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 Form Merged Data 3. This was then used in a principal components analysis, which revealed 16 individuals as genetic outliers. These were removed from Merged Data 2. Abbreviation: GR, genotyping rate. Fast, Oxford, and London, NIMH, and Washington University (WashU), where possible. All GWAS datasets were transformed into the same PLINK format (1 and 2 coding in PLINK.bed.fam and .bim format). Any samples that overlapped between GWAS datasets were removed. Each sample was checked individually for discrepancies between AAD and age at sampling (AAS). Samples with age at sampling greater than AAD were removed from further analysis. Polymorphisms (SNPs) present on the Illumina 300 chip enabled us to merge all the datasets. AAD information was extracted from the Mayo dataset (Carrasquillo et al., 2009) using the --keep and --make-bed command in PLINK. This was repeated for samples from the Alzheimer's Research UK (ARUK) data (which encompassed centers in Nottingham, Bristol, Manchester, Belfast, Centre London Oxford NIMH WashU Nottingham Mayo Manchester Bristol Belfast). Age at death (yrs) was removed from further analysis. Data merging was performed using --bmerge in PLINK in Consensus call mode. QC procedures were undertaken for the merged data to account for population stratification and differences in the Illumina chip versions. Data merging and QC procedures are illustrated in Fig. 2. The merged data were separated into two GWAS datasets (A and B) using the --geno 0.05 command in PLINK. For both of the GWAS datasets, the following QC procedures were carried out in order: SNPs with genotyping rate less than (--geno) were excluded from further analysis; SNPs with a minor allele frequency (MAF) less than (--maf) were excluded from further analysis. A list of SNPs with MAF between and was generated (--freq). Within this shortlist, SNPs with a genotyping rate less than (--geno) were excluded (--exclude) from further analysis. SNPs with a Hardy-Weinberg Equilibrium p-value less than (--hwe --hwe-all) were excluded from further analysis, irrespective of status (AD cases or controls). Individuals with a genotyping rate less than (--mind) were excluded from further analysis. Using the QC'd GWAS dataset A, a linkage disequilibrium (LD) pruned subset of 85,826 SNPs common to all arrays and HapMap data (--indep-pairwise) was generated. No SNPs within this list had an LD r-value greater than 0.2 across sliding windows (window size of 50 SNPs and 5 SNPs to shift the window). This subset of SNPs was used by EIGENSTRAT (Price et al., 2006) for the following calculations: A. to detect genetic outliers defined by any individual whose ancestry is at least 6 SD from the mean on one of the top 10 axes of variation; B. to calculate principal components (PCs); C. To generate a population stratification plot, HapMap data were used as the reference dataset. We used a filtered version of HapMap data (European [CEU], Asian [CHB/JPT], Yoruba [YRI]) release #27 in PLINK binary format (.bed.bim and .fam) from the PLINK website (Purcell et al., 2007). EIGENSTRAT was used to calculate the genomic control inflation factor (λ). This was performed iteratively using the GWAS dataset A with AAD including between 1 to 10 PCs. Each calculation generated a single value, thus a total of 100 values were generated. The number of PCs to be included as covariates was determined when the lowest value was acquired after comparison of all 100 values. Extraction of PCs from EIGENSTRAT results is shown in Fig. 3. The box and whisker plot show the age at death (AAD) distribution for each center. The central box represents the distance between the first and third quartiles with the median marked with a diamond. The circles indicate that an individual's AAD is outside 2 times the interquartile range. The dashed rectangle highlights that the majority of the data have a similar range of AAD with the exception of the National Institute of Mental Health (NIMH) and Mayo data. Dataset B consisted of SNPs only on the Illumina 610 chip. The two GWAS datasets were merged using the same methods (--bmerge) as described above (shown as merged data 2 in Fig. 2). Frequency due to interchip and intercohort differences can cause an inflation of the type I error rate. AStatsDirect. For each of the LOAD susceptibility gene loci, the allele frequency was calculated and stratified by AAD category. The effect of the minor allele of each SNP involved in life span is illustrated in Figure 6. Haploview (version 4.1) was used to visualize GWAS results in Figure 5 (Barrett et al., 2005). A histogram of AAD from all individuals that passed QC was drawn using StatsDirect software (version 2.7.8) in Figure 1. The separation into five AAD categories was used only to facilitate visualization of MAF of candidate SNPs in the different age ranges and was not used to generate p-values. Power calculations were undertaken using QUANTO version 1.2.4. The required sample size was estimated using an additive model created by the software.

3. Results