Beyond Classical Inheritance: The Influence of Maternal Genotype upon Child's Brain Morphology and Behavior
Bibliographic record
Abstract
Genetic variance has been associated with variations in brain morphology, cognition, behavior, and disease risk. One well studied example of how common genetic variance is associated with brain morphology is the serotonin transporter gene polymorphism within the promoter region (5-HTTLPR). Because serotonin is a key neurotrophic factor during brain development, genetically determined variations in serotonin activity during maturation, in particular during early prenatal development, may underlie the observed association. However, the intrauterine microenvironment is not only determined by the child's, but also the mother's genotype. Therefore, we hypothesized that maternal 5-HTTLPR genotype influences the child's brain development beyond direct inheritance. To test this hypothesis, we investigated 76 children who were all heterozygous for the 5-HTTLPR (sl) and who had mothers who were either homozygous for the long (ll) or the short allele (ss). Using MRI, we assessed brain morphology as a function of maternal genotype. Gray matter density of the somatosensory cortex was found to be greater in children of ss mothers compared with children of ll mothers. Behavioral assessment showed that fine motor task performance was altered in children of ll mothers and the degree of this behavioral effect correlated with somatosensory cortex density across individuals. Our findings provide initial evidence that maternal genotype can affect the child's phenotype beyond effects of classical inheritance. Our observation appears to be explained by intrauterine environmental differences or by differences in maternal behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".