An integrative genomic toolkit for studying the genetic, evolutionary, and molecular underpinnings of eusociality in insects
Bibliographic record
Abstract
While genomic resources for social insects have vastly increased over the past two decades, we are still far from understanding the genetic and molecular basis of eusociality. Here, we briefly review three scientific advancements that, when integrated, can be highly synergistic for advancing our knowledge of the genetics and evolution of eusocial traits. Population genomics provides a natural way to quantify the strength of natural selection on coding and regulatory sequences, highlighting genes that have undergone adaptive evolution during the evolution or maintenance of eusociality. Genome-wide association studies (GWAS) can be used to characterize the complex genetic architecture underlying eusocial traits and identify candidate causal variants. Concurrently, CRISPR/Cas9 enables the precise manipulation of gene function to both validate genotype-phenotype associations and study the molecular biology underlying interesting traits. While each approach has its own advantages and disadvantages, which we discuss herein, we argue that their combination will ultimately help us better understand the genetics and evolution of eusocial behavior. Specifically, by triangulating across these three different approaches, researchers can directly identify and study loci that have a causal association with key phenotypes and have evidence of positive selection over the relevant timescales associated with the evolution and maintenance of eusociality in insects.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".