Materials and methods

Genotyping and SNP selection

from the GWAS described by Deelen et al. (2011). The LLS GWAS cases were genotyped using Illumina Infinium HD Human660W-Quad BeadChips (Illu- mina, San Diego, CA, USA). The RS GWAS controls were genotyped using Illumina Infinium II Human- Hap 550K Beadchips and Illumina Infinium II HumanHap550-Duo BeadChips (Illumina), respec- tively (Teichert et al. 2009). Of the 551,606 SNPs measured in both the LLS GWAS cases and RS GWAS controls, 516,712 SNPs passed quality control ≥ using the following criteria: SNP call rate 0.95 or ≥ MAF 0.01 in RS GWAS controls and LLS GWAS cases, PHWE ≥ 10−4 and no between-chip effect in the RS GWAS controls, and good cluster plots in the LLS < GWAS cases and RS GWAS controls if P 1  10(cid:3)4 (Deelen et al. 2011).

Leiden longevity study

For the LLS, long-lived siblings of European descent were recruited together with their offspring and the partners of the offspring. Families were included if at least two long-lived siblings were alive and fulfilled the age criterion of 89 years or older for males and 91 years or older for females, representing less than 0.5% of the Dutch population in 2001 (Schoenmaker et al. 2006). In total, 944 long-lived proband siblings were included with a mean age of 94 years (range, – – 89 104), 1,671 offspring (61 years, 39 81), and 744 – partners (60 years, 36 79). DNA from the LLS was extracted from samples at baseline using conventional methods (Beekman et al. 2006). For the GWAS, 403 unrelated LLS siblings (one sibling from each sibling pair) were included (LLS GWAS cases) (Deelen et al. 2011).

We analyzed SNPs within a 10-kb window around genes encoding proteins that belonged to the IIS (Fig. 1) and TM pathway (Fig. 2). A gene was defined as an NCBI Entrez Gene (mRNA or RNA) cluster, corresponding to a set of transcripts (RefSeq) for which the alignments can be obtained from the UCSC genome browser (http://genome.ucsc.edu/), in which all transcripts within a cluster agree on strand and overlap. Due to an overlap of the 10-kb windows

The RS is a prospective population-based study people aged 55 years and older, which was to study neurological, cardiovascular, locomotor,

around IGF2 and INS, two SNPs, rs4320932 and rs7924316, were assigned to both genes.

views/release/bioc/html/globaltest.html; Goeman et al. 2004), which is capable and powerful for analyz- ing GWAS data (Chapman and Whittaker 2008; Pan 2009). This test is based on a multiple logistic regression model that uses the phenotype as the response variable and the SNPs in the SNP set as covariates and which automatically takes the correlations between SNPs into account. The null hypothesis is tested that none of the SNPs in the SNP set are associated with the phenotype. P values are calculated using a permutation test based on 10,000 permutations.

Statistical analysis

PLINK set-based test

– In the PLINK set-based test ( set-test, http://pngu. mgh.harvard.edu/purcell/plink; Purcell et al. 2007), a single SNP analysis (in our case, a trend test) of the original pathway or gene SNP set is performed. For each SNP set, a mean SNP statistic is calculated from – the single SNP statistics of a maximum amount ( set- max) of independent SNPs below a certain P value – threshold ( set-p). If SNPs are not independent, i.e., in case linkage disequilibrium (r2) is above a certain – threshold ( set-r2), the SNP with the lowest P value in the single SNP analysis is selected. The same – analysis is performed with a certain amount ( mperm) of simulated SNP sets in which the phenotype status of the individuals is permuted. An empirical P value for the SNP set is computed by calculating the number of times the test statistic of the simulated SNP sets exceeds that of the original SNP set. For the analysis in this study, the parameters were set to – set- p 0.05 – set-r2 0.5, – set-max 99999, and – mperm 10,000.

For the comparative approach, 10,000 random SNP sets per pathway SNP set were generated and tested to determine the chance to find a similar- sized SNP set with a comparable or lower P value as compared to the original pathway SNP set.

SNP ratio test

snpratiotest/; O'Dushlaine et al. 2009) performs a single SNP analysis (in our case, a trend test) of the original pathway or gene SNP set and of similar-sized SNP sets in which the phenotype status of the individuals is permuted. An empirical P value of the SNP set is computed by calculating the ratio between the proportion of SNPs that shows an association below a certain P value threshold (p) in the original GWAS dataset and in the simulated GWAS datasets. The amount of significant SNPs in the simulated GWAS datasets is defined as the top n SNPs with the lowest P values, where n is the amount of SNPs with an association below p in the original GWAS dataset. For the analysis in this study, we made use of “ the scripts described in SRT_documenta- ” tion_090310.pdf (http://sourceforge.net/projects/ snpratiotest/). For the analysis in this study, p was set to 0.05, and the amount of simulated datasets used was 10,000.

GRASS (http://linchen.fhcrc.org/grass.html; Chen et al. 2010) calculates “ eigenSNPs” for each gene in the pathway SNP set by summarizing the variation of a gene using principal component analysis. Subsequently, one or more of these “ eigenSNPs” per gene are selected using regularized logistic regression to calculate a test statistic for each pathway SNP set. The same analysis is performed with simulated SNP sets in which the phenotype status of the individuals is permuted. The P value per pathway SNP set is calculated by comparing the test statistic of the original pathway SNP set with that of the combined simulated pathway SNP sets. For the analysis in this study, the amount of simulated pathway SNP sets was 10,000.

Global test