Differential Methylation at Novel Putative Imprinting Control Regions on Chromosome 20 Associated with Childhood Obesity
Everly, C. F.; Allen, T. K.; Skaar, D. A.; Corliss, B. A.; Jima, D. D.; Maguire, R. L.; Jadallah, H.; Jirtle, R. L.; Murphy, S. K.; Tzeng, J.-Y.; Hoyo, C.
Show abstract
DNA methylation may link early-life exposures to later obesity risk; however, most studies examine individual CpG sites using commercial arrays that cover <5% of the genome and often overlook biologically-relevant regulatory regions. Imprinting control regions (ICRs) regulate parent-of-origin gene expression and are enriched for growth and metabolic genes, representing strong candidates for developmental programming of obesity. In 586 participants of the Newborn Epigenetics STudy (NEST), we leveraged the novel Human Imprintome Array to measure methylation at >1,000 characterized and putative ICRs genome-wide in umbilical cord blood. We applied principal component and kernel machine regression to identify ICR methylation at birth associated with sustained childhood obesity, defined as BMI [≥]95th percentile during most of childhood. Sustained childhood obesity was associated with differential methylation at seven ICRs in cord blood, with 2-10% mean differences between obesity and normal weight groups, including novel regions localized to chromosome 20q11.1-20q11.21 (ICR_1182, ICR_1179, ICR_1181, ICR_1177, ICR_1180, and ICR_1165), mapping to or near CDC27P4, LINC01597, DUX4L37, and DUX4L34, and ICR_927 mapping to previously characterized ZNF597/NAA60. Most ICRs overlapped multiple transcriptional regulator binding sites. Associations persisted in peripheral blood collected in later childhood at ages 8-16 years. Among offspring of mothers with pre-pregnancy obesity, an additional 50 associations were identified. Sex-stratified analyses revealed no further insights. These findings identify novel ICRs-particularly on chromosome 20q-as potential early-life epigenetic biomarkers of childhood obesity. If replicated, these stable ICR methylation patterns may identify children at risk for obesity at birth, informing targeted prevention before obesity develops and guiding novel therapeutic strategies.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Twin pair analysis uncovers novel links between DNA methylation, mitochondrial DNA quantity and obesity 96%
- Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes 95%
- Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic loci and establishes causal relationships between educational attainment and healthy eating 94%
Similar papers in this journal
- Characterization of the genetic architecture of BMI in infancy and early childhood reveals age-specific effects and implicates pathways involved in Mendelian obesity 95%
- A long-non-coding RNA, LINC00473, confers the human adipose tissue thermogenic phenotype through enhanced cAMP responsiveness. 93%
- A Western Dietary Pattern during Pregnancy is Associated with Neurodevelopmental Disorders in Childhood and Adolescence 92%
Similar papers in this journal
- Nicotinamide riboside improves muscle mitochondrial biogenesis, satellite cell differentiation and gut microbiota composition in a twin study 92%
- Transcriptional regulation of adipocyte lipolysis by IRF2BP2 91%
- Long-term ketogenic diet causes hyperlipidemia, liver dysfunction, and glucose intolerance from impaired insulin trafficking and secretion in mice 90%
Similar papers in this journal
- 3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity 95%
- Serum proteomic profiling of physical activity reveals CD300LG as a novel exerkine with a potential causal link to glucose homeostasis 93%
- Robust single nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.