Combining Genotypic, Phenotypic and Pedigree Information to Analyze Functional Traits in Dairy Cattle
Bibliographic record
Abstract
Reliabilities of genomic estimated breeding values (GEBV) for functional traits, e.g. health, fertility and reproduction, remain low compared to those for production in dairy cattle. This is likely because large training populations are required for evaluation of lowly heritable traits. Different strategies have been proposed to overcome this limitation, such as the use of genotyped cows, inclusion of external bulls (multi-trait across-country evaluation, MACE), and adoption of different methodologies, such as the simultaneous use of genotyped and non-genotyped animals, known as the single-step genomic BLUP (ssGBLUP). Thus, the main objectives of this thesis were to evaluate strategies for combining genotypic, phenotypic and pedigree information to analyze functional traits in Holstein cattle and investigate the effects of two deleterious recessive haplotypes (AH1 and AH2) on reproduction performance of Canadian Ayrshire cattle. Data for various functional traits recorded in Canada and MACE estimated breeding values were obtained from the Canadian Dairy Network (CDN, Guelph, Canada). Additionally, information of carriers and non-carriers bulls for AH1 and AH2 were used to investigate their effects on reproductive performance. Genomic predictions were obtained using multi-step and single-step GBLUP under different strategies, such as adding genotyped cows in the evaluation, integrating MACE information, blending traditional and genomic evaluations, and using different proportions of polygenic effect. A genome-wide association study and functional analyses were also performed for three fertility disorders, namely retained placenta, metritis and cystic ovaries. Genomic predictions for functional traits benefited greatly from simultaneous use of phenotypes, pedigree, and genotypes. Integration of MACE data using ssGBLUP yielded the highest reliabilities compared to other methods and also helped reduce bias of genomic predictions. Effects of AH1 and AH2 on reproductive performance of Canadian Ayrshire cattle were validated. A negative effect of AH1 on stillbirth rates was observed, whereas AH2 had a negative impact on 56-day non-return rate. These findings provide valuable information on strategies to more accurately predict GEBV for functional traits in dairy cattle by adopting ssGBLUP and different sources of domestic and foreign information. Biological understanding of reproductive disorders and validated lethal haplotypes affecting fertility will help enhance accuracy of selection and mating plans in the Canadian dairy cattle breeding programs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".