Children differ substantially in their sensitivity to the quality of their environment. Some are more sensitive and more likely to develop Post-Traumatic Stress Disorder (PTSD) in response to Childhood Adversities (CAs), but might also benefit more from Positive Home Experiences (PHE). The aim of this study is to investigate the role of Environmental Sensitivity (ES), CAs and PHEs in PTSD development in children and adolescents. Data was collected from N = 2,569 children/adolescents. PTSD symptoms, CAs, PHEs and ES were assessed with self-report measures. We found that higher ES and CAs emerged as risk factors for PTSD development whereas higher levels of PHEs protected against PTSD. ES moderated the effects of CAs (β = 1.08, p < .001) on PTSD symptoms in the total sample. This moderating effect was more pronounced in girls, suggesting that highly sensitive girls with high childhood adversities were more likely to have higher PTSD symptoms than girls with low levels of sensitivity (β = 1.09, p < .001). In conclusion, Environmental Sensitivity played an important role as a risk factor for PTSD and as a moderating factor that accentuated the main effects of childhood adversities, particularly in girls.

According to environmental sensitivity models, children vary in responsivity to parenting. However, different models propose different patterns, with responsivity to primarily: (1) adverse parenting (adverse sensitive); or (2) supportive parenting (vantage sensitive); or (3) to both (differentially susceptible). This preregistered study tested whether these three responsivity patterns coexist. We used intensive longitudinal data of Dutch adolescents (N = 256, Mage = 14.8, 72% female) who bi-weekly reported on adverse and supportive parenting and their psychological functioning (tmean = 17.7, tmax = 26). Dynamic Structural Equation Models (DSEM) indeed revealed differential parenting effects. As hypothesized, we found that all three responsivity patterns coexisted in our sample: 5% were adverse sensitive, 3% vantage sensitive, and 26% differentially susceptible. No adolescent appeared unsusceptible, however. Instead, we labeled 28% as unperceptive, because they did not perceive any changes in parenting and scored lower on trait environmental sensitivity than others. Furthermore, unexpected patterns emerged, with 37% responding contrary to parenting theories (e.g., decreased psychological functioning after more parental support). Sensitivity analyses with concurrent effects and parent-reported parenting were performed. Overall, findings indicate that theorized responsivity-to-parenting patterns might coexist in the population, and that there are other, previously undetected patterns that go beyond environmental sensitivity models.

Children differ in their environmental sensitivity (ES), which can be measured observationally or by self-report questionnaire. A parent-report scale represents an important tool for investigating ES in younger children but has to be psychometrically robust and valid. In the current multistudy, we validated the parent-report version of the Highly Sensitive Child (HSC-PR) scale in Italian children, evaluating its factorial structure (Study 1, N = 1,857, 6.2 years, age range: 2.6–14 years) through a multigroup Confirmatory Factory Analysis in preschoolers (n = 1,066, 4.2 years) and school-age children (n = 791, 8.8 years). We then investigated the HSC-PR relationship with established temperament traits (Study 2, N = 327, 4.3 years), before exploring whether the scale moderates the effects of parenting stress on children’s emotion regulation (Study 3, N = 112, 6.5 years). We found support for a bi-factor structure in both groups, though in preschoolers minor adaptations were suggested for one item. Importantly, the HSC-PR did not fully overlap with common temperament traits and moderated the effects of parenting stress on children emotion regulation. To conclude, the HSC-PR performs well and appears to capture ES in children.

The interplay of parenting and environmental sensitivity on children’s behavioral adjustment during, and immediately after, the COVID-19 lockdown restrictions was investigated in two longitudinal studies involving Italian preschoolers (Study 1, N = 72; 43% girls, Myears = 3.82(1.38)) and primary school children (Study 2, N = 94; 55% girls, Myears = 9.08(0.56)). Data were collected before and during the first-wave lockdown (Studies 1 and 2) and one month later (Study 1). Parental stress and parent–child closeness were measured. Markers of environmental sensitivity in children were temperamental fearfulness and Sensory Processing Sensitivity. Results showed little change in externalizing and internalizing behaviors over time, but differences emerged when considering parenting and children’s environmental sensitivity. In preschoolers, greater parenting stress was related to a stronger increase in internalizing and externalizing behaviors, with children high in fearful temperament showing a more marked decrease in externalizing behaviors when parenting stress was low. In school-aged children, parent–child closeness emerged as a protective factor for internalizing and externalizing behaviors during COVID-19, with children high in Sensory Processing Sensitivity showing a marked decrease in internalizing behaviors when closeness was high. Implications for developmental theory and practice in times of pandemic are discussed.

