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Common genetic associations between age-related diseases

Nature Aging volume 1, pages 400–412 (2021)Cite this article

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Abstract

Age is a common risk factor in many diseases, but the molecular basis for this relationship is elusive. In this study we identified four disease clusters from 116 diseases in UK Biobank data, defined by their age-of-onset profiles, and found that diseases with the same onset profile are genetically more similar, suggesting a common etiology. This similarity was not explained by disease categories, co-occurrences or disease cause–effect relationships. Two of the four disease clusters had an increased risk of occurrence from ages 20 and 40 years, respectively. They both showed an association with known aging-related genes, yet differed in functional enrichment and evolutionary profiles. Moreover, they both had age-related expression and methylation changes. We also tested mutation accumulation and antagonistic pleiotropy theories of aging and found support for both.

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Fig. 1: Age-of-onset profiles clustered by the PAM algorithm, using dissimilarities calculated with temporal correlation measure (CORT).
Fig. 2: Genetic similarities and mediated pleiotropy across diseases.
Fig. 3: Enrichment of disease-associated genes in known longevity modulators and GO categories.
Fig. 4: Risk allele frequencies for diseases associated with different age-of-onset clusters.

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Data availability

The primary data source used in the study was the UKBB resource20, which requires an application for access (https://www.ukbiobank.ac.uk/). This study was conducted under application number 30688. The UKBB GWAS summary statistics provided by Neale laboratory were downloaded for Townsend Deprivation Index and diet regimes (http://www.nealelab.is/uk-biobank). GTEx v.8 eQTL and expression data were accessed on 20 October 2020 via the GTEx data portal (https://www.gtexportal.org/home/datasets)33. GWAS Catalog v.1.0.2 e96 (ref. 70) dataset was accessed on 30 July 2019 via https://www.ebi.ac.uk/gwas/docs/file-downloads. The gene lists available in ‘Human Ageing Genomic Resources’34,35 were downloaded using https://genomics.senescence.info/download.html and CellAge data were kindly made available before the data release on 2 October 2019 by Avelar et al.36. We accessed ChEMBL (https://www.ebi.ac.uk/chembl/)73 and PubChem (https://pubchem.ncbi.nlm.nih.gov/)71 using their APIs and UniChem (https://www.ebi.ac.uk/unichem/)72 mappings were used to map PubChem CIDs to ChEMBL IDs. DGIdb (https://www.dgidb.org/)74 was used to compile drug–target gene interactions. Results of Adelman et al.37 and Marttila et al.38 age-related methylation studies were downloaded as article supplementary files. We accessed 1000 Genomes Project41 allele frequencies using the vcf file provided on the 1000 Genomes Project website (https://www.internationalgenome.org/data).

The full set of GWAS results from this study can be accessed using BioStudies (S-BSST407) and all other results generated in the analysis are provided as Supplementary Datasets and Tables.

Code availability

BOLT-LMM (v.2.3.2) (https://data.broadinstitute.org/alkesgroup/BOLT-LMM/)60, PLINK (v.1.90b6.4) (https://www.cog-genomics.org/plink/)61 and VarMap (https://www.ebi.ac.uk/thornton-srv/databases/VarMap)62 software were used. All remaining analyses were performed using R81 (v.3.5.0) (https://cran.r-project.org/), using RStudio IDE (v.1.1.453) (https://rstudio.com/). The following R packages were used: TSclust55 (v.1.2.4), cluster (v.2.0.7.1), HDL31 (v.1.3.8) (https://github.com/zhenin/HDL/), VariantAnnotation63 (v.1.28.13), TxDb.Hsapiens.UCSC.hg19.knownGene64 (v.3.2.2), GenomicRanges65 (v.1.32.3), biomaRt66 (v.2.36.1), RCurl82 (v.1.98.1.2), jsonlite83 (v.1.7.1), rtracklayer67 (v.1.40.3), liftOver68 (v.1.12.0), goseq75 (v.1.40.0), preprocessCore80 (v.1.50.0) and LCV32 method (https://github.com/lukejoconnor/LCV) implemented in R. The following packages were used for data handling: tidyverse84 (v.1.3.0) and data.table85 (v.1.12.4). The following packages were used for data visualization: igraph86 (v.1.2.1), ggnetwork87 (v.0.5.8), ggforce88 (v.0.2.2.9000), ggpubr89 (v.0.4.0), ggrepel90 (v.0.8.2), GGally91 (v.2.0.0), RColorBrewer92 (v.1.1.2), scales93 (v.1.1.1), ggthemes94 (v.4.2.0) and pheatmap95 (v.1.0.12).

All other analysis was performed using custom codes written in bash (v.4.2) or R (v.3.5.0) and are available in GitHub at https://github.com/mdonertas/ukbb_ageonset.

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Acknowledgements

This research has been conducted using the UK Biobank Resource (application no. 30688). The authors thank the GWAS Catalog team for providing the list of studies using UK Biobank data; J. Stephenson and R. Laskowski for their help in running VarMap tool; and M. Somel, S. Ozanne, P. Beltrao and W. Huber for fruitful discussions. This research was funded in whole, or in part, by the Wellcome Trust (098565/Z/12/Z). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission. H.M.D., D.K.F., L.P. and J.M.T. were funded by this Wellcome Trust grant. The work was also supported by the European Molecular Biology Laboratory (J.M.T), the EMBL International PhD Programme (H.M.D) and Comisión Nacional de Investigación Científica y Tecnológica - Government of Chile (CONICYT scholarship; M.F.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Handan Melike Dönertaş, Daniel K. Fabian, Matías Fuentealba & Janet M. Thornton

  2. Institute of Healthy Aging, Department of Genetics, Evolution and Environment, University College London, London, UK

    Daniel K. Fabian, Matías Fuentealba & Linda Partridge

  3. Max Planck Institute for Biology of Aging, Cologne, Germany

    Linda Partridge

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Contributions

H.M.D. conceived and designed the study with contributions from L.P. and J.M.T. H.M.D. analyzed the data with the help of D.K.F. and M.F.V. H.M.D. interpreted the results and wrote the manuscript with contributions from all authors. All authors read, revised and approved the final version of this manuscript.

