9 A comparison of different genomic prediction methods in the Canadian Landrace swine population
Bibliographic record
Abstract
Abstract Increasing the accuracy of breeding value prediction can lead to more profitability through accelerating genetic progress for economic traits. The objective of this study was to assess the predictive abilities and unbiasedness of best linear unbiased prediction (BLUP) and popular genomic prediction methods of BayesC, BayesC(π = 0.99), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP). Genotypic information (50K and 60K) of 4,890 performance tested Landrace pigs before February 2019 and 471 validation Landrace pigs that both had phenotypic information on backfat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) from two Canadian pig breeding companies (AlphaGene and Alliance Genetics Canada) were used. The de-regressed breeding values (DEBV) were employed in GBLUP and Bayesian methods. A total number of 48,580 single nucleotide polymorphisms remained after quality control and imputation steps. The prediction accuracies were calculated using the correlation between predicted breeding values before performance test and DEBVs after performance test. All employed genomic prediction methods showed higher prediction accuracies for BFT (50.80–52.68%), ADG (26.61–34.47%), and LMD (18.25–25.08%) compared to BLUP method (BFT = 28.54%, ADG = 16.41%, LMD = 17.15%). The highest prediction accuracies for BFT and ADG were obtained using ssGBLUP method, and for LMD it was obtained using BayesC(π = 0.99). The BayesC(π = 0.99) showed also the lowest prediction biases across the studied traits (+0.05 for BFT, 0.00 for AGD, and -0.10 for LMD). In conclusion, our results revealed the superiority of ssGBLUP (for BFT and ADG) and BayesC(π = 0.99) (for LMD) over other tested methods in this study. However, the prediction accuracies from the tested genomic prediction methods were not significantly different from each other. Thus, employing these methods can be helpful for accelerating the genetic improvement of BFT, ADG, and LMD in the moderate population size of Canadian Landrace.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".