Effects of epistatic interaction on detection and parameter analysis of quantitative trait loci
Bibliographic record
Abstract
Recent scientific support for the involvement of genetic locus interaction in quantitative trait variation and the widespread use of quantitative trait locus (QTL) mapping has resulted in the need to examine those aspects concurrently. Computer software was written to simulate interacting quantitative trait loci (QTLs) in plant populations. Using this software, interacting QTLs were simulated to examine effects of epistasis on the detection of QTLs and the quality of QTL parameter estimates. Simulations involved doubled haploid populations exhibiting two non-epistatic traits and seven epistatic traits, each trait at four levels of heritability. Detection efficiency of QTL main and interaction effects decreased with decreasing heritability. At a given level of broad-sense heritability, traits differed with respect to the relative quality of main-effect detection and interaction-effect detection. Main-effect detection was notably poor for one epistatic locus that has a relatively small additive effect. Position estimates were accurate but their precision deteriorated with decreasing heritability. The quality of QTL effect estimates declined consistently with decreasing heritability, and loss in the accuracy was associated with losses in power of detection.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".