Discriminating between allele‐ and genotype‐specific transmission ratio distortion
Bibliographic record
Abstract
Transmission ratio distortion (TRD) is defined as the observed deviation from the expected Mendelian inheritance of alleles from heterozygous parents. This phenomenon is attributed to various biological mechanisms acting on germ cells, embryos or fetuses, or even in early postnatal life. Current statistical approaches typically use two independent parametrizations assuming that TRD relies on allele- or genotype-related mechanisms, although they have never been tested and compared. This study compared allele- and genotype-related TRD models on simulated datasets with 1000 genotyped offspring and real data from 168 sire-dam-offspring beef cattle trios. The analysis of simulated datasets favored the true model of analysis in most cases (>93%), and a low percentage of missidentification occurred under (almost) null dominance (genotype-related model) or similar and moderate-to-low sire- and dam-specific TRD parameters (allele-related model). Moreover, the correlation between simulated and predicted distortion parameters was high (>0.97) under the true model. The comparison of allele- and genotype-related TRD models is an appealing tool to infer the biological source of TRD (i.e. haploid vs. diploid cells) when screening the whole genome. The analysis of beef cattle data corroborated a TRD region previously reported in chromosome 4, although discarding allele-related mechanisms and favoring the genotype-related model as the more reliable one. The results of this study highlight the relevance of implementing and comparing different parametrizations to capture all kinds of TRD, and to compare them using appropriate statistical methods.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".