucleotide polymorphism (SNP) chip. The SNP effects were estimated using a linear mixed model with a 30-SNP sliding window approach used to express the percentage genetic variance explained. Positional candidate genes were those located within 50 kb of the top 1% of SNP windows explaining the most genetic variance. Of the 95 positional candidate genes, seven were further classified as functional candidate genes because of their association with both a significant gene ontology and molecular function term. The results of the GWAS emphasize the polygenic nature of the trait with no specific genomic region contributing a large portion to the overall genetic variance. Significant pathways relating to growth, muscle development, collagen formation, circulatory system development, cell response to stimulus, and cytokine production were identified. These results help to support published biological associations between WS and hypoxia and oxidative stress and provide information that may be useful for future-omics studies in understanding the biological associations with WS development in turkeys.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".