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. 2021 Jun;20(6):e13376.
doi: 10.1111/acel.13376. Epub 2021 May 26.

Genetic associations for two biological age measures point to distinct aging phenotypes

Chia-Ling Kuo  1   2 , Luke C Pilling  2   3 , Zuyun Liu  4 , Janice L Atkins  3 , Morgan E Levine  5
Affiliations

Genetic associations for two biological age measures point to distinct aging phenotypes

Chia-Ling Kuo et al. Aging Cell. 2021 Jun.

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.

Keywords: APOE; biomarkers; cardiac aging; inflammaging; polygenic risk score.

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Conflict of interest statement

MEL is named on patent applications for epigenetic clocks and holds licenses for the clocks she has developed. MEL also serves as the Bioinformatics Advisor for Elysium Health.

Figures

FIGURE 1
FIGURE 1
PhenoAgeAccel (bottom) and BioAgeAccel (top) Manhattan plots (colors to separate adjacent chromosomes without other indications), including the top 10 mapped genes of lead SNPs (see Tables 2 and 3)
FIGURE 2
FIGURE 2
Significant gene sets identified by MAGMA for PhenoAgeAccel (in red) and BioAgeAccel (in green) at the Bonferroni‐corrected level, 0.05/10,678 for 10,678 gene sets)
FIGURE 3
FIGURE 3
Association between tissue‐specific gene expression and PhenoAgeAccel‐gene or BioAgeAccel‐gene association (p‐values significant at the Bonferroni‐corrected level 0.05/53 for 53 tissue types in red bars and others in green bars)
FIGURE 4
FIGURE 4
Comparisons between the top 20% or 40%‐60% and the bottom 20% of PhenoAgeAccel (in red) or BioAgeAccel (in blue) Polygenic Risk Score (PRS) for biomarkers included in PhenoAge or BioAge and a variety of aging phenotypes (*significantly associated with the top 20% of PhenoAgeAccel PRS at the 5% false‐discovery‐rate adjusted level; +significantly associated with the top 20% of BioAgeAccel PRS at the 5% false‐discovery adjusted level)
FIGURE 5
FIGURE 5
Odds ratios (ORs) for diseases comparing the top 20% or 40%‐60% to the bottom 20% of PhenoAgeAccel (in red) or BioAgeAccel (in blue) polygenic risk score (PRS) (*significantly associated with the top 20% of PhenoAgeAccel PRS at the 5% false‐discovery‐rate adjusted level; +significantly associated with the top 20% of BioAgeAccel PRS at the 5% false‐discovery‐rate adjusted level)
FIGURE 6
FIGURE 6
Mean standard deviation (SD) differences between non‐e3e3 and e3e3 genotypes: (1) biomarkers of PhenoAge (top) or BioAge (bottom) sorted by p‐value from left to right for the null hypothesis of no genotypic effects; (2) p < 0.05, p < 0.01, and p < 0.001 labelled by *, **, ***, respectively
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Update of

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