Genotyping

DNA was isolated from blood spot cards using the QIAamp DNA Mini and Micro Kits (Qiagen, Ger- many); genotyping was performed using Illumina GoldenGate technology (Illumina Inc, San Diego, CA, USA), and data clean up, data verification, and reproducibility checks were conducted as previously described (Soerensen et al. 2010). In total, 16 SNPs were excluded, leading to 102 SNPs for data analysis. Genotyping in the German and Dutch study popula- tions was performed using Sequenom MassARRAY iPLEX®Gold technology (Sequenom®, Inc., San Diego, CA, USA); see the Supplementary Material (Online Resource 1) for details.

Study populations for replication of initial findings

For replication of the initial observations from the – Danish case control study, two German samples of – 1,613 unrelated long-lived individuals (age 95 110) and 1,140 middle-aged controls (mean age 67.2) were used. The participants were identified through local registry offices and from the Federal Administrative Office. They were recruited from the different geo- graphic regions of Germany and were all of German ancestry (Nebel et al. 2005). Approval was received from the Ethics Committee of the University Hospital Schleswig-Holstein.

For replication of initial findings from the Danish longitudinal study, the Dutch Leiden 85-plus Study was used. This prospective cohort was initiated in 1997, when all 85-year-old inhabitants of the city Leiden in the Netherlands were invited to participate. The cohort consists of 563 participants, all Caucasians – and members of the 1912 1914 birth cohort recruited from 1997 to 1999 (Bootsma-van der Wiel et al. 2002). The Medical Ethical Committee of the Leiden University Medical Centre approved the study, and informed consent was obtained from all participants or, in case of severe cognitive impairment, from their guardian.

Whole blood samples were collected, stabilized, and transported in PAXgene Blood RNA Tubes (Qiagen) − and stored at 80°C. RNA was isolated using the PAXgene Blood RNA Kit (Qiagen), and the integ- rity and concentration of RNA were determined by the RNA 6000 Nano Kit and a Bioanalyser 2100 (Agilent Technologies, USA). Reverse transcription was performed by the high-capacity cDNA Reverse Transcription kit (Applied Biosystems, USA) taking μ maximum 2 g of purified RNA in a total volume μ of 20 l. IL-6 expression was determined by qPCR using TaqMan Gene Expression Assays (Applied biotechnology) in duplex reactions of FAM-labeled IL6 primers (Hs00985637_m) and VIC-MGB- labeled ACTB (beta actin) endogenous control (limited) primers (4326315E). First validation experiments were carried out checking for equal efficiency of the primers in single and duplex reactions with varying cDNA dilutions and by

The 16 genes were chosen as part of a large candidate gene study based on comprehensive literature/data base searches for candidate longevity genes, candidate longevity SNPs, and tagging SNPs. The details of this selection are described in the Supplementary Material

subsequently determining the dynamic range by μ standard curves. Consequently, 20 l reactions in- μ μ cluding 1 l of each primer and 4 l cDNA sam- ples diluted 1:10 were made in triplicates applying the standard protocol of the StepOnePlus real-time PCR system (Applied biotechnology). Thresholds established in the validation experiments were ap- plied, the real-time curves were confirmed, and in case of C values off range, such samples were t excluded.

time of death was put at December 31, 2000, when half of the males and one third of the females in cohort had died. Accordingly, the early/late death 0 was defined as early time of death between (date at intake) and December 31, 2000 (i.e., 0 living to maximum age 95) and late time death after January 1, 2001 (i.e., individuals surviv- to age 95+). Fourteen of the 1089 oldest-old indi- were alive by January 1st 2010 (the recent Their remaining life expectancies were im- using www.mortality.org holding cohort mortal- data for the Danish population (based on data from Statistics Denmark). As a second survival variable, employed the number-of-days-lived variable de- as the exact number of days lived from date of in 1998 to either death or the imputed date. The of normal distribution and equal variance tested in the STATA 11.1 statistical program Corporation, College Station, TX, USA) by – – – Wilk and Breusch Pagan/Cook Weisberg and since the variable did not demonstrate nor- distribution, it was transformed to the square root a better fit. Association analysis was conducted by (number-of-days-lived variable) or logistic (ear- death variable) regression in Plink, adjusting sex and age or stratifying by sex, while adjusting age and applying additive, recessive, and dominant

Statistical analysis

quencies (CCA) and genotype frequencies (CCG), – as well as case control comparisons of haplotype frequencies (CCH) were performed for the Danish cohorts using the Plink statistical software (http:// pngu.mgh.harvard.edu/purcell/plink; Purcell et al. 2007). CCH was analyzed by regression analysis adjusting for sex. Haplotypes were defined by a sliding window of three SNPs (only considering haplotypes with frequency >5%). In sex-stratified – CCG analysis, first an additive model (Cochran Armitage test) and secondly recessive and dominant models were applied. To adjust CCG for sex, when analyzing both genders combined, CCG was ana- lyzed using a generalized linear model in the R software (http://www.r-project.org). Again additive, recessive, and dominant models were applied. The German replication data were analyzed in the same way for CCAs and CCGs. Finally, gene-based analysis was conducted in Plink by set-based analyses for each gene, calculating a mean of the single-SNP P values taking into account linkage disequilibrium (LD) be- tween the SNPs. The P-set value was corrected for multiple testing for the number of SNPs in each gene