Age-related trajectories of DNA methylation network markers: a parenclitic network approach to a family-based cohort of patients with Down Syndrome

Despite the cause of Down Syndrome (DS) is well established, the underlying molecular mechanisms that contribute to the syndrome and to the phenotype of accelerated ageing remain largely unknown. DNA methylation profiles are largely altered in DS but it remains unclear how different methylation regions and probes are structured into a network of interactions. We develop and generalize the Parenclitic Networks approach that enables to find correlations between distant CpG probes (and not pronounced as stand-alone biomarkers) and quantifies hidden network changes in DNA methylation. DS and a family-based cohort (including healthy siblings and mothers of persons with DS) is used as a case study. Following this approach, we constructed parenclitic networks and obtained different signatures informative for (i) the differences between individuals with DS from healthy individuals; (ii) differences between young and old healthy individuals and determining the place of DS individuals on this scale; (iii) differences between DS individuals from their age-matched siblings, and (iv) the difference between DS and the adult population (their mothers). Conducted Gene Ontology analysis showed that the CpG network approach is more powerful than the single CpG approach in identifying biological processes related to DS phenotype, like those involved in central nervous system, skeletal muscles, disorders in carbohydrate metabolism, cardiopathology, and oncogenes. Our open-source software implementation is accessible to all researchers. The software includes a complete workflow to construct Parenclitic Networks with any machine learning algorithm as a kernel to build edges. We anticipate a broad applicability of the approach to other diseases.