352 Awardee Talk: Identification of novel haplotypes with recessive and allelic inheritance patterns affecting embryonic development processes, gestation losses and post-natal lethality in cattle
Bibliographic record
Abstract
Abstract Genomic data allows the screening of homozygous haplotypes and recessive lethal alleles, which could affect reproductive performance in cattle and other species. Here, we propose an approach based on tracing the inheritance of alleles from heterozygous parents to offspring to identify significant departure from the expected Mendelian inheritance (Transmission Ratio Distortion – TRD). TRDscan software was used to identify genomic regions with TRD using 436,651 trios (sire-dam-offspring) of genotypes from Holstein dairy cattle. SNP-by-SNP analysis was performed using 132,990 SNPs. TRD haplotypes were identified using sliding windows of 2-,4-,7-,10- and 20-SNPs. In total, 109 SNPs and 495 haplotypes were identified with significant TRD (Bayes factor≥100). Interestingly, some of the identified TRD regions overlap with previously known regions with recessive lethal alleles (e.g., HH0, HH1, HH3, HH5). Novel genomic regions with significant TRD were also identified with annotated genes functionally clustered into specific phenotypes related to male and female infertility and postnatal lethality. Approximately 18% of previously identified quantitative trait loci mapped around the TRD regions were related with fertility traits (calving ease, scrotal circumference, fertility index, and non-return rate). Validation of the results was performed using ~13,000 of Holstein embryo genotypes, in trios. The results will be integrated with the TRD regions identified to fine mapping the contribution of the TRD for each embryonic stage and they may be helpful to precisely target genomic regions associated with fertility, embryonic development processes, gestation losses and post-natal lethality in cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.010 |
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".