Accounting for heterogeneity of variances to improve the precision of QTL mapping in dairy cattle
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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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".