,

SNPs common to all arrays and Ha

ap data (--indep-pairwise) was gen- erated. No

SNPs within this list had an LD r

value greater than

across sliding windows (window size of

SNPs and

SNPs to shift the window). This subset of SNPs was used by EIGENSTRAT (Price et al.,

) for the following calculations: A. to detect genetic outliers defined by any individual whose ancestry is at least

SD from the mean on

of the top

axes of variation; B. to calculate principal components (PCs), C. To generate a population stratification plot Ha

ap data were used as the reference dataset—we used a filtered version of Ha

ap data (European [CEU], Asian [CHB/JPT], Yoruba [YRI]) release

in PLINK binary format (.bed.bim and .fam) from the PLINK web site (Purcell et al.,

).

EIGENSTRAT was used to calculate the genomic con- Qa. trol inflation factor ( ). This was performed iteratively using the GWAS dataset “A” with AAD including between

to

PCs. Each calculation generated a  XN single value, thus a total of

values were gener- ated. The number of PCs to be included as covariates NY was determined when the lowest value was acquired  after comparison of all

values.

Extraction of PCs from EIGENSTRAT results.

Fig. 3. Box and whisker plot, showing 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.

taset “B” consisted of SNPs only on the Illumina 610 chip. The 2 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, as they can cause an inflation of the type I error rate. A box and whisker plot was drawn using StatsDirect (version 2.7.8) (Fig. 3). Only 2 centers (Mayo and NIMH) showed signif- icant differences in AAD range compared with the rest of the data. Therefore, we carried out 2 logistic regression tests using the WashU data as a control and the Mayo and NIMH data as cases. The test incorporated the top 6 PC (described previously) and AAD as covariates. For each comparison, a Q-Q plot of (cid:2)2 of observed versus expected p values was generated using “estlambda” function in GenABEL (version 1.6.4) (Fig. 4) (Aulchenko et al., 2007).

A. to detect genetic outliers defined by any whose ancestry is at least

SD from the mean on of the top

axes of variation; B. to calculate principal components (PCs), C. To generate a population stratification plot data were used as the reference dataset—we used filtered version of Ha

ap data (European Asian [CHB/JPT], Yoruba [YRI]) release

PLINK binary format (.bed.bim and .fam) from PLINK web site (Purcell et al.,

). EIGENSTRAT was used to calculate the genomic Qa. trol inflation factor ( ). This was performed using the GWAS dataset “A” with AAD between

to

PCs. Each calculation generated  XN single value, thus a total of

values were ated. The number of PCs to be included as NY was determined when the lowest value was  after comparison of all

values. Extraction of PCs from EIGENSTRAT results.

2.2. Quantitative trait analysis

Ten known LOAD susceptibility genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) were tested for association with human aging using the most significant SNPs found in the previous GWAS (Table 2). The best proxy (with the highest LD; using HapMap data) was used to inform the effect of SNPs if they were not present in our data.

Quantitative trait (QT) analysis of all SNPs was per- formed using multivariable linear regression (--linear) ad- justed for AD status, gender, and the top 6 PC. AAD was used as a continuous trait in this analysis thus giving max- imum statistical power. Manhattan plots were drawn using

After the above QC, dataset “A” consisted of SNPs common to both Illumina 300 and Illumina 610 chips, whereas da-

a) Mayo compared with WashU. b) NIMH compared with WashU        e47g 35 zea 38 8Ry °°0 5 10 15, 20Expected Expected

Fig. 4. Q-Q plot of (cid:2)2-(cid:2)2 p values to determine bias in single nucleotide polymorphism (SNP) frequencies observed in Mayo (a), and National Institute of Mental Health (NIMH) (b) versus Washington University (WashU) data. (a) Logistic regression (Mayo versus WashU samples) adjusted for the top 6 principal components (PCs) and age at death (AAD). Five SNPs (circled) showed significant bias in the Mayo compared with WashU data taking into account population stratification and AAD. (b) Logistic regression comparing NIMH data versus WashU data adjusted for the top 6 PCs and AAD. No bias was observed in NIMH compared with WashU. Solid line represents expected under null hypothesis, i.e., no difference (or no association); open circles represent data points; dashed line the fitted slope of all data points. The diagram was drawn using GenABEL in R (version 2.12.1).

  carried out using “Grouping Categorize” function in StatsDirect. For each of the LOAD susceptibility gene loci, the allele frequency was calculated and stratified by AAD cate- gory. The effect of the minor allele of each SNP involved in life span was as illustrated (Fig. 6).

Haploview (version 4.1) to visualize GWAS results (Fig. 5) (Barrett et al., 2005). A histogram of AAD from all indi- viduals that passed QC was drawn using StatsDirect soft- ware (version 2.7.8) (Fig. 1).

The separation into 5 AAD categories were 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