In this study, we examined the joint effect of genetic variation in the IIS and TM pathways on human longevity using data from the Leiden Longevity Study (LLS) and younger population controls from the Rotterdam Study (RS). We used several self-contained and competitive tests to analyze associations between SNPs in genes from these pathways and human longevity. Our results provide insights into the complex genetic networks that regulate aging and longevity, and may have implications for future research on interventions to extend healthy lifespan.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 of people aged 55 years and older, which was designed to study neurological, cardiovascular, and locomotor functions. Around IGF2 and INS, two SNPs, rs4320932 and rs7924316, were assigned to both genes. The Global test (http://www.bioconductor.org/help/bioc-views/release/bioc/html/globaltest.html; Goeman et al. 2004), which is capable and powerful for analyzing GWAS data (Chapman and Whittaker 2008; Pan 2009), is used in this study. 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 while automatically taking 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 show 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 used the scripts described in SRT_documentation_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:
To adjust for multiple testing, the significance level was set at the Bonferroni-corrected nominal P value (which is 0.05/(number of pathway or gene SNP sets tested)). In this study, we used a modified version of127,509,334 269.84 26 64.00% mLST8 (mTORC2) MLST8 64223 16 2,195,451 2,199,419 3.97 4 80.00% mTOR (mTORC2) MTOR 2475 1 11,089,176 11,245,195 156.02 11 50.00% PEPCK PCK1 5105 20 55,569,543 55,574,919 5.38 10 33.33% PDK1 PDPK1 5170 16 2,527,971 2,593,190 65.22 0 0.00% PHLPP1 PHLPP1 23239 18 58,533,714 58,798,646 264.93 41 64.29% PHLPP2 PHLPP2 23035 16
70,236,353
70,306,205
69.85
4
15.38% PI3K PIK3CA
5290
3
180,349,005
180,435,191
86.19
10
44.44%
PIK3CB
5291
3
139,856,921
139,960,875
103.95
8
42.86%
PIK3CD
5293
1
9,634,377
9,711,759
77.38
8
42.11%
PIK3R1
5295
5
67,558,218
67,633,405
75.19
31
65.12%