A network-based meta-analysis for characterizing the genetic landscape of human aging.

Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website ( https://gemex.eurac.edu/bioinf/age/ ).