Developing a liquid capture chip to accelerate the genetic progress of cattle
Bibliographic record
Abstract
Abstract Large‐scale genotyping at a low cost is crucial for molecular breeding of livestock. In this study, the Cattle110K capture chip was developed, based on the genotyping by target sequencing system. The chip panel included 112,180 single necleotide polymorphisms (SNPs), from potential functional regions screened by genome‐wide associated study, BayesB, expression quantitative trait loci‐mapping, ATAC‐seq, and reported functional markers. All the SNPs on the panel were distributed evenly on the cattle genome, with more than 99% of the markers having a minor allele frequency greater than 0.05. Assessment results indicated that a total of 1.2 M high‐quality SNPs were identified in the 110 K regions, averaging approximately 10 SNPs per target sequence. The genotype consistency for the repetitive samples using the Cattle110K liquid chip was 99.21% while the concordance between the Illumina BovineHD BeadChip and this chip averaged 98.17%. A significant association signal for slaughter weight and carcass length was identified on 37.3–41.5 Mb of chromosome 6, pinpointing the NCAPG‐LCORL locus. This locus has previously been associated with meat and carcass traits in cattle. Additionally, novel candidate regions were identified around 3.4 Mb of chromosome 13 and 73.5 Mb of chromosome 8, significantly correlated with hip height and marbling score, respectively. We compared the accuracy of genomic estimated breeding values between the Illumina BovineHD BeadChip and this chip. The results demonstrated that the Cattle110K capture chip had a comparable ability in genomic prediction to the Illumina BovineHD BeadChip. Advances in using the cost‐effective liquid capture chip are expected to accelerate the genetic progress of cattle in the coming years.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".