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Genome-wide association study of aging

Stefan Waltera,b,1, Gil Atzmonc,d,e,1, Ellen W. Demerathf,1, Melissa E. Garciag,1, Robert C. Kaplanh,1, Meena Kumarii,1, Kathryn L. Lunettaj,1, Yuri Milaneschik,1, Toshiko Tanakak,1, Gregory J. Tranahl,1, Uwe Vlkerm,1, Lei Yun,1, Alice Arnoldo, J. Benjaminp,q, Reiner Biffarr, Aron S. Buchmann, Eric Boerwinkles, David Coupert, Philip L. De Jageru, Denis A. Evansv,
Tamara B. Harrisg,
Wolfgang Hoffmannw,x,
Albert Hofmanb,
David Karasiky,
Douglas P. Kiely,
Thomas Kocherr,
Maris Kuningasb,
Lenore J. Launerg,
Kurt K. Lohmanz,
Pamela L. Lutseyf,
Johan Mackenbacha,
Kristin Marcianteaa,
Bruce M. Psatyaa,bb,
Eric M. Reimancc,
Jerome I. Rotterdd,
Sudha Seshadrip,q,
Michelle D. Shardellee,
Albert V. Smithff,
Cornelia van Duijnb,
Walstongg,
M. Carola Zillikenshh,
Stefania Bandinelliii,2,
Sebastian E. Baumeisterw,2,
David A. Bennettn,2,
Luigi Ferruccijj,2,
Vilmundur Gudnasonff,2,
Mika Kivimakii,2,
Yongmei Liuz,2,
Joanne M. Murabitop,q,2,
Anne B. Newmankk,2,
Henning Tiemeierb,ll,2,*,

a Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
b Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
f Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN,
g Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, National Institutes of Health, Bethesda,
h Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
i Department of Epidemiology and Public Health, University College London, London, UK
j Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
m Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
n Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
o Department of Biostatistics, University of Washington, Seattle,
v Rush Institute for Healthy Aging, Rush University Medical Center,
w Institute of Community Medicine, Ernst-Moritz-Arndt-University Greifswald,
x for Integrated Dementia Care Research (CIDC), a scientific cooperation between the Universities and University Hospitals of Rostock and Greifswald,
and the German Center for Neurodegenerative Disease (DZNE), Bonn,
y 0197-4580/$ see front matter 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.neurobiolaging.2011.05.026

Received 7 January 2011; received in revised form 14 April 2011; accepted 30 May 2011

Abstract

Human longevity and healthy aging show moderate heritability (20%50%). We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death. No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p << 5 x 10^-8). We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p < |10^-5). These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease. In addition to considerable overlap between the traits, pathway8.8 (SD 5.7) years, and we registered 7314 major events. Frailty and survival free of disease have been suggested as more promising phenotypes for studies of aging because mortality is a very heterogeneous outcome caused by multiple chronic conditions (Vijg and Suh, 2005). We aimed to identify single nucleotide polymorphism (SNPs) associated with all-cause mortality (time to death) in a hypothesis-free GWAS in approximately 25,000 unselected persons of European ancestry. We also performed GWAS of time to event, defined by major incident events (myocardial infarction, heart failure, stroke, dementia, hip fracture, or cancer) or death, as an alternative phenotype for healthy aging. Finally, we analyzed the SNPs along with their respective most likely associated genes identified in the GWAS meta-analyses to identify pathways and networks associated with aging and longevity.

As different genotyping platforms were used across studies, we imputed to 2.5 million SNPs using the HapMap 22 CEU (Build 36) genotyped samples as a reference. We used the semiparametric Cox proportional hazard to model time to event for both phenotypes in each study. Follow-up time since baseline was used as time scale. An additive genetic model was used in this analysis. We subsequently combined the individual study results in a meta-analysis using a fixed-effects model that combined the study-specific regression parameters and standard errors using inverse variance weighting.