0310 Assessing genetic diversity in Canadian beef cattle populations using Illumina BovineSNP50 chip
Bibliographic record
Abstract
The main objective of this study was to utilize genomic profiles to assess genetic diversity within and between Canadian beef cattle populations to gain insights on population admixture and dynamics. Individuals (n = 2831) were genotyped for Illumina BovineSNP50 for 9 populations (Gelbvieh (GVH, n = 488), Charolais (CHA, n = 396), Angus (AAN, n = 492), Simmental (SIM, n = 404), Limousin (LIM, n = 205), Hereford (HER, n = 591), Hays Converter (HC, n = 208), Kinsella composites (KC, n = 15) and Lacombe Research Centre (LRC, n = 33). A total of 2828 individuals with 43,172 SNPs across 29 autosomes passed quality control and were used for further analyses. To study population structure between populations, a principal component analysis (PCA) was performed using SNP1101 software. Genomic inbreeding coefficients for each individual were estimated using 4 methods; VanRaden 2008 (Fv), Leutenegger 2003 (Fl), excess of homozygosity (Fh) and GCTA software (Fg) method implemented in SNP1101 software. The PCA analysis reported clear divergence between GVH, CH, AAN, SIM, LIM, HER and HC populations where 7 clusters were well defined, illustrated in Fig. 1. The KC and LRC populations are distributed between the other clusters confirming their genetic architectures as crossbred. The most genomically divergent breeds were CHA, AAN, GVH and HER. The correlations between inbreeding coefficients Fv with Fl and Fg were strong; 0.98 and 0.93, respectively. The average estimate of genomic inbreeding coefficients (Fv, Fl, and Fg) were highest for the HER ranged from 12.8 ± 0.1to 18.5 ± 0.2% followed by AAN ranged from 10 ± 0.1 to 12.7 ± 0.1%. In addition, the genomic inbreeding coefficients for composites/crossbreds ranged from 2.0 ± 1.0 to 4.0 ± 1.0% and from 1.0 ± 0.7 to 7.0 ± 1.0% for KC and LRC, respectively, where these inbreeding levels were low across all methods compared with purebred cattle. In conclusion, the genomic assessment of inbreeding using different methods indicated that HER and AAN breeds had the highest inbreeding level and thus inbreeding depression should be assessed for their traits at the genome level. Information on specific regions that are fixed for deleterious alleles allows directed introgression between breeds to help address performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".