21 Unravelling Transmission Ratio Distortion Across the Genome of a Crossbreed Beef Cattle Population
Bibliographic record
Abstract
Abstract Transmission ratio distortion (TRD) is a process when one allele from either parent is preferentially transmitted to the offspring. The identification of genomic regions affected by TRD might help in the detection of lethal alleles or potential genes affecting reproduction. Here, we investigated TRD in crossbreed beef cattle population aiming to identify genomic regions showing altered deviations in segregation that could be affecting reproduction performance. A total of 237 genotyped animals were used including 46 sires, 80 dams, and 111 parent-offspring (trios). The predominant breeds of these animals were Angus (61.83%), Simmental (18.99%), Gelbvieh (6.12%), Charolais (3.65%), Hereford (2.46%) and Limousin (1.57%). After excluding SNPs with minor allele frequency lower than 0.05 and call-rate lower than 0.90, a total of 369,902 autosomal SNPs were retained for further analyses. The SNP-by-SNP analysis was performed within a Bayesian framework using TRDscanv.2.0 software, using 100,000 iterations, with 10,000 iterations being discarded as burn-in. As table 1 shows, 33 SNPs were identified with TRD, considering a Bayes Factor (BF)≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. Among them, 26 SNPs were parent-unspecific and 7 SNPs were parent-specific TRD. For parent-specific TRD, 214 were identified for sire- and 162 for dam-TRD (BF≥100). Among them, 4 SNPs were detected with sire- and dam-TRD in opposite direction of preference of transmission. Preliminary functional and positional analysis was performed using the list of TRD regions with BF≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. For sire-TRD, 14% of the identified QTL (n = 254) were related to non-return rate. For dam-TRD, 21 regions related to conception rate were found (1.5%) and 13 regions related to stillbirth (0.93%). Haplotype analysis is in progress to identify additional candidate regions and alleles with TRD to better understand this phenomenon in a crossbreed beef population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".