2009 (http://www.genome.gov/26525384), we reviewed 266 GWASs, 69 of which reported on associations with coronary artery disease, heart failure, cancer, and type 2 diabetes. These 69 GWASs reported 23 disease-associated loci resulting from at least 2 independent GWASs, harboring 81 associated SNPs with P < 10−4 (Table S1). For each locus, the most replicated SNP was selected from the 81 SNPs, and, subsequently, in case of an equal number of replications, the SNP with the lowest reported P value was selected. For 5 loci, 9 further disease-associated SNPs were selected that were in low to moderate linkage disequilibrium (r2 < 0.80) with the most replicated SNP. Thus, 32 SNPs were selected to be genotyped with the Sequenom iPLEX. Because 2 SNPs failed in genotyping, 30 SNPs covering 22 loci were studied. Seventeen loci are represented by 1 SNP, 3 loci are represented by 2 SNPs, 1 locus is represented by 3 SNPs (8q24.21), and 1 locus is represented by 4 SNPs (9p21.3).

In our study, we detected the odds of the carriers with the – highest number of risk alleles, with an OR of 0.99 (95% CI: 0.97 1.03), to survive into old age. The boundaries of the 95% CI in this analysis are thus close to unity, which was also the case for the boundaries of the OR per extra risk allele. Although, in general, the association of disease risk alleles with longevity remains to be investigated (if all common and uncommon dis- ease risk alleles are discovered and tested for their association with human longevity accounting for their interaction with en- vironmental factors), our data suggest that longevity is not likely fi determined by the absence of GWAS-identi ed risk alleles for cardiovascular disease, cancer, and type 2 diabetes.

fi Because the current GWAS-identi ed SNPs have a relatively – low predictive value for disease risk (24 26), it was expected that such risk alleles only marginally affect population-wide survival. However, the cumulative effect of cardiovascular disease, cancer, and type 2 diabetes risk alleles, which was expected to be more pronounced (9, 10), did not affect population survival. The ex- tent to which disease risk attributable to the cumulative effects of susceptibility alleles is associated with population-based mor- tality risk should be tested in patient-based studies. Alternatively, other genetic and environmental factors present in individuals surviving into old age may counteract the detrimental effects of disease susceptibility alleles.

Genotyping. Genotyping of the selected SNPs was performed using the Sequenom MassARRAY iPLEXGold. The average genotype call rate for genotyped SNPs was 96.3%, and the average concordance rate was 99.7% among 4% duplicated control samples. All 30 SNPs were in Hardy–Weinberg equilibrium (P ≥ 0.002) among the controls.

Statistical Analysis. Single SNP analysis meta-analysis. We applied a variance- modified Cochrane–Armitage test to take into account the relatedness be- tween the highly aged sibling cases when computing the variances of the scores (29). In the meta-analysis of both the LLS and Leiden 85 Plus Study/ NTR controls, the score statistics of the two studies were combined using

a fixed-effects approach. Cumulative SNP analysis. For each individual who was genotyped for all 30 SNPs, the total number of disease risk alleles was counted. The difference in thenumberofdiseaseriskallelesbetweenlong-livedsubjectsandpopulation controls was assessed by linear regression. In these analyses, the number of riskalleleswasthedependentvariableandthecasestatuswasincludedinthe modelasacategoricalvariable.Totakeintoaccountdependencieswithinthe long-lived sibships of the LLS, robust SEs were used [i.e., the variance was computed from the between-family variation (30)]. P values were also based on these robust SEs.

Materials and Methods

Study Populations. All participants of the LLS, Leiden 85 Plus Study, and NTR provided written informed consent after receiving an explanation of the nature and consequences of the study.

LLS and Control Samples. For the LLS, nonagenarian siblings of European de- scent were recruited together with their offspring and the partners of the offspring, who served as population controls. Families were recruited if at least two long-lived siblings were alive and fulfilled the age criterion of 89 y or older for men and 91 y or older for women, representing less than 0.5% of the Dutch population in 2001 (1). In total, 944 long-lived proband siblings with a mean age of 94 y (range: 89–104 y), 1,671 offspring with a mean age of 60 y (range: 39–81 y), and 744 partners with a mean age of 60 y (range: 36–79 y) were included.

Logistical regression was performed with control/long-lived status as the outcome, study and gender as covariates, and 10% upper or lower tail as a variable. For the estimation of the OR per extra risk allele, logistical re- gression was performed with control/long-lived status as the outcome, study and gender as covariates, and the sum of risk alleles as a variable. Analyses were performed using the software package STATA/SE 11.0 (DPC Software). Interaction analysis. Polychotomous regression (31) and logic regression (32) were applied to the datasets of the LLS and Leiden 85 Plus/NTR controls to identify subsets of SNPs that might have a joint effect on longevity. Cross- validation was used for selecting the best subset of SNPs and the best functional forms of those subsets. The two approaches are different in that the polychotomous regression is flexible in terms of inheritance modes of the SNPs, although only pairwise interactions are considered, whereas the logic regression is used to identify multiple interacting dominant and re- cessive effects of SNPs.