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 Bhringer, Erik B. van den Akker, Anton J. M. de Craen, Fernando Rivadeneira, Andr G. Uitterlinden, Rudi G. J. Westendorp, Jelle J. Goeman, P. Eline Slagboom and Jeanine J. Houwing-Duistermaat conducted a study using genotyped GWAS data of 403 unrelated nonagenarians from long-lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. They 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 they 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.

In genome-wide association studies (GWAS) of complex traits like human longevity, 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 SNP set grouped per pathway or gene region. There are two main types of methods: the competitive and the self-contained tests. The competitive 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.

Human longevity is a complex trait that is assumed to be determined by variation in many genes with small effects. Previous GWAS studies, in which single SNP analyses were performed, 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%. Although the effect of genetic variation in APOE is relatively large, the heritability of longevity is only partially explained by this variation. 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.

Single SNP analysis has been successful in identifying loci for various quantitative traits and diseases. 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 should be able to provide additional biological insight into the studied trait.

The insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway is considered as a candidate pathway for studying human longevity. It is involved in the adaptation of the organism to its changing environment. When experimentally induced in model organisms like worms, flies, and mice, mutations in genes that play a role in IIS (e.g., homologues of human IGF1R, INSR, IRS1, PI3K, and FOXO) were shown to have a considerable effect on lifespan. Although the IIS pathway is evolutionarily conserved, the complexity of the human IIS pathway is much larger compared to that of model organisms.

Since the amount of tests in SNP set analysis is low compared to single SNP analysis, it requires a lower penalty for multiple testing. Therefore, SNP set analysis is also very suitable in studies with low power for GWAS analysis.considered to be within the window if it was located between 5 kb upstream and 5 kb downstream of the gene boundaries. SNPs were selected based on their location within the window and their minor allele frequency (MAF), which had to be at least 0.01 in the Rotterdam Study (RS) controls. Quality control was performed on the selected SNPs, and only those passing all criteria were included in subsequent analyses.