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The genetic architecture of biological age in nine human organ systems

Nature Aging (2024)Cite this article

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Abstract

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci–BAG pairs (P < 5 × 10–8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer’s disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

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Fig. 1: Genomic loci associated with the nine BAGs.
Fig. 2: Phenome-wide associations of the identified genomic loci and SNP-wide heritability estimates of the nine BAGs.
Fig. 3: Gene-level biological pathway annotation and tissue-specific gene expression.
Fig. 4: Gene–drug–disease network of the nine BAGs.
Fig. 5: Partitioned heritability enrichment and genetic correlation of the nine BAGs.
Fig. 6: Causal multiorgan network between the nine BAGs and 17 clinical traits of chronic diseases, lifestyle factors and cognition.

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Data availability

This study used the UK Biobank resource under application numbers 35148 and 60698. The raw imaging data are restricted to registered researchers and are protected and unavailable due to data privacy laws; access can be obtained at https://www.ukbiobank.ac.uk/. The GWAS summary statistics corresponding to this study are publicly available on the MEDICINE knowledge portal (https://labs-laboratory.com/medicine). The gene–drug–disease network used data from the DrugBank database (v.5.1.9; https://go.drugbank.com/). Our genetic analyses also used GWAS summary statistics from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) and GWAS Catalog (https://www.ebi.ac.uk/gwas/) as well as GWAS summary data from individual studies by requesting directly from the authors. Gene sets were obtained from the Molecular Signatures Database (MSigDB, v7.5.1; https://www.gsea-msigdb.org/gsea/msigdb/). Tissue specificity enrichment analysis used data from GTEx V8 (https://gtexportal.org/home/). The analysis for partitioned heritability estimates used data from ROADMAP (https://egg2.wustl.edu/roadmap/web_portal/) and ENTEx (https://www.encodeproject.org/). All source data and original figures are also publicly available at Zenodo at https://doi.org/10.5281/zenodo.11075409 (ref. 84). All unrestricted data supporting the findings are also available from the corresponding author upon request.

Code availability

The software and resources used in this study are all publicly available at the following links:

• BioAge (git version 37e54eb): https://github.com/yetianmed/BioAge, biological age prediction

• PLINK (v2.0): https://www.cog-genomics.org/plink/, linear model GWAS

• FUMA (v1.5.0): https://fuma.ctglab.nl/, gene mapping and genomic locus annotation

• GCTA (v1.94.1): https://yanglab.westlake.edu.cn/software/gcta/#Overview, heritability estimates and mixed-effect GWAS with fastGWA

• LDSC (git version aa33296): https://github.com/bulik/ldsc, genetic correlation and partitioned heritability

• TwoSampleMR (v0.5.6): https://mrcieu.github.io/TwoSampleMR/index.html, Mendelian randomization

• Coloc (v5): https://github.com/chr1swallace/coloc, Bayesian colocalization

• LCV (git version 39950a8): https://github.com/lukejoconnor/LCV, LCV for causal inference

• GREP (v1.0.0): https://github.com/saorisakaue/GREP, gene–drug–disease network analysis

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Acknowledgements

We want to express our gratitude to the UK Biobank team for their invaluable contribution to advancing clinical research in our field. We gratefully acknowledge the support of the iSTAGING Consortium, funded by the National Institute on Aging through grant RF1 AG054409 at the University of Pennsylvania (C.D.). Additionally, we acknowledge the funding program from the Rebecca L. Cooper Foundation at the University of Melbourne (A.Z.). We thank P. Parmpi and J. Incmikoski at the University of Pennsylvania for their valuable administrative support. We thank J. Deelen and J. M. Murabito for their generosity in providing the GWAS summary statistics.

Author information

Authors and Affiliations

  1. Laboratory of AI and Biomedical Science (LABS), University of Southern California, Los Angeles, CA, USA

    Junhao Wen

  2. Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

    Ye Ella Tian & Andrew Zalesky

  3. Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ioanna Skampardoni, Zhijian Yang, Yuhan Cui, Elizabeth Mamourian & Christos Davatzikos

  4. Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

    Filippos Anagnostakis

  5. Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  6. Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA

    Arthur W. Toga

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Contributions

Study concept and design: J.W. Acquisition, analysis or interpretation of data: J.W. Drafting of the manuscript: J.W., Y.E.T., A.Z. and C.D. Critical revision of the manuscript for important intellectual content: J.W., Y.E.T., I.S., Z.Y., Y.C., F.A., E.M., B.Z., A.Q.T., A.Z. and C.D. Statistical analysis: J.W. BAG index generation: Y.E.T.

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Correspondence to Junhao Wen.

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Nature Aging thanks Amanda Elliot, Dan Arking and Luke Pilling for their contributions to the peer review of this work.

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Extended data

Extended Data Fig. 1 Scatterplots of the main GWAS sensitivity analysis for nine BAGs.

