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Rare genetic coding variants associated with human longevity and protection against age-related diseases

Nature Aging volume 1, pages 783–794 (2021)Cite this article

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Abstract

Extreme longevity in humans has a strong genetic component, but whether this involves genetic variation in the same longevity pathways as found in model organisms is unclear. Using whole-exome sequences of a large cohort of Ashkenazi Jewish centenarians to examine enrichment for rare coding variants, we found most longevity-associated rare coding variants converge upon conserved insulin/insulin-like growth factor 1 signaling and AMP-activating protein kinase signaling pathways. Centenarians have a number of pathogenic rare coding variants similar to control individuals, suggesting that rare variants detected in the conserved longevity pathways are protective against age-related pathology. Indeed, we detected a pro-longevity effect of rare coding variants in the Wnt signaling pathway on individuals harboring the known common risk allele APOE4. The genetic component of extreme human longevity constitutes, at least in part, rare coding variants in pathways that protect against aging, including those that control longevity in model organisms.

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Fig. 1: Longevity association of rare variants.
Fig. 2: Protective rare variants in Wnt signaling genes for APOE4+.
Fig. 3: Common polygenic risk of age-related diseases.
Fig. 4: Analysis of pathogenic rare variants.

Data availability

All summary statistics for the longevity association of rare coding variants in our Ashkenazi Jewish longevity cohort are available at http://zdzlab.einsteinmed.org/1/longevity.html. Due to privacy concerns for our research participants, individual-level genetic data from the Einstein longevity study are not publicly available; however, anonymized data will be shared by request from a qualified academic investigator, providing the data transfer is approved by the Institutional Review Board and regulated by a material transfer agreement. The German longevity cohort data are part of the PopGen Biobank (Schleswig-Holstein, Germany) and can be accessed through a Material Data Access Form (http://www.uksh.de/p2n/Information+for+Researchers.html). Sequence and phenotype data of the UK Biobank and ADSP cohorts are available at https://bbams.ndph.ox.ac.uk/ams/ and https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000572, respectively. All software used in our analyses was open source and is described in Methods.

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Acknowledgements

This work was supported by NIH grant nos. R01 HG008153 (Z.D.Z.), R01 AG061155 (S.M.), R01 AG060747 (M.D.G.), R01 AG057909 (N.B. and Z.D.Z.), P01 AG017242 (J.V.) and U19 AG056278 (J.V., P.D.R., L.J.N., Y.S. and W.C.L.) and a Career Scientist Award from the Irma T. Hirschl Trust to Z.D.Z. We thank the the Popgen Biobank and the Popgen 2.0 Network at Kiel University for help with recruitment of some of the long-lived individuals. G.G.T. was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) through project no. 390870439 (EXC 2150 – ROOTS). We thank T. Wang (Albert Einstein College of Medicine) for comments and suggestions. We thank the Management and Leadership Team at RGC for contributing to securing funding, study design and oversight and reviewing the manuscript (G. Abecasis, A. Baras, M. Cantor, G. Coppola, A. Deubler, A. Economides, L. A. Lotta, J. D. Overton, J. G. Reid and A. Shuldiner). We thank Sequencing and Lab Operations at RGC for performing and being responsible for sample sequencing (J. Marcovici, E. Weihenig, A. Lopez and J. D. Overton); for performing and being responsible for exome sequencing (A. DeVito, J. LaRosa, L. Widom, C. Beechert, C. Forsythe, E. D. Fuller, M. Lattari, M. Sotiropoulos Padilla, S. E. Wolf, A. Lopez and J. D. Overton); for conceiving and being responsible for laboratory automation (T. D. Schleicher, Z. Gu, A. Lopez and J. D. Overton); and for being responsible for sample tracking and the library information management system (M. Pradhan, K. Manoochehri, R. H. Ulloa and J. D. Overton). We thank Genome Informatics at RGC for performing, and being responsible for, the analysis needed to produce exome and genotype data (X. Bai, A. Hawes, W. Salerno and J. G. Reid); for providing computing infrastructure development and operational support (G. Eom and J. G. Reid); for providing variant and gene annotations and their functional interpretation of variants (S. Balasubramanian and J. G. Reid); and for conceiving and being responsible for creating, developing and deploying analysis platforms and computational methods for analysis of genomic data (E. K. Maxwell, J. C. Staples, L. Habegger and J. G. Reid). We thank Research Program Management at RGC for contributing to the management and coordination of all research activities, planning, execution and reviewing of the manuscript (M. B. Jones and L. J. Mitnaul).

