121 Quantifying Genetic Relationships to Maintain Genetic Diversity in the Canadian Dairy Population
Bibliographic record
Abstract
Abstract Intensive selection pressure coupled with the use of a limited number of high genetic merit sires has resulted in reduced effective population size and increased levels of inbreeding in dairy cattle populations. The increased rate of inbreeding amplifies the frequency of recessive deleterious alleles, possibly leading to inbreeding depression in economically important traits, such as disease resistance, fertility and production. To better understand the relationship of available and active breeding sires to Canadian herds, quantifying genetic relationships between these groups could prove insightful. The genetic relationship value (R-value) represents the average number of alleles identical by descent shared between an animal and a reference population and is currently estimated based on pedigree information. Furthermore, the average R-value indicates the relationship of a sire to the rest of the population. Estimating the R-value between sires and individual herds may offer a more refined tool for producers to select sires that are less related to their specific herds. The aim of this study was to quantify and characterize R-values between individual sires and individual herds within the Canadian Holstein population. To quantify R-values, a dataset comprised of 11,914 sires born between 1953 and 2020 and 584,740 active cows born between 1997 and 2022 from 5,592 herds was considered. Active cows were defined as those currently alive, on milk recording, and actively contributing to the national milk inventory. Active sires were considered those used to breed the active cows. All data were provided by Lactanet Canada and analyses were carried out on PEDIG using the par2 program for relationship estimation. Results indicate that herd-level R-values (i.e., R-values estimated using individual sires and individual herds) showed variation. The R-value of individual sires to individual herds containing active cows ranged from 0.43% to 32.38. This result indicates that outbred sires are available to individual farmers when the population is considered at the herd level, and that the development of herd-level R-values may provide a selection tool which promotes the use of less related sires. Next steps include developing and estimating genomic R-values and comparing them with the pedigree-based R-values for the genotyped Canadian Holstein population. By better understanding of the relationship of available breeding sires to individual Canadian herds, a simple tool for producers to identify sires which are less related to their herds can be developed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".