Transmission disequilibrium test for quantitative trait loci detection in livestock populations
Bibliographic record
Abstract
The performance of several transmission disequilibrium tests (TDT) for detection of quantitative trait loci (QTL) in data structures typical of outbred livestock populations were investigated. Factorial mating designs were simulated with 10 sires mated to either 50 or 200 dams, each family having five or eight full sibs. A single marker and QTL, both bi-allelic, were simulated using a disequilibrium coefficient based on complete initial disequilibrium and 50 generations of recombination [i.e. D = D(0)(1 - theta)50], where theta is the recombination fraction between marker and QTL. The QTL explained either 10% (small QTL) or 30% (large QTL) of the genetic variance for a trait with heritability of 0.3. Methods were: TDT for QTL (Q-TDT; both parents known), 1-TDT (only one parent known) and sibling-based TDT (S-TDT; neither parent known, but sibs available). All were found to be effective tests for association and linkage between the QTL and a tightly linked marker (theta < 0.02) in these designs. For a large QTL, theta = 0.01, and five full sibs per family, the empirical power for Q-TDT, 1-TDT and S-TDT was 0.966, 0.602 and 0.974, respectively, in a large population, versus 0.700, 0.414 and 0.654, respectively, in a small population. For a small QTL effect, theta = 0.01, large population the empirical power of these tests were 0.709, 0.287 and 0.634. The power of Q-TDT, 1-TDT and S-TDT was satisfactory for large populations, for QTL with large effects and for five full sibs per family. The 1-TDT based on a linear model was more powerful than the normal 1-TDT. The empirical power for Q-TDT and 1-TDT with a linear model was 0.978 and 0.995 respectively. TDT based on analogous linear models, incorporating the polygenic covariance structure, provided only small increases in power compared with the usual TDT for QTL.
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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 | 0.047 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".