Author information

Author notes

  1. These authors contributed equally Jhih-Rong Lin, Patrick Sin-Chan.

  2. A full list of members and their affiliations appears in Acknowledgements.

Authors and Affiliations

  1. Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA

    Jhih-Rong Lin, Joydeep Mitra, Quanwei Zhang, M. Reza Jabalameli, Zhen Wang, Nha Nguyen, Sofiya Milman, Yousin Suh, Jan Vijg, Nir Barzilai & Zhengdong D. Zhang

  2. Regeneron Pharmaceuticals Inc., New York, NY, USA

    Patrick Sin-Chan & Alan R. Shuldiner

  3. School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy

    Valerio Napolioni

  4. Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany

    Guillermo G. Torres, Andre Franke & Almut Nebel

  5. Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA

    Tina Gao, Gil Atzmon, Sofiya Milman & Nir Barzilai

  6. Division of Endocrinology, Diabetes and Clinical Nutrition, Department of Internal Medicine I, Kiel University, Kiel, Germany

    Matthias Laudes

  7. Institute of Transfusion Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany

    Siegfried Görg

  8. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Michael D. Greicius

  9. Department of Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel

    Gil Atzmon

  10. Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, NY, USA

    Kenny Ye

  11. Department of Biology, University of Rochester, Rochester, NY, USA

    Vera Gorbunova

  12. Department of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA, USA

    Warren C. Ladiges

  13. Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA

    Laura J. Niedernhofer & Paul D. Robbins

  14. Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA

    Yousin Suh

  15. Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA

    Yousin Suh

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  1. Jhih-Rong Lin
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Contributions

J.-R.L. and Z.D.Z. conceived the formal analysis. J.-R.L. executed the formal analysis. P.S.-C. and A.S. obtained the study resources. Z.D.Z., P.S.-C., J.M., Q.Z. and T.G. performed data curation. Z.W. performed variant imputation. V.N., G.G.T., M.D.G., A.F., A.N. and S.G. participated in replication analysis. Z.D.Z. and N.B. conceived of the research goals and acquired funding. J.-R.L. and Z.D.Z. wrote the original draft. J.V., Y.S., S.M., P.D.R., L.J.N., W.C.L., V.G., K.Y., G.A., M.L., M.R.J. and N.N. participated in review and editing. The R.G.C. performed WES and SNP array genotyping.

Corresponding author

Correspondence to Zhengdong D. Zhang.

Ethics declarations

Competing interests

J.V. is a founder of Singulomics Corp. P.D.R. and L.J.N. are cofounders of NRTK Biosciences. All other authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks George Martin and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 The replication study of gene-set longevity association using the WES data of the German longevity cohort.

The longevity case-control study consists of 1,265 longevity cases and 4,195 longevity controls. P* denotes P-value corrected for 12 categories of rare variants using the minimal-P value test from Flannick et al.67 (Methods). The text for the significant association denotes the lowest raw P-value among different groups of tested rare variants and FDR. (A) Full longevity cohort. (B) APOE4 stratified cohorts.

Extended Data Fig. 2 The replication study of gene-set longevity association using the UK Biobank WES data.

The longevity case-control study consists of 104 cases with at least one parent age at death ≥ 100 years and 23,405 controls with both parent age at death < 95 years. P* denotes P-value corrected for 12 categories of rare variants using the minimal-P value test from Flannick et al.67 (Methods). The text for the significant association denotes the lowest raw P-value among different groups of tested rare variants and FDR. (a) Full longevity cohort. (b) APOE4 stratified cohorts.

Extended Data Fig. 3 The replication study of gene-set longevity association using the ADSP WES data.

The longevity case-control study consists of 1,121 non-AD cases with age ≥ 90 years and 38 non-AD controls with age < 75 years. P* denotes P-value corrected for 12 categories of rare variants using the minimal-P value test from Flannick et al.67 (Methods). The text for the significant association denotes the lowest raw P-value among different groups of tested rare variants and FDR.

Extended Data Fig. 4 Gene-set rare variant association in the APOE4-stratied cohorts of the discovery (Ashkenazi Jewish) longevity cohort.

P* denotes P-value corrected for 6 categories of tested variants using the minimal-P value test from Flannick et al.67 (Methods). The text for the significant association denotes the lowest raw P-value among different groups of tested rare variants and FDR.

Extended Data Fig. 5 Lifespan analysis of protective variants in WNT signaling genes for noncentenarians.

