Epigenetic Patterns Modulate the Connection Between Developmental Dynamics of Parenting and Offspring Psychosocial Adjustment

Abstract

This study attempted to establish and quantify the connections between parenting, offspring psychosocial adjustment, and the epigenome. The participants, 35 African American young adults (19 females and 16 males; age = 17-29.5 years), represented a subsample of a 3-wave longitudinal 15-year study on the developmental trajectories of low-income urban mother-offspring dyads. Mothers were assessed on their perceptions of maternal stress at each wave. Offspring were assessed on their perceptions of maternal parenting at each wave and on their adaptive and maladaptive behavior at the last wave. Genome-wide DNA methylation in peripheral T lymphocytes at the third wave was assayed using Methyl Binding Domain(MBD) sequencing. Statistically significant associations were identified between the change in offspring's perception of parenting from middle childhood to adulthood and the DNA methylation in offspring's adult genomes. Specifically, the slope of perceived parental rejection across the 3 time points was related to an increase in methylation, or a potential downregulation, of 565 genes thought to be involved in the control of a broad spectrum of biological functions generally related to cellular signaling. A subset of these epigenetic marks, clustered in 23 genes, some of which participate in the development and functioning of the CNS, were in turn associated with psychosocial adjustment as captured by interpersonal relationships and emotional self-evaluation. This appears to be one of the first investigations of the modulating role of the methylome in associations between developmental dynamics of parenting throughout the formative years of child and adolescent development and psychosocial adjustment in adulthood.

Figure 1

Linear regression model that relates the score of dynamic change in perceived parental rejection (PARQ-slope) and the mean methylation levels of 818 parenting-associated markers, or 300bp MBD-seq fragments for the entire sample of 35 individuals (A) and for 32 samples, with the exclusion of extreme cases (B). Figure 1A shows that the sample contains three extreme cases. One case had an unusually high PARQ-slope value (4.82) with average DNA methylation compared to the rest of the sample (i.e., exerting a leverage on the regression line). Two cases showed discrepancies as reflected in high PARQ-slope values (5.12 and 4.45) and high DNA methylation. A set of diagnostic analyses of the regression of mean methylation on PARQ-slope values and the covariates was performed to identify any concerns with these extreme cases. Results showed (1) no concerning leverage of mean methylation on the fitted values as indicated by hat values (values were 0.48, 0.46, and 0.46; mean hat value was 0.17); (2) no unusual large or small residuals as indicated by most studentized residuals within the ± 2 range (largest studentized residual was 2.64 with a Bonferroni-adjusted p-value of .47); (3) no major influence on the regression coefficients of PARQ-slope as indicated by a plot of residuals against leverage (Cook's d values were 0.87, 0.54, and 0.84). Given the small sample size, we have tested and evaluated other types of robust regression models using high breakdown point (M- or MM-) estimators as these models tend to be less vulnerable to unusual data. Estimators such as “least trimmed squares” and “least median of squares” are alternatives to ordinary least squares regression that account better for the effect of extreme values. However, these methods led to problems of convergence in many of the regression models we tested. In robust regression using M-estimators, Wald-type inference of the significance of coefficients typically requires larger samples—due to the unreliable asymptotic covariance matrix in small samples—than the data available in this study. Thus, although we were aware of the potential influence of these cases on the regression, we decided to utilize ordinary least squares regression but cautiously interpret the results. Figure 1B shows that the association between the PARQ-slope and the mean of ME-markers is high and significant within the sample after removing the three extreme cases. The y-axis (mean DNA-methylation) has been rescaled to better illustrate the linear interrelation.

Figure 2

Linear regression model that describes the relationship between the Personal Adjustment composite score and the average methylation level of 38 methylation markers that were associated with both Personal Adjustment and the PARQ-slope. Relationships were established for the entire studied sample (A) and for the sample without any cases with extremely high methylation levels (B). Both plots show high negative associations between the variables.

Figure 3

The tissue (blood) specific co-expression network for the 23 genes associated with both the PARQ-slope and the BASC-CV Personal Adjustment, constructed based on Genotype-Tissue Expression (GTEx) project database (GTExConsortium, 2013) via a WebQTL resource (http://www.genenetwork.org). Interactions for absolute correlations above .50 are shown; Pearson pairwise correlations are represented. The nodes contain the symbols of genes. Notably, 12 of these 23 genes are known to be expressed in blood cells, and 9 of 12 active genes showed a high co-variability in their expression levels among individuals.