We scrutinized the robustness of the nine primary GWAS using the European ancestry populations by fully considering linkage disequilibrium. We only included the independent significant SNPs in four different sensitivity check analyses. We reported three statistics i) r-β: Pearson’s r between the two sets of β coefficients from the two splits; ii) C-β: concordance rate of the sign of the β coefficients from the two splits – if the same SNP exerts the same protective/risk effect between the two splits; iii) P-β: the difference between the two sets of β coefficients from the two splits – if the two sets of β coefficients (mean) statistically differ (two-sample t-test). Detailed statistics are presented in Supplementary Note 1 for a) split-sample, b) sex-stratified, c) fastGWA vs. PLINK, and d) European vs. Non-European GWAS analyses.

Extended Data Fig. 2 SNP-based heritability, beta coefficients, and alternative allele frequency using the brain-BAG comparable populations and different inclusion criteria for the SNPs.

a) The SNP-based heritability of the nine BAGs using populations from downsampling to the brain BAG population. Error bars represent the standard error of the estimated parameters, and the measure of the center for the error bars represents the inferred statistics (that is, SNP-based heritability). b) The absolute value of the beta coefficients of the independent significant SNPs of the nine BAG GWASs using populations from downsampling to the brain BAG population (N = 30,108); the independent significant SNPs are shown separately for each BAG. c) The alternative (effective) allele frequency of the independent significant SNPs from the nine BAG GWASs using populations from downsampling to the brain BAG population (N = 30,108). d) The beta coefficients of the independent significant SNPs using the original full samples but with all identified independent significant SNPs across the nine BAG GWASs (with the same number of SNPs tested), where we see no difference regarding allele frequency in Figure e). f) The absolute value of the beta coefficients of the independent significant SNPs plus the candidate SNPs in LD of the nine BAG GWASs using the original full samples; the SNPs are shown separately for each BAG. g) The alternative allele frequency for the setting in Figure f). h) The absolute beta coefficients of the nine BAGs using all genome-wide SNPs (the y-axis was truncated to 0.1 for visualization purposes). i) the alternative allele frequency did not differ for Figure h) including all genome-wide SNPs. For Figure c, e, g, and i, the upper/lower whiskers show the upper/lower boundaries based on the 1.5xthe interquartile range (IQR). The upper/lower hinge displays the top/bottom end of the IQR. The central measures denote the median values.

Extended Data Fig. 3 Trumpet plots of the alternative allele frequency vs. the beta coefficient of the nine BAG GWASs.

The trumpet plots display the inverse relationship between the alternative (effect) allele frequency and the effect size (beta coefficient) for the brain, cardiovascular, eye, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal BAGs. Only the independent significant SNPs were considered. The dot size corresponds to the effect size, while the transparency of the dot is proportional to its statistical significance.

Extended Data Fig. 4 Bayesian colocalization signal between the pulmonary BAG and FEV/FVC.

Here, we illustrate the colocalization signal between the pulmonary BAG and the FEV/FCV feature at the genomic locus: 4q24, with the top lead SNP (causal SNP: rs7664805). Genetic colocalization was evidenced at one locus (4q24) between the pulmonary BAG and the FEV/FCV feature. The signed PP.H4.ABF (0.99) denotes the posterior probability (PP) of hypothesis H4, which suggests that both traits share the same causal SNP (rs7664805). All P-values were two-sided.

Extended Data Fig. 5 Gene-set enrichment analysis using sex-stratified GWAS results.

Gene-set enrichment analysis was performed using the GWAS summary statistics specific to females (a) and males (b). Gene set enrichment analyses were performed using curated gene sets and GO terms from the MsigDB database. Only significant gene sets are presented after adjusting for multiple comparisons using the Bonferroni correction. All P-values were two-sided.

Extended Data Fig. 6 Tissue-specific gene expression analysis using sex-stratified GWAS results.

Tissue-specific enrichment analysis was performed using the GWAS summary statistics specific to females (a) and males (b). Gene-property analyses evaluate tissue-specific gene expressions for the nine BAG-related genes using the full SNP P-values distribution. Only significant gene sets are presented after adjusting for multiple comparisons using the Bonferroni correction. All P-values were two-sided.

Extended Data Fig. 7 Genetic correlations using sex-stratified GWAS results.

The genetic correlation between each pair of BAGs was determined using sex-stratified GWAS summary statistics from our analyses. Most of the genetic correlations showed consistency between females and males, albeit sex differences are evident in certain BAGs, particularly in the cardiovascular BAG results. Specifically, males exhibit dominant correlations between cardiovascular BAGs and hepatic and renal BAGs, while females demonstrate specific correlations with musculoskeletal and pulmonary BAGs. All P-values were two-sided, and Bonferroni correction was employed to denote significant signals (* symbols).

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Wen, J., Tian, Y.E., Skampardoni, I. et al. The genetic architecture of biological age in nine human organ systems. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00662-8

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