In summary, stratification was tested using HapMap data #23 as reference, and the UK, USA, and HapMap CEU samples formed a single cluster. The analysis of SNPs showed their chromosome number, base pair position, gene name, p value, and direction of effect. The processed text has corrected spelling errors, split combined words correctly, removed redundancy and corrected grammatical errors where necessary.A much larger sample size is required to detect an association with common variants. With rare exceptions, common variants are known to exert only small to moderate effects, according to previous studies of many complex disorders and traits (Bodmer and Bonilla, 2008). Presenting our data on genes that lie between 10^5 and 10^8, while not reaching genome-wide significance, may enable groups to identify genes for future study, especially if there is overlap with other studies. Additionally, these data could be used as part of a larger meta-analysis.

In this study, we performed an association test of the top GWAS LOAD genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) with human aging using the most significant SNPs found in previous studies. Apart from the well-documented association between APOE and LOAD, the association with the other nine genes was identified recently through large GWAS, each with a sample size of over 10,000 (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al., 2011).

The results of our analysis provided compelling evidence of an association between the APOE locus (rs2075650) and human aging (p = 5.27 x 10^(-4)) (Table 3A) with a risk effect based on the analysis of 1364 samples using AAD as a continuous trait. The minor allele frequency plot (Fig. 6) shows that the MAF of this SNP significantly decreases in the old AAD category (MAF = 0.21; AAD >89 years of age; n = 228) compared with the other four younger AAD categories (MAF = 0.27; AAD <= 89; n = 1136). Individuals homozygous for the minor allele G showed significantly lowered AAD (p = 0.002) compared with individuals homozygous for the major allele.

GWAS provide a method of identifying common genetic variations associated with disease or phenotype in an unbiased manner. However, it comes with the price of multiple testing given that many thousands of SNPs are tested simultaneously, and a very stringent significance threshold (p = 5 x 10^(-8)) is often used to infer a genome-wide significant association and to avoid a large number of false positives (Bertram and Tanzi, 2008). Factors associated with aging have been addressed by correcting for AD status. However, it is possible that the APOE association with age in the AD group could be due to its involvement in the duration and severity of AD (Dal Forno et al., 2002). We do not, however, have clinical pathological features for these samples and therefore cannot explore these endophenotypes.

We have conducted an analysis using AAD as a quantitative trait; this is believed to provide more power compared with the traditional case/control approach. The advantage of statistical power gained compared with the case/control analysis is dependent upon the design of the study. For example, dichotomizing the AAD distribution into cases and controls would give less power than comparing the low and high extremes of the quantitative trait (Plomin et al., 2009). Increasing statistical power by including more samples is imperative to elucidate genuine genetic associations in our study. Including more samples with extreme phenotypes (e.g., exceptional longevity nonagenarian and centenarians) would give more power than addition of samples of average AAD (Plomin et al., 2009; Tan et al., 2010).

Considering that pure controls where individuals die without experiencing any age-related diseases probably do not exist, we considered it valid to undertake an analysis using both sets. However, due to the large number of AD cases that have been used relative to the number of controls (about three-quarters of the total), any association with human aging implicated in the study may be biased and specific to the AD population. We acknowledge this as a limitation. However, using just the control population would result in insufficient numbers for a valid analysis. Follow-up studies using only control samples will be required to confirm these associations.

Domestic and international collaborations are often required to raise sufficiently large sample sizes in order to have adequate power to detect genuine disease associations. This is especially true for SNPs with a small effect size. However, such combined analysis can, in some instances, generate new problems. For instance, interchip and intercohort differences could create spurious genome-wide significant associations. More importantly, these SNPs may pass all conventional QC filtering (e.g., Hardy-Weinberg equilibrium p-value, minor allele frequency, and genotyping rate threshold) increasing the likelihood of generating false-positive results, which are notIn this study, the goal is to achieve maximum power to detect novel aging-associated variants. Ideally, this test is better performed using only control samples, as the AD group is likely affected by different environmental and genetic factors in addition to aging.