Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.08.19.456979

Socially stratified epigenetic profiles are associated with cognitive functioning in children and adolescents

Raffington, L. A. S.·Tanksley, P.·Sabhlok, A.·Vinnik, L.·Mallard, T. T.·King, L.·Goosby, B.·Harden, K. P.·Tucker-Drob, E. M.
View original source ↗Export metadata ↓

Abstract

Childrens cognitive functioning and educational performance are socially stratified. Social inequality, including classism and racism, may operate partly via epigenetic mechanisms that modulate neurocognitive development. Following preregistered analyses of data from 1,183 8-to 19-year-olds from the Texas Twin Project, we examined whether salivary DNA-methylation measures of inflammation (DNAm-CRP), cognitive functioning (Epigenetic-g), and pace of biological aging (DunedinPoAm) are socially stratified and associated with performance on tests of cognitive functions. We find that children growing up in more disadvantaged families and neighborhoods and children from marginalized racial/ethnic groups exhibit DNA-methylation profiles associated with higher chronic inflammation, lower cognitive functioning, and faster pace of biological aging. These salivary DNA-methylation profiles were associated with processing speed, general executive function, perceptual reasoning, verbal comprehension, reading, and math. Given that the DNA-methylation measures we examined were originally developed in adults, our results suggest that social inequalities may produce in children molecular signatures that, when observed in adults, are associated with chronic inflammation, advanced aging, and reduced cognitive function. Salivary DNA-methylation profiles might be useful as a surrogate endpoint in assessing the effectiveness of psychological and economic interventions that aim to reduce negative effects of childhood social inequality on lifespan development. Significance StatementChildrens cognitive functioning differs by dimensions of social inequality, such as class and race. Epigenetic mechanisms that regulate gene expression might be critically involved in the biological embedding of environmental privilege and adversity. We find that children growing up in more disadvantaged families and neighborhoods and from marginalized racial/ethnic groups exhibit higher chronic inflammation, lower cognitive functioning, and a faster pace of biological aging, as indicated by novel salivary DNA-methylation measures. These DNA-methylation measures of higher inflammation, lower cognitive functioning, and a faster pace of biological aging were, in turn, associated with performance on multiple cognitive tests. DNA-methylation measures might be useful as a surrogate endpoint in evaluation of programs to address the childhood social determinants of lifelong cognitive disparities.

Source connections

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Raffington, L. A. S., Tanksley, P., Sabhlok, A., Vinnik, L., Mallard, T. T., King, L., Goosby, B., Harden, K. P., Tucker-Drob, E. M.. 2021-08-23. Socially stratified epigenetic profiles are associated with cognitive functioning in children and adolescents. https://doi.org/10.1101/2021.08.19.456979

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗
bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗
bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