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

  1. Chia‐Ling Kuo
  2. Luke C. Pilling
  3. Zuyun Liu
  4. Janice L. Atkins
  5. Morgan E. Levine

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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.

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  1. Version published to 10.1111/acel.13376
  2. ScreenIT

    SciScore for 10.1101/2020.07.10.20150797: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Biological Age Measures: Biomarkers included in PhenoAge1 and/or BioAge2 are listed in Table 1.
    BioAge2
    suggested: None
    Biological age acceleration was estimated by the residual of PhenoAge or BioAge after subtracting the effect of chronological age using a linear regression model, termed PhenoAgeAccel and BioAgeAccel, respectively.
    BioAge
    suggested: None
    Additionally, a gene-property analysis was performed to test for positive relationships (one-sided test) between tissue-specific gene expression profiles and gene associations with PhenoAgeAccel or BioAgeAccel, using 53 tissue types from the GTEx repository version50.
    BioAgeAccel
    suggested: None
    Polygenic Risk Scores: The PRSice-2 software version 2.2.251 was used to perform polygenic risk score (PRS) analysis.
    PRSice-2
    suggested: (PRSice, RRID:SCR_017057)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Inevitably, our study has limitations. The UK Biobank participants are healthier than the general population35; therefore, are less susceptible to accelerated aging. The disease status was determined based on self-reported doctor diagnoses at baseline and electronic health records to 2017. Given that some participants were still relatively young and will likely go on to develop late-onset morbidity this will contribute to misclassification, which could bias associations towards the null. Nevertheless, when disease prognostic biomarkers were analyzed, we observed consistent results. Last but not least, our findings are based on European-descent participants and may not be generalizable to other ancestry populations. Overall, the mapped genes and enriched genes sets highlight that these two biological age measures may capture different aspects of the aging process—cardiometabolic by BioAge and inflammaging/immunoscenece by PhenoAge. Nevertheless, PhenoAgeAccel and BioAgeAccel PRSs are not disease-specific and can be used to prioritize genetic risk for multiple morbidity or mortality outcomes—particularly cardiovascular diseases and all-cause mortality via BioAge, and liver or kidney diseases, COPD, rheumatoid arthritis, hypothyroidism, and type I and type II diabetes via PhenoAge. Our findings confirm the hypothesis that individuals may age in different ways, due in part to different underlying genetic susceptibility. In moving forward, understanding personalized aging suscepti...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.07.10.20150797v2 on medRxiv
  4. Version published to 10.1101/2020.07.10.20150797v1 on medRxiv