We have conducted an analysis using AAD as a quan- titative 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 com- paring the low and high extremes of the quantitative trait (Plomin et al., 2009). Increasing statistical power by including more samples is imperative to elucidate genu- ine genetic associations in our study. Including more samples with the 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 re- quired to confirm these associations.

Domestic and international collaborations are often re- quired 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 interco- hort differences could create spurious genome-wide signif- icant associations. More importantly, these SNPs may pass all conventional QC filtering (e.g., Hardy-Weinberg equi- librium p value, minor allele frequency, and genotyping rate threshold) increasing the likelihood of generating false pos- itive results, which are not corrected for by principal com- ponent analysis.

Genetic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD1) Collaborators

man, Valentina Moskvina, Richard Abraham, Paul Holling- worth, Marian Hamshere, Jaspreet Singh Pahwa, Kimberley Dowzell, Amy Williams, Nicola Jones, Charlene Thomas, Alexandra Stretton, and Angharad Morgan, Medical Re- search Council (MRC) Centre for Neuropsychiatric Genet- ics and Genomics, Neurosciences and Mental Health Re- search Institute, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK; Simon Lovestone, John Powell, Petroula Proitsi, and Michelle K. Lupton, Department of Neuroscience, Institute of Psychiatry, Kings College London, London, UK; Carol Brayne, Institute of Public Health, University of Cam- bridge, Cambridge, UK; David C. Rubinsztein, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK; Michael Gill, Brian Lawlor, and Aoibhinn Lynch, Mercer’s Institute for Research on Aging, St. James’ Hospital and Trinity College, Dublin, Ireland; Kevin Mor- gan, and Kristelle Brown, Institute of Genetics, Queen’s Medical Centre, University of Nottingham, Nottingham, UK; Peter Passmore, David Craig, Bernadette McGuinness, and Stephen Todd, Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK; Clive Holmes, Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK; David Mann, Clinical Neuroscience Research Group, Greater Manchester Neurosciences Centre, University of Man- chester, Salford, UK; A. David Smith, Oxford Project to Investigate Memory and Ageing (OPTIMA), University of Oxford, Level 4, John Radcliffe Hospital, Oxford, UK;Seth Love, and Patrick G. Kehoe, Dementia Research Group,

As shown in Figs. 4 and 5, ignoring comparison of data from different centers (Mayo and WashU) would give spu-  rious genome-wide significant associations (rs4110518, p 5.96 Ooy X 10(cid:3)9 and rs2944476, p  2.19  10(cid:3)8). Therefore, an extra caution should be made when performing GWAS analysis which utilizes data from multiple centers.

Including center as a covariate has been widely used to solve such problems raised by centers and this is largely effective. However this is not always possible, especially in circumstances where the number of cases and controls are significantly different between centers. In our study, cor- recting for center was not possible due to the AAD bias in centers sampled. The overall spread of AAD is crucial to our analysis and the difference in allele frequency between individuals with relatively young AAD (Mayo) and rela- tively old (WashU) may well represent genuine associa- tions.

Furthermore, we have employed samples from both LOAD cases and controls in this study which is intended 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 to be affected by different environmental and genetic factors in addition to

GERAD1 acknowledgements