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Correspondence to Handan Melike Dönertaş or Janet M. Thornton.

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Peer review information Nature Aging thanks Luke O’Connor, Luke Pilling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Disease categories and co-occurrences.

a, Disease hierarchy for the 116 diseases included in the analysis. The nodes are colored by the disease categories as indicated in the legend. b, Disease co-occurrence matrix summarizing relative risk scores and correlations. Each row and column denote diseases ordered by hierarchical clustering of risk scores. The color is defined by relative risk scores while the size is determined by ϕ value, indicating the robustness of the association (see Methods). The diagonal tiles are colored by the UK Biobank’s disease hierarchy to visualize if diseases from the same category cluster together. Associations for the 62 diseases that have at least one relative risk ratio higher than four (log2RR ≥ 2) or lower than minus four log2RR ≤ −2 are plotted.

Source data

Extended Data Fig. 2 Distribution of median age-of-onset across disease categories.

Points show diseases grouped by categories (individual boxplots). Categories are ordered by the median value of the median age-of-onset. The boxplots show the first and third quartiles, the median (dark line), and the whiskers extend from the quartiles to the last point in 1.5xIQR distance to the quartiles.

Source data

Extended Data Fig. 3 The number of significant variants across diseases, age-of-onset clusters, and disease categories.

a, Number of diseases for different number of significant variants (pBOLT-LMM≤5e-8). Diseases with the highest number of associations (N≥10,000) are given as an inset table. b, Comparison of the number of significant associations (y-axis, on a log scale) across age-of-onset clusters (x-axis) (ANOVA after excluding cluster 4, p = 0.06). Since the y-axis is on a log scale, diseases with zero significant associations are not shown on the graph. c, The same as (b) but for disease categories. Categories are ordered by the median number of significant SNPs. The boxplots (b-c) show the first and third quartiles, the median (dark line), and the whiskers extend from the quartiles to the last point in 1.5xIQR distance to the quartiles.

Source data

Extended Data Fig. 4 The raw and corrected values of genetic similarities within and across age-of-onset clusters.

a, The difference between genetic similarity within and across age-of-onset clusters, calculated between 47 diseases. Y-axis shows the genetic similarity (see Methods). b, The same as (a) but the y-axis is corrected for disease category and co-occurrence using a linear model. This panel is the same as Figure 2b and given here only for easier comparison. The boxplots show the first and third quartiles, the median (dark line), and the whiskers extend from the quartiles to the last point in 1.5xIQR distance to the quartiles. P-values are calculated using F-test on a linear model between genetic similarity scores and different/same age of onset clusters for panel a and including different/same disease category and disease co-occurrence (risk ratio) as covariates in panel b.

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Extended Data Fig. 5 Genetic similarities calculated using the high-definition likelihood (HDL) inference method.

a, The correlation between the genetic similarity scores calculated using the SNP overlap-based odds ratio (x-axis) and HDL (y-axis). Blue points show the similarities calculated between diseases in different age of onset clusters and red points show the similarities calculated between diseases in the same age of onset cluster. The correlation coefficient and p-value are calculated using a two-sided Spearman correlation test. The linear regression line (blue) and 95% confidence interval (gray shaded area) are shown. b, The difference between genetic similarity within and across age-of-onset clusters, calculated between 59 diseases. Y-axis shows the genetic similarity calculated using HDL. The difference between different and same age clusters is tested using a two-sided Wilcoxon test. The boxplots show the first and third quartiles, the median (dark line), and the whiskers extend from the quartiles to the last point in 1.5xIQR distance to the quartiles.

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Extended Data Fig. 6 The overlap between genes associated with selected aging-related traits and genes associated with diseases in different clusters.

The x-axis shows the log2 enrichment score, and the y-axis shows the age-of-onset clusters. The numbers of genes in each cluster (for both multidisease and multicategory genes) are given. The size of the points shows the statistical significance based on a one-sided permutation test (large points show nominal p-value ≤ 0.05, small ‘x’ indicates non-significant overlaps – none of the associations are significant after multiple testing correction), and the color shows different aging-related GWAS Catalog traits. The colored numbers near the points show the numbers of overlapping genes.

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Extended Data Fig. 7 Drug-target gene interaction network for the drugs specifically targeting multicategory genes in age-dependent clusters.

‘Drug-target gene’ interaction network for the drugs that specifically target multicategory cluster 1, cluster 2, or cluster ‘1 & 2’ genes as determined by Fisher’s exact test. Blue diamonds show the drugs with a significant association or targeting only one gene in these gene groups. Diamonds without written names are only represented with the ChEMBL IDs in the datasets and did not have names. Drug labels written in bold are drugs approved for different conditions. Circles represent the genes targeted by the significant hits, colored by their age-of-onset cluster. Gray circles show the genes targeted by these drugs but are not among the gene set of interest.

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Supplementary Tables 1–10. Descriptions of each table are available within the file.

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Dönertaş, H.M., Fabian, D.K., Fuentealba, M. et al. Common genetic associations between age-related diseases. Nat Aging 1, 400–412 (2021). https://doi.org/10.1038/s43587-021-00051-5

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