Characterizing various genetic and environmental factors influencing human life span is currently one of the world’s major scientific challenges (Jylhava and Hurme, 2010). To date, genome-wide association studies (GWAS) are one of the most widely used approaches for identifying common genetic variations associated with human diseases. It has been suggested that with increasing sample size, promising signals of association between human traits and genetic variants can be revealed (McCarthy, et al., 2008). Tan and Christensen have shown that increasing sample age from nonagenarians to centenarians further increases the power to discover variants associated with human aging (Tan et al.,

GenAge is a database of genes related to aging (genom- ics.senescence.info). To date there are over 250 genes that have been recorded by the GenAge database based on ex- tensive literature reviews. All of these genes have shown possible association with human aging (de Magalhães et al., 2009). Most of these genes play critical parts in a variety of biological pathways in humans, and a significant number of these genes ((cid:5) 100) are related to severe human diseases. It is generally believed that genes and biomarkers implicated in age-related diseases such as coronary artery disease (CAD), cerebrovascular disease (CVD), and Alzheimer’s disease (AD) have a role in successful aging (Panza et al., 2009). Identification of genuine aging genes may uncover “master genes” that increase our understanding of many age-related diseases.

The aim of this study was to (1) investigate whether the “known” late-onset Alzheimer’s disease (LOAD) genes play a role in human aging. This could address the question if these genes are directly associated with AD or indirectly by allowing successful aging; and (2) search for candidate genetic risk factors associated with human survival and aging, which may merit further study.

There are a number of biological pathways that have been reported important in human aging, including lipid/cholesterol metabolism [GO:0006629; GO:0008203] (APOE, PON1, CETP) (Barzilai et al., 2003; de Chaves and Narayanaswami, 2008; Efrat and Aviram, 2010), immune system processes [GO:0002376] (IL6 and IL10) (Jylhävä and Hurme, 2009), drug metabolism [KEGG:hsa00982] (GSTT1) (Glatt et al., 2007; Taioli et al., 2001), energy metabolism in mitochondria (SIRT3) (Polito et al., 2010), and insulin receptor signaling pathway [GO:0008286] (IGF1R, FOXO3A, KLOTHO) (Arking et al., 2005; Suh et al., 2008; Willcox et al., 2008).

2. Methods

Through collaborative efforts, we were able to draw on combined sample GWAS datasets and analyze subject-level genotype data from 1385 subjects (1047 LOAD cases and 338 controls) with documented age at death (AAD) (Table 1). The AAD histogram follows a normal distribution (Fig.  1), with mean AAD of 80.2 years of age (SD 8.9 years). All of these data were subject to subsequent quality control (QC) procedures and analysis. The data analysis was per- formed using PLINK software package (pngu.mgh.harvard. /~p edu/ purcell/plink) release 1.06 (Purcell et al., 2007).

Insights into human aging have been gained from study- ing model organisms. Extension of lifespan can be achieved by manipulating a few genes in laboratory animals, such as flies, worms, and mice (Kenyon, 2010). The insulin/insulin- like growth factor 1 (IGF-1) signaling pathway has a well- established role in influencing lifespan within model organ-

2.1. Datasets; pooling and QC

We have obtained data from 9 research centers, 3 from the USA and 6 from the UK (GERAD1 Consortium). The GERAD1 sample comprised up to 3941 AD cases and 7848

Table 1

Summary of sample information

Dataset Sample AD status Gender Genotyping Mean AAD Origin size (AD/controls) (male/female) chip (AD/controls) Mayo 434 220/214 246/188 Illumina 300 73.5/71.7 USA NIMH 46 46/0 12/34 Illumina 610 78.1/- USA WashU 332 294/38 140/192 Illumina 610 84.1/86.1 USA Belfast 235 213/22 99/136 Illumina 610 82.2/83.1 UK Bristol 59 43/16 21/38 Illumina 610 82.5/81.6 UK London 238 194/44 83/155 Illumina 610 86.1/83.0 UK Manchester 1 1/0 0/1 Illumina 610 79.0/- UK Nottingham 39 35/4 18/21 Illumina 610 83.6/79.5 UK Oxford 1 1/0 0/1 Illumina 610 79.5/- UK Pooled 1385 1047/338 619/766 — 81.5/76.1 —

Genome-wide association studies (GWAS) data were obtained from a total of 9 centers, 3 from the USA (NIMH, WashU, and Mayo) and 6 from the UK (Nottingham, Bristol, Manchester, Belfast, Oxford, and London). Sample size, number of cases and controls, and gender for each cohort are as indicated together with details of the mean age at death in years for cases and controls and the genotyping chip used in each study. “-” means the information is not applicable. Key: AAD, age at death; AD, Alzheimer’s disease; NIMH, National Institute of Mental Health; WashU, Washington University.

AAD, age at death; AD, Alzheimer’s disease; NIMH, National Institute of Mental Health; WashU, Washington University.

the National Institute of Mental Health (NIMH) AD Genetics Initiative. All AD cases met criteria for either probable (Na- tional Institute of Neurology and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (now known as the Alzheimer’s Association) [NINCDS-ADRDA], Diagnostic and Statistical Manual of Mental Disorders, 4th Ed [DSM-IV]) or definite (Consortium to Establish a registry for Alzheimer’s Disease [CERAD]) AD. All elderly controls screened for dementia using the Mini Mental State Examination (MMSE) or Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog), were deter- mined to be free from dementia at neuropathological examination or had a Braak score of 2.5 or lower. All studies used the Illumina 610 QuadChip, except the Mayo study which used the Illumina HumanHap300 chip (Carrasquillo et al., 2009).