Identification of Individual Differences in Responsivity to Prenatal Adversity using the Biological Signature related to Neighborhood Disadvantage Exposure
Bibliographic record
Abstract
Genes are fundamental units of heredity that encode genetic information and serve as the basis for the development and functioning of all living organisms.Variations within the genetic code, as well as their interactions with environmental factors, underpin the diverse array of individual differences that ultimately dictate our susceptibility to various diseases.Genome-wide association studies (GWAS) have increasingly generated informative genetic risk variants for mental health-related traits (Wray et al. 2012).These correlation studies highlight the complex and intricate polygenic nature of many psychiatric disorders.A standard GWAS, however, falls short of properly capturing the mechanisms that can control gene expression.In particular, the influence of epigenetic factors, which have a significant impact on gene regulation, is disregarded.Consequently, employing a multi-omics approach that integrates data from various sources, such as genomics, transcriptomics, and epigenomics, can build more accurate and informative risk assessment models.Using a Neighbourhood Disadvantage (ND) EWAS (Reuben et al. 2020), we constructed an expression-based polygenic risk score (ePRS) weighted by Nacc (Nucleus Accumbens) tissue expression (Silveira et al. 2017a) and a methylation score for children using 3 different cohorts (GUSTO, ALSPAC, BIBO).We also calculated a prenatal adversity score by summing various sources of hardship experienced during the prenatal period to inform on the impact of early life environmental stressors.Multiple linear regression models were constructed to investigate the influence of the ePRS, methylation scores (M) and prenatal adversity (A) on socioemotional outcomes.We observed that variability was best explained by a multi-omics model (M + ePRS x A + ePRS +A) in the GUSTO cohort at 7 years.Using simple slope analysis, we showed that children at age 7 years old who have higher ePRS and who are exposed to high prenatal adversity have heightened emotional and behavioural problems in comparison to children who have low exposure to adversity.Meanwhile, we observed that children with a higher.ePRS score in the ALSPAC cohort at 11 years has increased externalizing behavioural problems when exposed.To high levels of prenatal adversity.We also observed that children in the BIBO cohort who have high ePRS and who are not exposed to adversity have lower internalizing scores than children with lower ePRS.We also investigated the biological background of the ePRS exploring its gene network, biological pathways and tissue expression.Our enrichment analysis revealed that the Neighbourhood Disadvantage (ND) gene network is prominently expressed in fetal development and young adulthood.The genes in the ND network are involved in the regulation of the synaptic vesicle cycle, synapse organization and nervous system development.These results highlight an important gene network involved in individual differences in neurodevelopmental processes, as well as vulnerability to behavioural problems in children.Overall, this thesis aimed to explore the complex interplay between genes and the environment and their impact on childhood socioemotional problems, focusing on the role of variability in genetic and environmental factors in influencing susceptibility to these socioemotional issues. RésuméLes gènes sont des unités fondamentales de l'hérédité qui codent l'information génétique et servent de base au développement et au fonctionnement de tous les organismes vivants.Les variations au sein du code génétique, ainsi que leurs interactions avec les facteurs environnementaux, sont à la base de toute une série de différences individuelles qui déterminent en fin de compte notre susceptibilité à diverses maladies.Les études d'association à l'échelle du génome (GWAS) ont de plus en plus généré des variantes de risque génétique informatives pour les traits liés à la santé mentale (1).Ces études de corrélation mettent en évidence la nature polygénique complexe de nombreux troubles psychiatriques.Toutefois, une étude d'association pangénomique standard ne parvient pas à saisir correctement les mécanismes qui peuvent contrôler l'expression des gènes.En particulier, l'influence des facteurs épigénétiques, qui ont un impact significatif sur la régulation des gènes, n'est pas prise en compte.Par conséquent, l'utilisation d'une approche multi-omique qui intègre des données provenant de différentes sources, telles que la génomique, la transcriptomique et l'épigénomique, peut permettre d'élaborer des modèles d'évaluation des risques plus précis et plus informatifs.À l'aide de l'EWAS (Neighbourhood Disadvantage) (2), nous avons construit un score de risque polygénique basé sur l'expression (ePRS) pondéré par l'expression du tissu Nacc (Nucleus Accumbens) (3) et un score de méthylation pour les enfants en utilisant 3 cohortes différentes (GUSTO, ALSPAC, BIBO).Nous avons également calculé un score d'adversité prénatale en additionnant diverses sources de difficultés rencontrées pendant la période prénatale afin d'obtenir des informations sur l'impact des facteurs de stress environnementaux au début de la vie.Des modèles de régression linéaire multiple ont été élaborés pour étudier l'influence du ePRS, des scores de méthylation (M) et de l'adversité prénatale (A) sur les résultats socio-émotionnels dans trois cohortes indépendantes à différents âges (GUSTO,
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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 | 0.000 | 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".