Children differ in their response to environmental exposures, with some being more sensitive to contextual factors than others. According to theory, such variability is the result of individual differences in neurobiological sensitivity to environmental features, with some individuals generally more affected by both negative and/or positive experiences. In this exploratory study we tested whether left and right amygdala and hippocampus volumes (corrected for total brain size) account for individual differences in response to environmental influences in a sample of 62 boys. Cumulative general environmental quality, ranging from low to high, was measured across the first 9 years and child behavior was reported by teachers when boys were 12–13 years old. According to analyses, only the left amygdala volume – not any of the other brain volumes – emerged as an important brain region for sensitivity to positive environmental aspects. Boys with a larger left amygdala benefited significantly more from higher environmental quality than boys with a smaller left amygdala whilst not being more vulnerable to lower quality. Besides providing preliminary evidence for differences in environmental sensitivity due to brain structure, the results also point to the left amygdala as having a specific role regarding the response to environmental influences.

The aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

Childhood maltreatment robustly predicts adolescent externalizing behaviors (EB; e.g., violence, delinquency, substance use) and may crystalize patterns of EB by influencing sensitivity to the social environment (e.g., parenting, friendships). In a nationally representative sample of 9,421 adolescents, we modeled latent growth curves of EB from age 13 to 32 years. Next, we explored whether maltreated youth differed from nonmaltreated youth in their sensitivity to parental closeness, friendship involvement, and polymorphisms from dopamine genes linked to EB (dopamine receptors D2 and D4, dopamine transporter). Overall, maltreated youth had significantly higher levels of EB across adolescence and adulthood; however, maltreated and nonmaltreated youth showed similar patterns of EB change over time: violent behavior decreased in adolescence before stabilizing in adulthood, whereas nonviolent delinquency and substance use increased in adolescence before decreasing in the transition to adulthood. Maltreatment reduced sensitivity to parental closeness and friendship involvement, although patterns varied based on type of EB outcome. Finally, none of the environmental effects on EB were significantly moderated by the dopamine polygenic risk score after accounting for multiple testing. These findings underline the enduring effects of early maltreatment and implicate that maltreatment may contribute to long-term risk for EB by influencing children's sensitivity to social relationship factors in adolescence.

In order to assess the extent of genotype by environment interactions (G×E) and environmental sensitivity in sheep farm systems, environmental factors must be identified and quantified, after which the relationship with the traits(s) of interest can be investigated. The objectives of this study were to develop a farm environment (FE) scale, using a canonical correlation analysis, which could then be used in linear reaction norm models. Fine-scale farm survey data, collected from a sample of 39 Texel flocks across the United Kingdom, was combined with information available at the national level. The farm survey data included information on flock size and concentrate feed use. National data included flock performance averages for 21-week-old weight (21WT), ultrasound back-fat (UFD) and muscle (UMD) depths, as well as regional climatic data. The FE scale developed was then combined with 181 555 (21WT), 175 399 (UMD) and 175 279 (UFD) records from lambs born between 1990 and 2011, on 494 different Texel flocks, to predict reaction norms for sires used within the population. A range of sire sensitivities estimated across the FE scale confirmed the presence of genetic variability as both ‘plastic’ and ‘robust’ genotypes were observed. Variations in heritability estimates were also observed indicating that the rate genetic progress was dependent on the environment. Overall, the techniques and approaches used in this study have proven to be useful in defining sheep FEs. The results observed for 21WT, UMD and UFD, using the reaction norm models, indicate that in order to improve genetic gain and flock efficiency, future genetic evaluations would benefit by accounting for the G×E observed.

This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.

A Bayesian procedure was used to estimate linear reaction norms (i.e. individual G × E plots) on 297 518 litter size records of 121 104 sows, daughters of 2040 sires, recorded on 144 farms in North and Latin America, Europe, Asia and Australia. The method allowed for simultaneous estimation of all parameters involved. The analysis was carried out on three subsets, comprising (i) parity 1 records of 33 641 sows of line B, (ii) all parity records of 52 120 sows of line B and (iii) all parity records of 121 104 sows of lines A, B and A × B. Estimated heritabilities ranged from 0.09 to 0.10 (smallest to largest subset) for the intercept of the reaction norms, and were 0.15, 0.08 and 0.02 (ditto) for the slope. Estimated genetic correlations between intercept and slope were −0.09, +0.26 and +0.69 (ditto). The three subsets therefore showed a progressively lower genetic component to environmental sensitivity, and progressively less re-ranking of genotypes across the environmental (herd–year–season) range. In a genetic evaluation that does not include reaction norms in the statistical model, part of the G × E effect remains confounded with the additive genetic effect, which may lead to errors in the estimates of the additive genetic effect; the reaction norms model removes this confounding. The intercept estimates from the largest data subset show correlations with litter size estimated breeding values (EBV) from routine genetic evaluation (without reaction norms included) of 0.78 to 0.85 for sows with one to seven litter records, and 0.75 for sires. Hence, including reaction norms in genetic evaluation would increase the reliability of the EBV of young selection candidates without own performance or progeny data by considerably more than 100 × (1/0.75−1) = 33%. Reaction norm slope estimates turn out to be very demanding statistics; environmental sensitivity must therefore be classified as a ‘hard-to-measure’ trait.