A new approach to quantify the adaptive potential of gene expression variation in gymnosperms
Bibliographic record
Abstract
Variation in patterns of gene expression contributes to phenotypic diversity and can ultimately predict adaptive responses. However, in many cases, the consequences of regulatory mutations on patterns of gene expression and ultimately phenotypic differences remain elusive. A standard way to study the genetic architecture of expression variation in model systems has been to map gene expression variation to genetic loci (Fig. 1a). At the same time, in many nonmodel species, especially for long-lived organisms, controlled crosses are not feasible. If we are to expand our understanding of the role of regulatory mutations on phenotypes, we need to develop new methodologies to study species under ecologically relevant conditions. In this issue of Molecular Ecology, Verta et al. (2013) present a new approach to analyse gene expression variation and regulatory networks in gymnosperms (Fig. 1b). They capitalized on the fact that gymnosperm seeds contain an energy storage tissue (the megagametophyte) that is directly derived from a single haploid cell (the megaspore). The authors identified over 800 genes for which expression segregated in this maternally inherited haploid tissue. Based on the observed segregation patterns, these genes (Mendelian Expression Traits) are most probably controlled by biallelic variants at a single locus. Most of these genes also belonged to different regulatory networks, except for one large group of 180 genes under the control of a putative trans-acting factor. In addition, the approach developed here may also help to uncover the effect of rare recessive mutations, which usually remain hidden in a heterozygous state in diploid individuals. The appeal of the work by Verta et al. (2013) to study gene expression variation is in its simplicity, which circumvents several of the hurdles behind traditional expression quantitative trait locus (eQTL) studies, and could potentially be applied to a large number of species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".