Maternal serotonin transporter genotype affects risk for ASD with exposure to prenatal stress
Bibliographic record
Abstract
Stress exposure during gestation is implicated in several neuropsychiatric conditions, including autism spectrum disorder (ASD). Previous research showed that prenatal stress increases risk for ASD with peak exposure during the end of the second and the beginning of the third trimester. However, exposures to prenatal stress do not always result in ASD, suggesting that other factors may interact with environmental stressors to increase ASD risk. The present study examined a maternal genetic variation in the promoter region of the serotonin transporter gene (5-HTTLPR) affecting stress tolerance and its interaction with the effect of environmental stressors on risk for ASD. Two independent cohorts of mothers of ASD children recruited by the University of Missouri and Queen's University were surveyed regarding the prenatal environment and genotyping on 5-HTTLPR was performed to explore this relationship. In both samples, mothers of children with ASD carrying the stress susceptible short allele variant of 5-HTTLPR experienced a greater number of stressors and greater stress severity when compared to mothers carrying the long allele variant. The temporal peak of stressors during gestation in these mothers was consistent with previous findings. Additionally, increased exposure to prenatal stress was not reported in the pregnancies of typically developing siblings from the same mothers, regardless of maternal genotype, suggesting against the possibility that the short allele might increase the recall of stress during pregnancy. The present study provides further evidence of a specific maternal polymorphism that may affect the risk for ASD with exposure to prenatal stress. Autism Res 2016, 9: 1151-1160. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".