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A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways

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Abstract

Frailty is an aging-related clinical phenotype defined as a state in which there is an increase in a person’s vulnerability for dependency and/or mortality when exposed to a stressor. While underlying mechanisms leading to the occurrence of frailty are complex, the importance of genetic factors has not been fully investigated. We conducted a large-scale genome-wide association study (GWAS) of frailty, as defined by the five criteria (weight loss, exhaustion, physical activity, walking speed, and grip strength) captured in the Fried Frailty Score (FFS), in 386,565 European descent participants enrolled in the UK Biobank (mean age 57 [SD 8] years, 208,481 [54%] females). We identified 37 independent, novel loci associated with the FFS (p < 5 × 10–8), including seven loci without prior described associations with other traits. The variants associated with FFS were significantly enriched in brain tissues as well as aging-related pathways. Our post-GWAS bioinformatic analyses revealed significant genetic correlations between FFS and cardiovascular-, neurological-, and inflammation-related diseases/traits, and subsequent Mendelian Randomization analyses identified causal associations with chronic pain, obesity, diabetes, education-related traits, joint disorders, and depressive/neurological, metabolic, and respiratory diseases. The GWAS signals were replicated in the Health and Retirement Study (HRS, n = 9,720, mean age 73 [SD 7], 5,582 [57%] females), where the polygenic risk score built from UKB GWAS was significantly associated with the FFS in HRS individuals (OR per SD of the score 1.27, 95% CI 1.22–1.31, p = 1.3 × 10–11). These results provide new insight into the biology of frailty by comprehensively evaluating its genetic architecture.

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Data availability

The UKBB data are available through the UK Biobank Access Management System. The HRS data are accessible on dbGap with accession number phs000428.v2.p2. A reporting summary for this article is available in the supplementary tables. The full GWAS summary statistics produced by this study are freely available on figshare (https://figshare.com/s/6683396c68807fe4e729).

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Acknowledgements

We sincerely thank many GWAS consortia for making their GWAS summary data publicly accessible. We conducted the research using the UK Biobank resource under an approved data request (ref: 34763) and the HRS resource approved via dbGap.

Funding

GJF is supported by the National Institutes of Health (K76AG059992, R03NS112859 and P30AG021342), the American Heart Association (18IDDG34280056 and 817874) and the Neurocritical Care Society Research Fellowship. YY and HZ were supported in part by the National Institutes of Health (R01 GM134005) and National Science Foundation (DMS 1902903).

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Author notes

  1. Yixuan Ye and Rommell B. Noche contributed equally to this work.

Authors and Affiliations

  1. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Yixuan Ye & Hongyu Zhao

  2. Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA

    Rommell B. Noche, Natalia Szejko, Cameron P. Both, Julian N. Acosta, Audrey C. Leasure, Kevin N. Sheth & Guido J. Falcone

  3. Department of Neurology, Medical University of Warsaw, Warsaw, Poland

    Natalia Szejko

  4. Department of Bioethics, Medical University of Warsaw, Warsaw, Poland

    Natalia Szejko

  5. University of Hawai’I, John A. Burns School of Medicine, Honolulu, HI, USA

    Stacy C. Brown

  6. Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Thomas M. Gill

  7. Department of Biostatistics, Yale School of Public Health, 60 College Street, P.O. Box 208034, New Haven, CT, 06520, USA

    Hongyu Zhao

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  1. Yixuan Ye
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Corresponding authors

Correspondence to Hongyu Zhao or Guido J. Falcone.

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Web resources

Finngen, https://www.finngen.fi/en/access_results

FUMA, https://fuma.ctglab.nl

GNOVA, https://github.com/xtonyjiang/GNOVA

HRS, http://www.nia.nih.gov/research/resource/health-and-retirement-study-hrs

LDhub, http://ldsc.broadinstitute.org/ldhub/

LDSC, https://github.com/bulik/ldsc

MAGENTA, https://software.broadinstitute.org/mpg/magenta/

PLINK, https://www.cog-genomics.org/plink/

PRScs, https://github.com/getian107/PRScs

UKBB, https://www.ukbiobank.ac.uk

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The authors declare that they have no conflict of interest.

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Hongyu Zhao and Guido J. Falcone jointly supervised this work

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Ye, Y., Noche, R.B., Szejko, N. et al. A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways. GeroScience 45, 2511–2523 (2023). https://doi.org/10.1007/s11357-023-00771-z

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