P denotes uncorrected P-value derived from linear regression with the log-transformed age at death as the outcome and the gender as a covariate (See Methods). ‘WNT low’ and ‘WNT high’ represent the alternative allele count of rare variants in WNT signaling genes ≤ 1 and > 1 (the median), respectively. In parentheses are the numbers of individuals. MD stands for ‘median difference’. The asterisk denotes FDR < 0.05. (a) The lifespan difference of individuals carrying a high and low burden of protective rare variants in WNT signaling genes. (b) Negative effects of APOE4 on lifespan with high and low burden of protective rare variants in WNT signaling for noncentenarians.

Extended Data Fig. 6 Lifespan analysis of protective variants in WNT signaling genes for centenarians.

P denotes uncorrected P-value derived from linear regression with the log-transformed age at death as the outcome and the gender as a covariate (See Methods). ‘WNT low’ and ‘WNT high’ represent the alternative allele count of rare variants in WNT signaling genes ≤ 1 and > 1 (the median), respectively. In parentheses are the numbers of individuals. MD stands for ‘median difference’. (a) The lifespan difference of individuals carrying a high and low burden of protective rare variants in WNT signaling genes. (b) Negative effects of APOE4 on lifespan with high and low burden of protective rare variants in WNT signaling for centenarians.

Extended Data Fig. 7 Disease-PRS analyses for centenarian and control.

This shows the results of PRS analyses for age-related diseases in the centenarian cohort. In the boxplots, points represent individuals, and horizontal lines represent upper fence (maximum in Q3 + 1.5×IQR), upper quartile (Q3), median, lower quartile (Q1), lower fence (minimum in Q1 − 1.5×IQR), sequentially from top to bottom; IQR: interquartile range (25th to the 75th percentile). n = 910 biologically independent samples in the boxplots on the right panels for coronary artery disease, type 2 diabetes, stroke, and pancreatic cancer. n = 339 and 571 biologically independent samples in the boxplots on the right panels for prostate cancer and breast cancer, respectively. Above the boxplot on the right are raw and adjusted (in parentheses) P-values for the best prediction in the Nagelkerke’s R2 plot on the left, which were calculated based on logistic regression and the permutation test in PRSice-2, respectively. For stroke, breast cancer, prostate cancer, and pancreatic cancer, no robust association was observed between their PRS and the longevity status as originally defined in our cohort. (a) Coronary artery disease. (b) Coronary artery disease without considering SNPs within 1Mbps of rs7412 or rs429358 (SNPs for the APOE haplotype). (c) Type 2 diabetes. (d) Stroke. (e) Prostate cancer. Only males are considered. (f) Breast cancer. Only females are considered. (g) Pancreatic cancer.

Extended Data Fig. 8 Basic statistics of the lifespan cohort.

(a) Lifespan distribution of 553 individuals. (b) Survival curves of 202 males and 351 females composing the analyzed cohort. Females have a significant survival rate than males based on cox regression model (P = 1.71E-07; coxph in R package).

Extended Data Fig. 9 Correlation between lifespan and common-variant genetic risk of age-related diseases.

P-values were based on the result of linear regression (regress log lifespan on genetic disease risk) corrected for gender. (a) Alzheimer’s disease. The plots on the left and right show the boxplot and survival curves of APOE4 + and APOE4 − , respectively. MD stands for ‘Median Difference’. In the boxplots, points represent individuals, and horizontal lines represent upper fence (maximum in Q3 + 1.5×IQR), upper quartile (Q3), median, lower quartile (Q1), lower fence (minimum in Q1 − 1.5×IQR), sequentially from top to bottom; IQR: interquartile range (25th to the 75th percentile). n = 553 biologically independent samples. (b) Coronary artery disease. r represents ‘correlation coefficient’. (c) Type 2 diabetes.

Extended Data Fig. 10 Flowcharts of sample collection for different analyses.

(a) Flowchart of sample collection for PRS analyses and lifespan analyses of rare variants and disease PRS. Refer ‘Rare variant association analysis’ subsection for the strategy of removing kinship for PRS analysis that involves longevity status. The strategy of removing kinship in lifespan analyses is to randomly exclude one in pairs of individuals with the proportion of alleles shared identity-by-descent (IBD) > 0.4. (b) Flowchart of sample collection for rare variant association tests, network-integrated analyses, and lifespan analyses of rare variants (and APOE4).

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Lin, JR., Sin-Chan, P., Napolioni, V. et al. Rare genetic coding variants associated with human longevity and protection against age-related diseases. Nat Aging 1, 783–794 (2021). https://doi.org/10.1038/s43587-021-00108-5

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