Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

Joris Deelen & Hae-Won Uh & Ramin Monajemi & Diana van Heemst & Peter E. Thijssen & Stefan Böhringer & Erik B. van den Akker & Anton J. M. de Craen & Fernando Rivadeneira & André G. Uitterlinden & Rudi G. J. Westendorp & Jelle J. Goeman & P. Eline Slagboom & Jeanine J. Houwing-Duistermaat & Marian Beekman

Received: 7 July 2011 /Accepted: 28 October 2011 /Published online: 24 November 2011 # The Author(s) 2011. This article is published with open access at Springerlink.com

SNP set grouped per pathway or gene region. We used this approach to study the joint effect on human longevity of genetic variation in two candidate path- ways, the insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway and the telomere maintenance (TM) pathway. For the analyses, we used genotyped

Abstract In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze the combined effect of a

Electronic supplementary material The online version of this article (doi:10.1007/s11357-011-9340-3) contains supplemen- tary material, which is available to authorized users.

J. Deelen (*) P. E. Thijssen E. B. van den Akker : P. E. Slagboom M. Beekman Section of Molecular Epidemiology, Leiden University Medical Center, Zone S

-P, PO Box

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RC Leiden, The Netherlands e-mail: j.deelen@lumc.nl

P. E. Thijssen

H.-W. Uh R. Monajemi S. Böhringer J. J. Goeman J. J. Houwing-Duistermaat Section of Medical Statistics, Leiden University Medical Center, P.O. Box

,

RC Leiden, The Netherlands

F. Rivadeneira A. G. Uitterlinden Department of Internal Medicine, Erasmus Medical Center, P.O. Box 2040, 3015 CE Rotterdam, The Netherlands

GWAS data of 403 unrelated nonagenarians from long- lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. We analyzed 1,021 SNPs in 68 IIS pathway genes and 88 SNPs in 13 TM pathway genes using four self-contained pathway tests (PLINK set-based test, Global test, GRASS and SNP ratio test). Although we observed small differences between the results of the different pathway tests, they showed consistent significant association of the IIS and TM pathway SNP sets with longevity. Analysis of gene SNP sets from these pathways indicates that the association of the IIS pathway is scattered over several genes (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK, SGK2, and YWHAG), while the association of the TM pathway seems to be mainly determined by one gene (POT1). In conclusion, this study shows that genetic variation in genes involved in the IIS and TM pathways is associated with human longevity.

years, several methods have been developed to perform SNP set analysis on GWAS data (Wang et al. 2010; Fridley and Biernacka 2011; Holmans 2010). There are two main types of methods, the competitive and the self-contained tests. The compet- itive tests compare the association between a SNP set and trait to a standard defined by the genotyped SNPs outside the SNP set (complement), while the self- contained tests compare the SNP set to a fixed standard that does not depend on the complement (Goeman and Buhlmann 2007).

Human longevity is a complex trait that is assumed to be determined by variation in many genes with small effects. Previous GWA studies, in which single SNP analyses were performed (Newman et al. 2010; Deelen et al. 2011), have identified only one genome- wide significant locus contributing to survival into old age; APOE. However, the genetic contribution to human lifespan variation, determined in twin studies, – is estimated at 25 30% (Gudmundsson et al. 2000; Hjelmborg et al. 2006; Skytthe et al. 2003) and, although the effect of genetic variation in APOE is relatively large, the heritability of longevity is only partially explained by this variation (Deelen et al. 2011). Part of the remaining heritability might be explained by functionally related SNPs with small effects, of which the joint effect could not be detected in a single SNP analysis. Testing of SNP sets of candidate pathways for association with longevity would therefore be valuable.

Introduction

single SNP analysis have been very successful in identifying loci for various quantitative traits and diseases (Manolio et al. 2008). It became apparent that complex traits are usually determined by many genes with small effects and that results from single SNP analysis provide limited biological insight and only partly explain the genotypic variation of the studied trait. Instead of analyzing single SNPs, the combined effect of a SNP set, grouped per pathway or gene region, can be tested for association with the trait of interest. Such SNP set analysis could be used as an alternative approach for GWAS analysis and, since the composition of SNP sets is often based on pathways, should be able to provide additional biological insight of the studied trait.