aPermutation (n=10,000) P value

explorative and candidate based. Since we want to focus on two pathways, the IIS and TM pathways, we performed candidate-based pathway analysis. The advantage of testing candidate pathways instead of explorative testing is the decreased penalty for multiple testing, due to the limited amount of tests performed. For information about pathways, several databases are available, e.g., Gene Ontology (Ashburner et al. 2000) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto 2000), which are particularly useful for explorative studies (Wang et al. 2010). However, to our knowledge, the IIS and TM path- ways are not described in sufficient detail in these databases, and we therefore assembled these path- ways based on literature. Although the IIS pathway is available in KEGG (hsa04910; insulin-signaling pathway), only four of the nine IIS pathway genes that were associated with longevity, AKT1, AKT3, INS, and PIK3CA, were part of this pathway, which indicates that the pathway definition used in this study could have had a large influence on the results of the

available pathway tests require SNP P values as input data, while others require raw genotypes (Wang et al. 2010). Given that we have GWAS data available, we selected pathway tests that make use of raw geno- types. All four selected pathway tests are self- contained tests which deal with the complexity of SNP set testing by permuting the case-control status. While, the PLINK set-based test, Global test, and SNP ratio test do not completely incorporate LD information, GRASS employs PCA to deal with correlations within each gene. A simulation study showed that in general, GRASS was more power- ful than the PLINK set-based test (Chen et al. 2010). Simulation studies for Global test or SNP ratio test are not yet available. However, despite the differences between the methods, they all showed similar results for the IIS and TM pathways in this study.

SNP set analysis could have power to detect significant association, even if the power to detect associations in single SNP analysis is low (Fridley and Biernacka 2011), as was previously shown in the Welcome Trust Case Control Consortium (Torkamani et al. 2008). Our study has a power <1% to detect single SNP associations of the tested SNPs with an OR of 1.2 and a minor allele frequency of 0.25 (the mean frequency of the tested SNPs). However, because the small (non-significant) effects of the

Different pathway tests could show contradictory results, even when analyzing the same GWAS data (Wang et al. 2010). These discrepancies are caused by differences in, for example, the underlying statistical methods of the tests. Therefore, we used several

and management of GWAS genotype data for the Rotterdam study are supported by the Netherlands Organisation for Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2) and the Nether- lands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. The Rotterdam Study is funded by the Erasmus Medical Center and Erasmus University, Rotterdam; the Netherlands Organiza- tion for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam.

SNPs are jointly tested, the pathway SNP set analysis is able to detect a significant association of the IIS and TM pathway. This indicates that SNP set analysis could be a useful approach for studies which showed no significant associations in single SNP analysis.

There is still much debate about the optimal size of the window used in SNP set analysis (Holmans 2010; Fridley and Biernacka 2011; Wang et al. 2010), and we choose a fixed window of 10 kb to take into account effects of SNPs in regulatory regions surrounding the genes. The same window was also used in a previous study of the IIS pathway (Pawlikowska et al. 2009). Although there is a chance that we will miss some functional SNPs, increasing the window would increase the chance that SNPs are included with no functional relationship to the tested gene.

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

gene/pathway is highly variable between genotyping platforms used for GWAS. In addition, there is a large variety in allele frequencies and presence of SNPs between populations. For single SNP analysis, one is dependent on association of the same SNP (or a SNP in high LD) for replication. However, when due to varying allele frequencies, different SNPs associate in different populations, SNP set analysis determines the combined effect of SNPs within a gene and is able to overcome this problem. Therefore, replication of SNP set analysis is assumed to be more reproducible between genotyping platforms and populations (Luo et al. 2010; Wang et al. 2010). To support these assumptions, our findings should be replicated in other cohorts.

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