Integrative epigenetics and transcriptomics identify aging genes in human blood.

Transcriptomic age-related gene expression changes show limited replicability across populations compared to epigenetic changes.

• Previously identified age-related gene expression changes have shown limited replicability across diverse populations.
• The study motivates integration of epigenetic and transcriptomic data specifically to overcome this replicability limitation.
• Epigenome-wide studies have identified a large number of genomic regions that consistently exhibit methylation changes with aging across diverse populations.

The integrative multi-omic approach identified genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood.

• The approach leverages high-resolution multi-omic data for integrative analysis of epigenetic and transcriptomic age-related changes.
• The method identifies genomic regions associated with both epigenetic and transcriptomic age-dependent changes simultaneously.
• The resulting gene set is referred to as 'multi-omic aging genes in blood.'

Multi-omic aging genes in blood are enriched for adaptive immune functions.

• Functional enrichment analysis revealed that multi-omic aging genes are enriched for adaptive immune functions.
• This biological enrichment is a characteristic feature distinguishing multi-omic aging genes from those identified by single-omic approaches.
• Blood was the tissue studied, which is consistent with adaptive immune cell composition changes with aging.

Multi-omic aging genes replicate more robustly across diverse populations compared to genes identified using epigenetic or transcriptomic data alone.

• Replication was assessed across diverse populations.
• Genes identified using only epigenetic or only transcriptomic data showed less robust replication.
• The integrative approach improved cross-population replicability of aging gene identification.

Multi-omic aging genes are more strongly associated with aging-related outcomes than genes identified using single-omic data alone.

• Associations with aging-related outcomes were stronger for multi-omic aging genes compared to epigenetic-only or transcriptomic-only gene sets.
• This finding supports the added value of integrating both data modalities for identifying functionally relevant aging genes.
• The stronger associations suggest these genes capture more biologically meaningful aging signals.

Multi-omic aging genes are proposed as targets for epigenetic editing to facilitate cellular rejuvenation.

• The authors suggest these genes 'may serve as targets for epigenetic editing to facilitate cellular rejuvenation.'
• The functional consequences of age-related methylation changes at these loci are linked to transcriptomic changes, providing a mechanistic rationale for targeting them.
• This application is framed as a translational implication of the multi-omic aging gene identification.