controls. A subset of this sample has been used in this study, comprising 3333 cases and 1225 elderly screened controls genotyped at the Sanger Institute on the Illumina (San Diego, CA, USA) 610-quad chip. These samples were recruited by the Medical Research Council (MRC) Genetic Resource for AD (Cardiff University; Kings College, London; