For replication of findings from the Danish longitudinal study, the Dutch Leiden 85-plus Study was used. This prospective cohort was ini- tiated in 1997, when all 85-year-old inhabitants of the city of Leiden in The Netherlands were invited to participate. The sample used here con- sists 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). Mean follow up time for survivors is 10.35 years (range 9–11 years). The Medical Ethical Committee of the Leiden University MedicalCentreapproved the study. All participants gave their informed consent.

Here we investigatethe association of common genetic variation in 148 genes composing the GH/IGF-1/INS signaling, DNA damage sig- naling and DNA repair and pro/antioxidant pathways with human longevity. The study populations are a cohort of Danes born in 1905 and followed from age 93 years until death, as well as a group of middle-aged controls. This provides a unique opportunity to investi- gate these variations in key candidate genes of human longevity using both case–control and longitudinal designs. Further, the SNPs identified in the case–control study are explored in the longitudinal data in order to investigate whether these SNPs also pose effects on survival during old age, and the SNPs identified in the longitudinal study are examined in the case–control data to investigate whether these SNPs also pose effects on survival from middle age to old age.

2.2. Selection of candidate genes

A comprehensive literature and data base search was conducted for the identification of longevity candidate genes in the GH/IGF-1/ INS signaling, DNA damage signaling and repair and pro/antioxidant pathways. The following databases were used: www.ncbi.nlm.nih. gov (/sites/entrez,/OMIM,/sites/entrez?Db=gap), www.genecards. org, www.hgvbaseg2p.org, genomics.senescence.info and genetic associationdb.nih.gov using the search terms ‘human longevity’,

Finally, considering the importance of replicating initial results in genetic association studies, the SNPs found to be significant in the case–control study are tested for association in a German case– control sample (1613 long-lived individuals and 1104 controls), while the findings from the Danish longitudinal study are tested in the prospective Dutch Leiden 85-plus Study (563 individuals).

quality of the genotype data, SNPs known to perform poorly on the Illu- mina GoldenGate genotyping platform were excluded. In total, 1415 SNPs were chosen and 1273 were successfully genotyped.

‘human aging’, ‘age related disease’ (including specific diseases e.g. myocardial infarction) and ‘premature aging syndromes’. Additionally, genes previously identified through animal models were considered. In order to cover the core biological functions of the three candidate pathways, the needed genes were chosen using the www.ncbi.nlm. nih.gov/sites/entrez, www.biocarta.com, www.reactome.org, www. genome.utah.edu/genesnps and the www.genome.jp/kegg/pathway. html databases. In total 152 genes composing the three candidate pathways were identified, 148 of which remained after cleaning of the GoldenGate genotyping data (see below). The 148 genes, divided by

2.4. Genotyping and data cleaning

and Micro Kits (Qiagen, Düsseldorf, Germany). The genotyping was conducted using the Illumina GoldenGate platform (Illumina Inc, San Diego, CA, USA) anddatacleaning wascarried out according totheman- ufacturer's recommendationsusingthe GenomeStudio software(http:// www.illumina.com/software/genomestudio_software.ilmn); primary exclusion criteria were sample call rateb90% and SNP call frequencyb 90%. Subsequently, SNPs clustering in the following categories were manually evaluated: a call frequency 90–95%, close SNP clusters (scoreb2.3), clusters with low intensity (scoreb0.2 or >0.8), cluster having a heterozygote cluster shifting towards a homozygote cluster (scoreb0.13), SNPs with excess heterozygosity (scoreb−0.3 or >0.2) and SNPs located on the X chromosome (considering male hemizygos- ity). In total, 142 SNPs and 175 DNA samples were excluded leaving 1825 individuals, 148 candidate genes and 1273 SNPs for data analysis. Furthermore, 24 samples were included twice in order to investigate the intra-plate reproducibility and 48 samples were included twice to test the inter-plate reproducibility. Data showed an intra-plate repro- ducibility of 99.4% and an inter-plate reproducibility of 96.8%.

2.3. Selection of SNPs

The chromosomal regions composing the 152 candidate genes were identified through the www.ncbi.nlm.nih.gov (/gene and /mapview) and genome.ucsc.edu/databases, using the same gene ID, Sequence Ac- cession IDs, NCBI Reference Sequence, assembly build 36 and SNP build 129. If more than one isoform of a gene transcript existed, the longest one was chosen. For each gene, 5000 bp upstream and 1000 bp down- streamwere included. Ifanother genewaslocated within theseregions, only the region until theneighboringgene was included.Due to thesize of the genotyping platform, the large intron 2 of IGF1R and the large in- tron 2 of INSR were excluded.

Candidate SNPs were identified based on the following criteria: SNPs previously identified in genetic association studies, coding SNPs and SNPs having potential functional impact (coding non- synonymous SNPs, SNPs located in potential splice sites or transcrip- tion factor binding sites and SNPs potentially inducing frame shifts or nonsense-mediated mRNA decay). For the identification of poten- tial functional SNPs, the www.ncbi.nlm.nih.gov/SNP, koreanbio.org/ Variome, snpper.chip.org, snps3d.org and manticore.niehs.nih.gov data bases were used. The identity of each SNP was checked by linking back to the NCBI data base.