3. Results 

3.1 Dataset composition and QC 
The full range of AAD (58-108 years) was separated into five AAD categories, the boundaries of which were selected to ensure each group contains an approximately equal number of samples. This ensures SNP minor allele frequencies across each of the age categories are directly comparable. This was done because the combined GWAS dataset has a sample size of 1385 before QC. After QC, four participants were removed for a low genotyping rate (--mind 0.05) from the Mayo GWAS (Carrasquillo et al., 2010). A single sample from Bristol was removed due to discrepancies between AAD and age at onset (AAO). An additional 16 samples were removed as genetic outliers by PCs analysis using EIGENSTRAT. This included 14 samples from the Mayo data, one from NIMH, and one from Belfast. The mean AAD in the pooled dataset (post QC) was greater than 80 years of age.

The multidimensional scaling (MDS) plot showed three distinct clusters. As expected, each cluster represents different population ancestry: CEU, middle right; CHB/JPT, bottom left; and YRI, top left (Figure 7). UK, USA, and HapMap_ CEU samples formed a single cluster. On closer inspection, slight deviation between UK and USA samples exists, and this was accounted for by including PCs as covariates. After stringent QC, there were 1364 samples (1031 LOAD cases and 333 controls, 608 male and 756 female) and 528,430 SNPs remaining for further analysis.

3.2 Analysis and results 
Genomic control inflation factor ((cid:1)) was calculated using EIGENSTRAT by iteratively including 0 to 10 PCs (Price et al., 2006). We found that including six PCs generated the lowest genomic control inflation factor ((cid:1) = 1.003). Assessment of the ten LOAD susceptibility genes yielded compelling evidence of association of APOE locus (rs2075650) with human aging (uncorrected p = 5.27 xX 10(cid:3)4), which withstood multiple testing after Bonferroni correction for ten independent tests (Table 3A).

Minor Allele Frequency (MAF) analysis was performed on ten late-onset Alzheimer's disease (LOAD) genes with respect to aging. Figure 6 shows the relationship between single nucleotide polymorphism (SNP) MAFs and human aging, where age at death (AAD) is separated into five categories. Each AAD category contains roughly equal amounts of samples to avoid bias in sample sizes. All ten known LOAD genes are shown together with the APOE locus highlighted in bold (all other loci in gray). The APOE locus (rs2075650) showed significant association with aging, with MAF = 0.27, AAD = 89 years of age (n = 1136), and MAF = 0.21, AAD > 89 years of age (n = 228). None of the other gene loci were significantly associated with aging.

In addition to examining the association of these ten LOAD susceptibility genes with aging, we performed a genome-wide analysis that includes all SNPs on the Illumina platforms after QC. The genome-wide significance threshold was calculated (p = 1.04 xX 10(cid:3)7) using Bonferroni correction for the number of independent tests (n = 483,066), which was estimated using the method we have previously described (Shi et al., 2010). There were 41 SNPs with p-value <= 5 xX 10(cid:3)5. These SNPs span the genome, representing 35 distinct signals (pairwise r2 < 0.8) across thirteen chromosomes. Twenty-four of them are located within 20 kilobase (kb) of known human genes with a widethat a sample size between 3000 and 15,000 is required to have 80% power to detect an association with a minor allele frequency (MAF) above 0.05, whereas approximately 4000 to 19,000 samples will be needed for 95% power. However, this estimation should be interpreted with caution as it is based on several assumptions, and gene-environment interaction has not been taken into account.

The analysis of the 10 documented late-onset Alzheimer's disease (LOAD) susceptibility loci with human aging showed no evidence of association with the remaining LOAD genes. This suggests that these genes are genuine LOAD genes with no detectable effects on human aging. However, it is still possible that these genes may have a weak effect on aging that our dataset was not large enough to detect.

It is important to note that advancing age is one of the most significant risk factors for LOAD, and the prevalence of LOAD increases with age. Therefore, understanding whether genes involved in LOAD play a role in successful aging is crucial. A longitudinal study following subjects for 18 years showed that the APOE gene affects the risk of mortality in individuals aged 75 and older. The risk was increased by 22% in those carrying the APOE ε4 allele, decreased by 28% in those carrying the APOE ε2 allele, and individuals carrying APOE ε3 allele showed no significant difference in risk.

The Manhattan plot shown in Fig. 5 represented a scenario before removal of five false positive SNPs. Comparing SNPs between NIMH and WashU data showed no significant difference in allele frequency, whereas five SNPs showed significant difference in allele frequency comparing Mayo data with the major allele A. No effect was seen for individuals carrying AG genotype.

It is not surprising that the two SNPs which showed genome-wide level of significance overlap with the five SNPs that showed significant bias between Mayo and WashU data, as the age at death of the Mayo data is significantly younger. It is not possible to correct for center, as the spread of the age at death is crucial in detecting genuine aging-associated variants. The difference in allele frequency due to samples with a young age at death in Mayo and an old age at death in WashU may well represent genuine associations. Including center as a covariate would abolish our ability to detect this effect.