Equivocal evidence for a link between megalencephaly-related genes and primate brain size evolution
Bibliographic record
Abstract
A large brain is a defining feature of modern humans, and much work has been dedicated to exploring the molecular underpinnings of this trait. Although numerous studies have focused on genes associated with human microcephaly, no studies have explicitly focused on genes associated with megalencephaly. Here, we investigate 16 candidate genes that have been linked to megalencephaly to determine if: (1) megalencephaly-associated genes evolved under positive selection across primates; and (2) selection pressure on megalencephaly-associated genes is linked to primate brain size. We found evidence for positive selection for only one gene, OFD1, with 1.8% of the sites estimated to have dN/dS values greater than 1; however, we did not detect a relationship between selection pressure on this gene and brain size across species, suggesting that selection for changes to non-brain size traits drove evolutionary changes to this gene. In fact, our primary analyses did not identify significant associations between selection pressure and brain size for any candidate genes. While we did detect positive associations for two genes (GPC3 and TBC1D7) when two phyletic dwarfs (i.e., species that underwent recent evolutionary decreases in brain size) were excluded, these associations did not withstand FDR correction. Overall, these results suggest that sequence alterations to megalencephaly-associated genes may have played little to no role in primate brain size evolution, possibly due to the highly pleiotropic effects of these genes. Future comparative studies of gene expression levels may provide further insights. This study enhances our understanding of the genetic underpinnings of brain size evolution in primates and identifies candidate genes that merit further exploration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".