pair was included in the study, resulting in a total of 1320 participants. The study was conducted to investigate genetic and environmental factors influencing age-related diseases and traits in middle-aged Danes. The participants were followed up in 2002 and 2007, and the vital status was obtained from the Danish Civil Registration System. 

3. Results

In this study, we examined the association between human longevity and common genetic variations in three major pathways: GH/IGF-1/insulin signaling, DNA damage signaling and repair, and pro/antioxidant pathways. We analyzed 1273 tagging SNPs in 148 genes belonging to these pathways. After correcting for multiple testing, we found rs9456497 (IGF2R) and 5 DNA repair SNPs (rs11571461 (RAD52), rs13251813 (WRN), rs1805329, rs2953983 (POLB), rs3211994 (NTLH1)) to be associated with longevity. 

In a longitudinal study with a follow-up of 11 years on survival in the oldest-old Danes, we found 2 pro/antioxidant SNPs (rs10047589 (TNXRD1), rs207444 (XDH)), 1 GH/IGF-1/INS SNP (rs26802 (GHRL)), and 3 DNA repair SNPs (rs13320360 (MLH1), rs2509049 (H2AFX), and rs705649 (XRCC5)) to be associated with mortality in late life. 

When examining the 11 SNPs from the case-control study in the longitudinal data, rs3842755 (INS), rs13251813 (WRN), and rs3211994 (NTHL1) showed the same directions of effect (p<0.05). Rs9456497 (IGF2R) and rs1157146 (RAD52) showed non-significant tendencies, indicative of effects also in late-life survival. In addition, rs207444 (XDH) presented the same direction of effect when inspecting the 6 SNPs from the longitudinal study. 

No formal replications were observed when investigating the 11 SNPs from the case-control study in 1613 oldest-old (age 95-110) and 1104 middle-aged Germans, although rs11571461 (RAD52) did show a supportive non-significant tendency (OR=1.162, 95% CI=0.927-1.457). The same was true for rs10047589 (TNXRD1) (HR=0.758,95%CI=0.543-1.058) when examining the 6 SNPs from the longitudinal study in a Dutch longitudinal cohort of oldest-old (age 85+, N=563). 

In conclusion, this candidate gene-based association study, the largest to date applying a pathway approach, not only points to potential new longevity loci but also underlines the difficulties of replicating association findings in independent study populations and thus the difficulties in identifying universal longevity polymorphisms. 

4. Introduction

Genetic factors contribute approximately 25% to variation in human lifespan (Herskind et al., 1996), with an effect thought to be minimal before age 60 years and most profound from age 85 years and onwards (Hjelmborg et al., 2006). Candidate longevity genes encode proteins involved in several biological processes, including lipid protein metabolism (APOE being by far the most validated longevity gene to date), GH/IGF-1/INS signaling, DNA damage signaling and repair, and pro/antioxidant pathways (Christensen et al., 2006). The genes composing these pathways encode proteins involved in processes such as energy metabolism, growth and differentiation, and the maintenance of genetic stability (including telomere stability), and protection against free radicals produced by the general metabolism. 

These are all processes believed to be of key importance for aging and longevity from nematodes to humans (reviewed in e.g. Burtner and Kennedy, 2010; Cluett and Melzer, 2009; Harman, 1973; Kenyon, 2010; Kuningas et al., 2008). Studies using various animal models have found that either depletion or over-expression of some of these candidate genes affect the lifespan of the organism (e.g. GH, GPX1, FOXO, KL, GHRHR, POLB, RAD52, INS and TP53 (Bartke, 2005; Cabelof et al., 2006; Flurkey et al., 2001; Giannakou et al., 2004; Holzenberger et al., 2003; Kawano et al., 2000; Kuro-o et al., 1997; Kurosites, SNPs located in regulatory regions such as promoters or enhancers). In addition, tagging SNPs were included to capture the genetic variation in the chromosomal regions of interest. Tagging SNPs were chosen using the Tagger algorithm implemented in Haploview version 4.2 (Barrett et al., 2005) with pairwise tagging, a minor allele frequency threshold of 5% and a r2 threshold of 0.8. In total, 1415 SNPs were selected for genotyping.

2.4. Genotyping and data cleaning
For genotyping, DNA was extracted from whole blood using standard procedures. The genotyping was performed using the Illumina GoldenGate platform (Illumina Inc, San Diego, CA, USA) and data cleaning was carried out according to the manufacturer's recommendations using the GenomeStudio software (http://www.illumina.com/software/genomestudio_software.ilmn). Primary exclusion criteria were a sample call rate of less than 90% and an SNP call frequency of less than 90%. Subsequently, SNPs clustering in certain categories were manually evaluated: call frequency between 90-95%, close SNP clusters (score less than 2.3), clusters with low intensity (score less than 0.2 or greater than 0.8), clusters having a heterozygote cluster shifting towards a homozygote cluster (score less than 0.13), SNPs with excess heterozygosity (score less than 0.3 or greater than 0.2) and SNPs located on the X chromosome (considering male hemizygosity). A total of 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 intra-plate reproducibility and 48 samples were included twice to test inter-plate reproducibility. Data showed an intra-plate reproducibility of 99.4% and an inter-plate reproducibility of 96.8%.