Among the genes that were highlighted through the IPA analysis in the analysis of time to event was MYC (v-myc myelocytomatosis viral oncogene homolog)—a multifunc- tional, nuclear phosphoprotein that plays a role in cell cycle progression, apoptosis, and cellular transformation. MYC functions as a transcription factor that regulates transcrip- tion of specific target genes. Second in line were E2F1 (E2F transcription factor 1), EGFR (epidermal growth factor re- ceptor), and CEBPA (CCAAT/enhancer binding protein [C/ EBP], alpha). EF21, a transcription factor, plays a crucial role in the control of cell cycle and action of tumor sup- pressor proteins can mediate both cell proliferation and p53-dependent/independent apoptosis. EGFR is a trans- membrane glycoprotein that serves as a receptor for mem- bers of the epidermal growth factor family and supports cell proliferation. CEBP-Alpha, a bZIP transcription factor, can bind as a homodimer to certain promoters and enhancers. CEBPA also forms heterodimers with the related proteins CEBP-beta and CEBP-gamma and modulates the expres- sion of leptin, interacts with CDK2 and CDK4, and thereby inhibits these kinases and causes growth arrest in cultured cells.

Third, our study is based on common genetic variants and therefore we cannot exclude effects due to low fre-  quency and rare variants ( 5%) or due to the presence of structural variation, such as copy number polymorphisms. Our discovery set may lack the power to identify all the relevant loci, even though we had sufficient power to detect  common SNPs (minor allele frequency 5% or more) with a relative risk of 1.10 (Supplementary Table S2).

Last, we cannot exclude that phenotypic heterogeneity our findings. While all cohorts had prospectively information on major health events and diagnoses, in the methods of assessment and classifica- might have limited the ability to identify true effects.

4. Discussion

tion might have limited the ability to identify true effects. Complex diseases may result from the effects of a large number of low frequency variants, with substantial allelic heterogeneity at disease-causing loci (Pritchard, 2001; Pritchard and Cox, 2002; Swarbrick and Vaisse, 2003). Theoretical modeling that incorporates mutation, random genetic drift, and purifying selection suggests that many of the variants that affect complex traits may be in the 1%–5% frequency range (Pritchard, 2001). Indeed, sequencing of candidate genes in an attempt to capture such low frequency variants, has led to the identification of rare variants with modest effects on body mass index (Ahituv et al., 2007; Challis et al., 2002; Cone, 2000), triglyceride levels (Romeo et al., 2007), high-density lipoprotein (HDL; Cohen et al., 2004; Romeo et al., 2007) and low-density lipoprotein (LDL) cholesterol levels (Cohen et al., 2005, 2006; Ko- towski et al., 2006).

In our analyses of over 25,000 individuals of 55 years and older followed for an average of 11 years, we did not identify genome-wide significant associations for all-cause mortality and survival free of major diseases. However, both traits highlighted loci with suggestive significance that were in the neighborhood of genes related to neural regu- lation. In addition, our pathway and network analyses iden- tified an enrichment of genes associated with cellular and neural development and function, and cell communication that may contribute to variation in human aging. Brain development might be responsible for the creation of redun- dancy in brain circuitry, which is associated with functional reserve and resiliency. Brain function regulates most of the compensatory strategy supporting maintenance of homeo- static equilibrium. Both of these processes are essential to healthy aging and longevity.

Several explanations are possible for the lack of genome- wide significant findings. First, mortality is arguably 1 of the most complex phenotypes, and several trajectories to- ward extreme old age have been identified (Evert et al., 2003). Multiple genes could mediate the aging process but would have their effects through numerous different patho-

It is impossible to determine the functional variant of a gene by GWAS. Moreover, we cannot conclude from the location of an SNP that this variation is involved in the expression of the closest gene. However, our top results suggested a possible role of genes involved in neurological

genes that were involved in most pathways indeed had substantial effects within the analyzed trait. NTRK2 (Rico et al., 2002), NCAM1 (Rutishauser et al., 1988), GRID2 (Hirai et al., 2003), and RIMS1 (Johnson et al., 2003; Schoch et al., 2002) are associated with neuronal development and disease pathways that were highlighted in the analysis of time to death. MYC (Cole, 1986; Goga et al., 2007), E2F1 (Nevins, 2001), EGFR (Wang et al., 2004), and CEBPA (Ménard et al., 2002; Wang et al., 2001) are associated with “cancer,” “cell function,” and “development” pathways.