| Title: | Genetic associations for two biological age measures point to distinct aging phenotypes |
| Journal: | Aging Cell |
| Published: | 26 May 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34038024/ |
| DOI: | https://doi.org/10.1111/acel.13376 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7359547 |
| Citations: | 54 (36 in last 2 years) as of 8 Aug 2024 |
Publication 4898
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Biological age measures outperform chronological age in predicting various aging outcomes, yet little is known regarding genetic predisposition. We performed genome-wide association scans of two age-adjusted biological age measures (PhenoAgeAcceleration and BioAgeAcceleration), estimated from clinical biochemistry markers (Levine et al., 2018; Levine, 2013) in European-descent participants from UK Biobank. The strongest signals were found in the APOE gene, tagged by the two major protein-coding SNPs, PhenoAgeAccel-rs429358 (APOE e4 determinant) (p = 1.50 × 10-72 ); BioAgeAccel-rs7412 (APOE e2 determinant) (p = 3.16 × 10-60 ). Interestingly, we observed inverse APOE e2 and e4 associations and unique pathway enrichments when comparing the two biological age measures. Genes associated with BioAgeAccel were enriched in lipid related pathways, while genes associated with PhenoAgeAccel showed enrichment for immune system, cell function, and carbohydrate homeostasis pathways, suggesting the two measures capture different aging domains. Our study reaffirms that aging patterns are heterogeneous across individuals, and the manner in which a person ages may be partly attributed to genetic predisposition.</p>
5 Authors
- Chia-Ling Kuo
- Luke C. Pilling
- Zuyun Liu
- Janice L. Atkins
- Morgan E. Levine
Enabling scientific discoveries that improve human health