Genotyping, imputation and genome-wide association analysis

Sex-stratified analysis

Alldiscoverystudieswere genotyped usingIllumina genotyping arrays, and pre-imputation quality control was performed for each study separately. Imputation was performed using + IMPUTE or MACH with reference HapMap Phase I II CEU release 22 (hg18/build36). Further details about the genotyping, quality control and imputation of each study are summarized in Supplementary Material, Table S2.

Sex-stratifiedanalysisofthecasesaged 85years(n ¼ 5400 women and n ¼ 1865) was performed to investigate the presence of men gender-dependent associations. In addition, the 15 loci that showed (suggestive) evidence for association with survival ≥ 85 and/or ≥ 90 years were tested for differences between sexes using for differences betweenLOOT

the formula: ( b − b ) / SE2 + SE2 . The results women men women men of this analysis are depicted in Supplementary Material, Table S4.

study II) were also genotyped using Illumina genotyping arrays and imputed using IMPUTE with reference HapMap + Phase I II CEU release 22 (hg18/build36) (Danish longevity study II) or deCODE software (deCODE). The other replication studies were genotyped with the Sequenom MassARRAY system using iPLEX Gold genotyping assays (Sequenom, San Diego, CA, USA). More information about the studies used in the replication phase can be found in Supplementary Material, Tables S1 and S2. Of the 15 SNPs measured with the Sequenom MassARRAYsystem,13were successfully genotypedinatleast 95% of the samples and the average genotyping call rate was 99.80%. We also checked the concordance between the SNPs measured with the Sequenom MassARRAY system and (imputed) GWAS data of the Leiden 85-plus study I cases, and the average concordance rate was 99.07%. The two SNPs that were not successfully genotyped with the Sequenom MassAR- RAY system (rs10957550 and rs4420368) were only analyzed in the replication studies, which had imputed GWAS data avail- able (deCODE and the Danish longevity study II).

Prospective analysis

Prospective analysis of rs2149954 and rs4420638 was per- formed using a Cox proportional hazards model adjusted for age at baseline, sex and study-specific covariates. The details about each of the analyzed cohorts are summarized in Supple- mentary Material, Table S5.

Pathway analysis

For the pathway analysis, we used GSEA implemented in MAGENTA (http://www.broadinstitute.org/mpg/magenta/) (33). In short, each SNP is mapped to a gene considering a window of 110 kb upstream and 40 kb downstream around the genes. Subsequently, each gene is assigned a gene association score based on the SNP with the lowest P-value, which is mapped to that gene and this score is adjusted for confounding

FUNDING

factors likegenesizeandtheamountofSNPsperkb.Geneswithin theHLAregionwereremovedfromanalysisowingtohighLDand high gene density in that region. The GSEA algorithm tests for over-representation of adjusted gene scores in a given pathway using a pre-defined score rank cutoff (in our case, the 95th and 75th percentile). The generated statistic is then compared with 10 000–1 000 000 gene sets of identical size randomly sampled from the genome to generate an empirical P-value for each path- way. In total, 3216 pathways from Gene Ontology, PANTHER, Ingenuity, KEGG, REACTOME and BIOCARTA were tested. Pathways were considered significant if the FDR-adjusted P-value (the 95th or 75th percentile) was ≤ 0.05.