Bias and accuracy of body weight trait evaluations of an F2 chicken using single-step genomic best linear unbiased prediction (ssGBLUP)
Bibliographic record
Abstract
The objectives of this study were (i) to compare the accuracy and bias of estimates of breeding values for body weight (BW) at 2–7 weeks of age using pedigree-based best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, and (ii) to determine the best level of minor allele frequencies (MAFs) for pre-selection of SNPs for genomic prediction (GP). Records of 488 F2 broiler chickens obtained from crossbreeding of fast-growing Arian chickens and slow-growing Iranian native chickens at 2–7 weeks of age were used. Samples were genotyped using Illumina Chicken 60K BeadChip. To investigate the effect of MAFs on the accuracy of prediction, 48 379 quality-controlled SNPs were grouped into five subgroups with MAF bins 0.05–0.1, 0.1–0.2, 0.2–0.3, 0.3–0.4, and 0.4–0.5. Our results confirmed the superiority of ssGBLUP compared to traditional BLUP methodology. The average accuracy of GP improved by 59.03%, 220.34%, 0.46%, 5.61%, 0.45%, and 2.73% using ssGBLUP compared to BLUP for BW at 2–7 weeks of age, respectively. Depending on the age group, using a subset of SNPs with a specific MAF bin compared to all SNPs resulted in a remarkable improvement of GP accuracy for the observed traits.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".