Abstract
BackgroundEpigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner.ResultsWe analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates.ConclusionCompared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.
Highlights
Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases
For DNA methylation (DNAm)-gestational diabetes mellitus (GDM) association tests, the coefficient for the interaction effect βkc based on β-value could be interpreted as change in methylation proportion in cell type k among individuals exposed to GDM compared to those not exposed to GDM, at probe c ; For DNAm-age 3 body mass index (BMI) z-score association tests, βkc could be interpreted as change in methylation proportion in cell type k associated with one unit increase in the BMI z-score, at probe c
(upstream regulatory region locus cg03908391, unmethylated among individuals exposed to GDM but completely methylated among those not exposed to GDM in CD8+ T-cell) [38], GRB10 [39], etc
Summary
Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. Various statistical approaches have been proposed to infer cell type composition in samples and correct for cell type heterogeneity in EWAS [8,9,10,11,12] Many of these approaches consider that the observed or estimated cell type proportions may be associated with the phenotype, but not with differences in DNAm (at most of the measured positions) [8]. A new algorithm called CellDMC was developed and validated by Zheng et al [4] to enable detection of cell type-specific differential DNA methylation by identifying interactions between the phenotype and cell type proportions in samples. The benefits of this approach in human samples containing cell type mixtures may worth extensive exploration
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