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Cited by 170Genetic Associations and EpidemiologyGenetics, Aging, and Longevity in Model OrganismsGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 80 of the 81 references it cites were found in a registry or an index.

Identification of 12 genetic loci associated with human healthspan

Open access
Zenin, Aleksandr, Tsepilov, Yakov, Sharapov, Sodbo, Getmantsev, Evgeny, Menshikov, L. I., Fedichev, Peter O., Aulchenko, Yurii·2019·Communications Biology
DOI 10.1038/s42003-019-0290-0PMC PMC6353874

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Abstract

Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity.

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Figures

Fig. 1 The incidence of the most prevalent chronic diseases, risk of death (the mortality rate) and healthspan for UKB participants. The disease incidence increases approximately exponentially with age at approximately the same rates. Disease incidence rates are calculated independently, participants that have more than one condition during follow-up period are counted for every disease they have, except for healthspan which is defined as the first event occurred. Shaded area represents 95% confidence interval
Fig. 1 The incidence of the most prevalent chronic diseases, risk of death (the mortality rate) and healthspan for UKB participants. The disease incidence increases approximately exponentially with age at approximately the same rates. Disease incidence rates are calculated independently, participants that have more than one condition during follow-up period are counted for every disease they have, except for healthspan which is defined as the first event occurred. Shaded area represents 95% confidence intervalCC BY · PubMed Central
Fig. 2 Discovery GWAS of healthspan in GCW-British individuals. The trait is a form of Martingale residual of the Cox-Gompertz proportional hazards model of healthspan as described in section Cox-Gompertz proportional hazards model and healthspan. The loci are tagged by SNPs from Table 2, labeled by the nearest gene symbol, replicated SNPs marked in bold
Fig. 2 Discovery GWAS of healthspan in GCW-British individuals. The trait is a form of Martingale residual of the Cox-Gompertz proportional hazards model of healthspan as described in section Cox-Gompertz proportional hazards model and healthspan. The loci are tagged by SNPs from Table 2, labeled by the nearest gene symbol, replicated SNPs marked in boldCC BY · PubMed Central
Fig. 3 Genetic correlation between GWAS of the healthspan and the diseases used to produce the healthspan phenotype in the UKB discovery cohort. The significant correlations marked in bold (p < 0.05 after Bonferroni correction)
Fig. 3 Genetic correlation between GWAS of the healthspan and the diseases used to produce the healthspan phenotype in the UKB discovery cohort. The significant correlations marked in bold (p < 0.05 after Bonferroni correction)CC BY · PubMed Central
Fig. 4 Thirty-five traits with significant and high genetic correlations with healthspan (|rg| ≥ 0.3; p ≤ 4.3 × 10−5). PMID references are placed in square brackets. Note the absence of genetic correlation between the healthspan and Alzheimer disease traits (rg = −0.03)
Fig. 4 Thirty-five traits with significant and high genetic correlations with healthspan (|rg| ≥ 0.3; p ≤ 4.3 × 10−5). PMID references are placed in square brackets. Note the absence of genetic correlation between the healthspan and Alzheimer disease traits (rg = −0.03)CC BY · PubMed Central

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Metadata

Authors
Zenin, A., Tsepilov, Y., Sharapov, S., Getmantsev, E., Menshikov, L. I., Fedichev, P. O., & Aulchenko, Y.
Affiliations
  • Novosibirsk State University (RU)
  • Moscow Institute of Physics and Technology (RU)
Corresponding author
Fedichev, Peter O.
ORCID
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2019-01-30
Volume
2
Issue
1
Pages
41-41
ISSN
2399-3642 (ISSN-L)
DOI
10.1038/s42003-019-0290-0
PMC
PMC6353874
OpenAlex
W2952981231
Cited by
170
References
81
Citations by year
2026: 22 · 2025: 26 · 2024: 31 · 2023: 23 · 2022: 15 · 2021: 25 · 2020: 13 · 2019: 12
FWCI
9.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Medical Research Council (MC_PC_17228)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
healthspan, human longevity, genetic loci, aging, chronic disease risk, Gompertz mortality law, all-cause mortality, genetic correlation, life-history traits, genetic risk models
MeSH terms
Aged, Alleles, Female, United Kingdom, Health Status, Humans, Longevity, Male, Cluster Analysis, Computational Biology, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Association Studies, Molecular Sequence Annotation

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