Biology subjects

Merrill, S. M.

Publications and source records attributed to Merrill, S. M..

4 recordsLinked to original sources

bioRxiv2025

RAMEN: Dissecting individual, additive and interactive gene-environment contributions to DNA methylome variability in cord blood

DNA methylation (DNAme) is the most commonly studied epigenetic mark in human populations. DNAme has gained attention in the Developmental Origins of Health and Disease field due to its gene expression regulation and potential long-term stability. Genetic variation and environmental exposures are amongst the main factors influencing inter-individual DNAme variability. However, the proportion and genomic distribution of their individual, additive and interactive effects on the DNA methylome remains unclear. Here, we introduce RAMEN, a Findable, Accessible, Interoperable, and Reusable (FAIR) framework tailored for DNAme microarrays. Using machine learning and statistical techniques, RAMEN models and dissects gene-environment contributions to genome-wide Variably Methylated Regions (VMRs), while controlling for spurious associations. To comprehensively test the power of RAMEN, we analyzed and characterized VMRs from cord blood samples from two independent cohorts (CHILD and PREDO; overall n=1,662). We identified genetics as a consistent key contributor to DNAme variability, usually in additive and interactive combinations with the environment, with genetic terms explaining the largest proportion of DNAme variance, compared to environmental and interaction terms. Operationalizing RAMEN as an R package to conduct scalable genome-exposome contribution analyses, our results highlighted the importance of genetic variation in sculpting DNAme patterns in early life.

bioinformatics↗

bioRxiv2025

Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method

Prospective birth cohorts offer the potential to interrogate the relation between early life environment and embedded biological processes such as DNA methylation (DNAme). These association studies are frequently conducted in the context of blood, a heterogeneous tissue composed of diverse cell types. Accounting for this cellular heterogeneity across samples is essential, as it is a main contributor to inter-individual DNAme variation. Integrated blood cell deconvolution of pediatric and longitudinal birth cohorts poses a major challenge, as existing methods fail to account for the distinct cell population shift between birth and adolescence. In this paper, we critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. The optimized algorithm, CellsPickMe, integrates cord and adult references and picks DNAme features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe, this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course.

bioinformatics↗

bioRxiv2024

Considerations for Cell Type Heterogeneity in Pediatric Salivary DNA Methylation Analyses: Comparison of Reference Panels & Stratification by Estimated Cell TypeProportion

Saliva is widely used in biomedical population research, including epigenetic analyses to investigate gene-environment interplay and identify biomarkers. Its minimally invasive collection procedure makes it ideal for studies in pediatric populations. Saliva is a heterogenous tissue composed of immune and buccal epithelial cells (BEC). Amongst the many epigenetic marks, DNA methylation (DNAm) is the most studied in human populations. Given DNAms integral role to cellular differentiation and maintenance, DNAm profiles are often highly cell type (CT)-specific and CT composition can drive salivary DNAm associations with environments or health as well as epigenetic age acceleration (EAA), discrepancy between chronological age and biological age derived from DNAm. To address this, reference-based CT deconvolution and statistically adjustment with estimated CT in DNAm analyses have become a common practice. However, it remains unclear how different CT reference panels--constructed from adult versus pediatric samples--affect DNAm results. Additionally, whether DNAm and EAA associations in saliva primarily originate from immune cells or BECs, or if they persist across saliva samples despite varying CT proportions, has yet to be examined. The current study used salivary DNAm samples obtained from 529 children (mean age=7.26 years, SD=0.26 years) in a community-based cohort, the Family Life Project. Our results highlighted the impact of estimated CT discrepancies across child and adult reference panels on DNAm associations. Upon stratifying the salivary DNAm samples into three subsamples--primarily BECs, primarily immune cells, and an approximately equal mix of both, we found significantly different EAAs across stratified samples when CT proportions were not accounted for. In both the context of DNAm and EAA associations, we detected stronger effects of cotinine concentrations, a tobacco smoke-exposure biomarker, in the subsample with primarily immune cells. We discussed the implications of our findings for the interpretation and replication of epigenetic research involving pediatric saliva samples.

bioinformatics↗

bioRxiv2023

Cross-Tissue Specificity of Pediatric DNA Methylation Associated with Cumulative Family Adversity

BackgroundCumulative family adversity (cumulative FA), characterized by co-occurring stressors in a family context, may be biologically embedded through DNA methylation (DNAm) and contribute to later health outcomes. Materials & MethodsWe compared epigenome-wide DNAm associated with cumulative FA in buccal epithelial cells (BECs; n=218) and peripheral blood mononuclear cells (PBMCs; n=51) from 7-13-year-old children in Canada, accounting for sex, age, predicted cell-type proportion, and genetic ancestry. ResultsHigher levels of cumulative FA were associated with DNAm at seven sites, primarily in stress- and immune-related genes, only in PBMCs. Negative mother-child interaction contributed to this association. ConclusionsThe findings of this study suggested that PBMC DNAm can be used as a marker for biological embedding of cumulative FA.

genomics↗