Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging

Daniel B Rosoff, Lucas A Mavromatis, Andrew S Bell, Josephin Wagner, Jeesun Jung, Riccardo E Marioni, George Davey Smith, Steve Horvath, Falk W Lohoff*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)

Abstract

The concept of aging is complex, including many related phenotypes such as healthspan, lifespan, extreme longevity, frailty and epigenetic aging, suggesting shared biological underpinnings; however, aging-related endpoints have been primarily assessed individually. Using data from these traits and multivariate genome-wide association study methods, we modeled their underlying genetic factor ('mvAge'). mvAge (effective n = ~1.9 million participants of European ancestry) identified 52 independent variants in 38 genomic loci. Twenty variants were novel (not reported in input genome-wide association studies). Transcriptomic imputation identified age-relevant genes, including VEGFA and PHB1. Drug-target Mendelian randomization with metformin target genes showed a beneficial impact on mvAge (P value = 8.41 × 10-5). Similarly, genetically proxied thiazolidinediones (P value = 3.50 × 10-10), proprotein convertase subtilisin/kexin 9 inhibition (P value = 1.62 × 10-6), angiopoietin-like protein 4, beta blockers and calcium channel blockers also had beneficial Mendelian randomization estimates. Extending the drug-target Mendelian randomization framework to 3,947 protein-coding genes prioritized 122 targets. Together, these findings will inform future studies aimed at improving healthy aging.

Original languageEnglish
Pages (from-to)1020–1035
Number of pages16
JournalNature aging
Volume3
Issue number8
Early online date7 Aug 2023
DOIs
Publication statusPublished - 7 Aug 2023

Bibliographical note

Funding Information:
We want to acknowledge the participants and investigators of the many studies used in this research without whom this effort would not be possible. We also want to acknowledge the Medical Research Council Integrative Epidemiology Unit (MRC-IEU, University of Bristol, UK), especially the developers of the MRC-IEU UK Biobank GWAS Pipeline. G.D.S. is a member of the UK MRC-IEU, which is funded by the MRC (MC_UU_00011/1) and the University of Bristol. This work was supported by the National Institutes of Health intramural funding (ZIA-AA000242 to F.W.L.) and the Division of Intramural Clinical and Biological Research of the Natiocholism. Figure 1 and Extended Data Figs. 5 , 6 and 7 were created using BioRender.com.

Funding Information:
We want to acknowledge the participants and investigators of the many studies used in this research without whom this effort would not be possible. We also want to acknowledge the Medical Research Council Integrative Epidemiology Unit (MRC-IEU, University of Bristol, UK), especially the developers of the MRC-IEU UK Biobank GWAS Pipeline. G.D.S. is a member of the UK MRC-IEU, which is funded by the MRC (MC_UU_00011/1) and the University of Bristol. This work was supported by the National Institutes of Health intramural funding (ZIA-AA000242 to F.W.L.) and the Division of Intramural Clinical and Biological Research of the Natiocholism. Figure and Extended Data Figs. , and were created using BioRender.com .

Publisher Copyright:
© 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Research Groups and Themes

  • Bristol Population Health Science Institute

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