114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population
Bibliographic record
Abstract
Abstract The advancement in various machine learning (ML) methods provides tools to extract features from a large data set of complex traits and to predict the outcome of the target trait. In this study, we assessed the genomic prediction accuracy using single trait models based on kernel ridge regression (ST_KRR) and linear support vector regression (STLinearSVR) for female feed intake, feed efficiency and fertility traits of Canadian crossbreed beef cattle (n = 2,834 genotyped with 83,875 single nucleotide polymorphisms) and compared their prediction performances to a single trait genomic best linear unbiased prediction (STGBLUP) method. We also evaluated the performances of multiple-trait genomic prediction methods including MAK-based KRR, MAK-based LinearSVR (MAK_MTLinearSVR), and multiple-trait genomic best linear unbiased prediction (MTGBLUP). For the single trait methods, we found that the ST_KRR model was 1.86% to 26.86% more accurate for feed intake and fertility traits including residual feed intake adjusted for backfat thickness (RFIfat), daily dry matter intake (DMI), feeding event duration (DUR), age of first calving (AFC), pre-breeding backfat at first parity (PBBF), and pre-breeding weight at first parity (PBWT) than the STGBLUP method, which had an average accuracy of 0.39 for the traits investigated. For growth and development traits including julian date of birth (JulianDt), weaning weight adjusted to 200 d (WT200d), and birth weight (BRWT) of heifers, STLinearSVR yielded a 5.44% to 9.92% greater accuracy than STGBLUP. As for the multiple trait methods, MAK_MTLinearSVR had a 1.1% to 20.42% greater accuracy for PBWT, AFC, WT200d, DUR, RFIfat, and BRWT than the single trait model of STLinearSVR, while MTGBLUP had a 0.42% to 32.64% greater accuracy for AFC, RFIfat, WT200d and PBBF than the single trait model of STGBLUP. Our results demonstrate the potential application of ML-based multiple-trait models in beef cattle to improve the effectiveness of genomic selection programs, in particular for traits with low heritability.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".