Finally, potential confounders such as smoking behavior were not adequately measured in most elderly cohorts, but none of the SNPs previously associated with smoking behavior in cohorts from European descent showed differences between cases (over eighty-five years) and controls in the joint analysis of the discovery and replication phase.Smoking behavior. However, as the frequency of these proxy SNPs for smoking behavior is similar between cases and controls, we expect no obvious differences in smoking behavior between the groups. (10,11) and is assumed to be determined by many genes (12,13). Furthermore, the genetic heterogeneity in ageing and lifespan regulation is expected to be high because individual genes may contribute by a diversity of late-acting deleterious stochastic (germline) variation resulting in a genetic component that is hard to disentangle (13). GWAS of complex late-onset diseases, such as osteoarthritis and Alzheimer's disease, with sample sizes comparable to our current study (4345), have identified more loci compared with GWAS of longevity. This most likely reflects the greater inherent complexity of the longevity trait, with its diverse spectrum of biological pathways subject to intrinsic and extrinsic (environmental) interactions. Hence, even larger GWAS (>50,000 long-lived individuals) may be required to identify additional longevity loci, preferably in the most stringent phenotype, i.e., the oldest old. APOE/APOC1 locus, we identified a novel locus on chromosome 5q33.3 that associates with survival beyond 90 years. Although rs2149954 is associated with survival beyond 90 years at a genome-wide significant level in our study, replication in additional cohorts from European as well as non-European descent is warranted. The minor allele of the lead SNP at this locus, rs2149954, promotes human longevity in a prospective meta-analysis by lowering the risk of mortality owing to stroke and non-cardiovascular causes. The locus harbors a lincRNA and is implicated in blood pressure regulation, but the mechanism by which it influences longevity likely also involves other traits. Western populations, the human longevity trait suffers from etiological heterogeneity. Lifespan extension in past generations owing to non-genetic factors likely created phenocopies diluting the genetic component of survival to ages 85 years. The genetic contribution to survival to ages 100 years is higher but will render smaller sample sizes for GWAS. This may explain MATERIALS AND All studies were analyzed separately using CC-assoc (https://www.msbi.nl/dnn/Research/Genetics/Software/TestsforGWAS inrelatedindividuals(cc_assoc).aspx), which is based on a modified version of the score test that takes into account imputation uncertainty and familial relatedness (46). SNPs with low imputation quality (R2 < 0.40) and a MAF of <1% or <5% (if T n ≥ 200) were excluded from analysis in the discovery cases phase. Adjustment for population stratification of the discovery studies was performed by multiplying the R2-adjusted variances T of the score statistic with the genomic inflation factor (λ range Study populations The discovery analysis was performed in 7729 cases that survived to ages 85 years (of which 5406 also survived to ages 90 years) and 16,121 controls below 65 years at baseline, from 14 studies. Replication was performed in 13,060 cases that survived to ages 85 years (of which 7330 also survived to ages 90 years) and 61,156 controls below 65 years at baseline, from six additional studies. All individuals were of European descent. The details of the discovery and replication studies can be found in Supplementary Material, Tables S1 and S2. Some cohorts only provided controls (GOYA, NTR, SU.VI.MAX, TwinsUK and WTCCC2) or only cases (BELFAST, CEPH centenarian cohort, Danish longevity study I/II, Leiden 85-plus Study I/II and Newcastle 85 Study), whereas others contained both (Calabria cohort, deCODE, EGCUT, GEHA Study, German longevity study, Leiden Longevity Study, Rotterdam Study I/II and TwinGene). The names of the studies in the tables and figures are based on the names of the cohorts containing the cases. The cases and controls used for each study originated from the same country (Supplementary Material, Table S1). The only exception is BELFAST (Northern Ireland), for which we used controls from the NTR (Netherlands). A check-in the PROSPER study, which includes individuals from Northern Ireland and the Netherlands, showed that the allele frequencies in control individuals from both countries are similar for our SNPs (data not shown). All participants provided written informed consent, and the study was approved by the relevant institutional review boards. Meta-analyses For the meta-analyses, a fixed-effect approach was used. Scores and variances of the studies were combined to obtain a single meta-statistic, which was adjusted using the genomic inflation factor (λ 1.019, discovery phase only) (Supplementary Material, Fig. S1). For(deCODE and the Danish longevity study II). Prospective analysis of rs2149954 and rs4420638 was performed 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 Supplementary Material, Table S5.