Accounting for heterogeneity of variances to improve the precision of QTL mapping in dairy cattle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The principle of interval mapping for quantitative trait loci (QTL) was originally developed for the analysis of single backcross data but it has been increasingly applied to more complicated experimental designs and data structures. It is important to study whether accounting for the heterogeneity of variance would improve the precision of QTL mapping based on data of multiple populations or families. This study compared homogeneous and heterogeneous maximum likelihood approaches for QTL mapping. The data consisted of 433 sons from six sire families with 69 microsatellite markers distributed over 12 chromosomes. The results of this study indicate that the heterogeneous approach generally produced a smaller residual variance and thus provided a better fit to the data than the homogeneous approach, meaning that the heterogeneous approach offers better precision in estimating both positions and effects of QTL. The results further showed that accounting for the heterogeneity of residual variance led to different statistical inferences from ignoring the heterogeneity of variance in QTL mapping. The heterogeneous approach is useful for QTL mapping based on the joint data of diverse reference populations or heteroscedastic data obtained from crossing animals with different genetic backgrounds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it