The dynamics of the genotype-phenotype association
Bibliographic record
Abstract
The integrity of an organism is maintained by networks of interacting genes. Such networks predict that genetic variants affect phenotypes in a nonadditive fashion. That is, the effect of an allelic variation in one gene is dependent on the variations in other genes. We summarize the analyses of a series of genes in a White Leghorn strain that support the existence of such gene networks: 1) genes are pleiotropic, 2) genes affect trait correlations, 3) genes affect trait distributions in a nonadditive fashion, 4) genes interact with each other, and 5) genes are at linkage disequilibrium, even when located on different chromosomes. The latter observation indicated that certain gene combinations lead to reduced reproductive fitness. Each candidate genes we analyzed segregated for multiple alleles that affected production traits. This finding was surprising, even for a strain with a large effective population size. The shapes of trait distributions appear to be a better descriptor of gene effects than measures of central tendency. Despite this complexity, it is feasible to conduct DNA-based selection, starting from any of several different genes that affect a trait. Gene networks may be altered in many different ways to improve a particular phenotype, but networks may differ in their effects on other traits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".