Second Discovery Stage Genotyping Methods

an iPLEX genotyping assay developed for use with the MassARRAY platform (Sequenom, Inc., San Diego, CA) (42). The iPLEX genotyping assay is based on mass spec- trometry and enables genotyping of 25–36 custom SNPs on a sample in a single reaction. For the purposes of quality control, the system first automatically calls the genotypes and then generates cluster plots for all SNPs that are in- spected individually by experienced technicians who check whether the plots show clear separation of the genotype clusters. There were two SNPs that did not pass quality con- trol and two SNPs where no heterozygotes could not be de- tected; thus, lack of Hardy–Weinberg equilibrium was the quality control. Negative controls were included in the genotyping procedure (8 per 384-well plate), and impor- tantly, 4% of samples were genotyped twice to confirm re- producibility (reproducibility was ≥99.7%).

Results

Table 1 provides the characteristics of the persons achiev- ing longevity and the comparison group in each of the four CHARGE discovery cohorts at the time of DNA collection. In line with the design of the study, persons achieving lon- gevity were 10–20 years older than participants in the com- parison group at baseline and were more likely to be women. Between 45% and 83% of those achieving longevity were still alive at the time that longevity status was ascertained. Among those who had died, the distributions of causes of death differed between those achieving longevity and the comparison group. Whereas 6%–12% of those achieving longevity died of cancer, more than 30% of the comparison group had death attributed to cancer. The prevalence of dia- betes and a history of ever smoking were higher in the com- parison group than in persons achieving longevity. The baseline prevalence of other cardiovascular risk factor lev- els showed substantial overlap between the two groups.

Statistical Analysis

Using logistic regression, each imputed and observed HapMap SNP was tested for association with the longevity outcome using an additive genetic model adjusting for sex. The mean dosage of one of the alleles (a value between 0 and 2) was the predictor for imputed SNPs. The CHS addi- tionally adjusted for field study site in the regression model, and the FHS used generalized estimating equations to ac- count for familial correlations. We used the ratio of observed to expected variance in the imputed SNP genotype counts as a quality control metric for imputed SNPs (43). This ra- tio, multiplied by the sample size, is an estimate of the ef- fective sample size. In the imputation software MaCH, this ratio is called r2 as it is an estimate of the allelic correlation between the imputed genotypes and the true genotypes for the SNP. A total of 2,287,520 SNPs that had average minor allele frequency greater than 0.01 and were genotyped or imputed in all studies with variance ratio greater than 0.1

The genomic control inflation factor lambda (l ) for GC each cohort was less than 1.05 (45). After meta-analysis, overall inflation of the meta-analysis p values was minor (l = 1.034; Figure 1). None of the SNP–longevity asso- GC ciations achieved the prespecified level of genome-wide significance of p < 5 × 10−8 (Figures 1 and 2). There were 273 SNP associations with meta-analysis p < 10−4, and of these, 7 SNP associations had p < 10−5 (Supplementary Table 2). Under the null hypothesis that there are no asso- ciations in the genome, we would expect 0.0001 × ~2.3 mil- lion = ~230 hits. Table 2 shows the top 24 independent SNPs associated with longevity along with the number of supporting SNPs (additional SNPs with linkage disequilib- rium r2 > .80 and p < 10−4). Thus, for example, there were

Table 1. Characteristics of Longevity Cases and Comparison Group at DNA Collection

CHS Framingham Heart Study Rotterdam Study AGES-Reykjavik Survival Age to Comparison Survival Age to Comparison Survival Age to Comparison Survival Age to Comparison Characteristic, M (SD) or % >90 y, n = 557 Group, n = 544 >90 y, n = 362 Group, n = 355 >90 y, n = 773 Group, n = 934 >90 y, n = 144 Group, n = 122 Age at DNA draw, y 79.6 (4.5) 69.5 (3.0) 87.3 (3.8) 66.5 (6.9) 83.7 (5.53) 66.5 (5.37) 88.0 (2.4) 73.8 (3.2) Women, % 61 54 70 34 79 41 56 43 Alive, % 45 0 36 0 33 0 83 0 Cause of death* CVD, % 39 33 22 23 34 32 48 39 Cancer, % 10 40 9 45 6 39 12 38 Other, % 50 27 57 25 52 27 40 23 Unknown, % 0.3 0.2 12 6 7 2 0 0 Body mass index, kg/m2 25.5 (3.9) 26.6 (5.2) 26.0 (4.1) 28.0 (5.5) 26.8 (3.81) 26.3 (3.75) 25.9 (4.0) 27.4 (4.7) Ever smoker, % 40 70 54 81.0 29 43 49.3 80 Hypertension, % 57 53 68 75 40 40 83 80 Diabetes, % 8 20 8 22 6 8 8 11 Total cholesterol, mg/dL 210.5 (40.2) 212.2 (38.7) 198.8 (38.1) (204.7 (47.1) 248 (49.4) 254 (46.8) 207.6 (44.3) 224.44 (42.7)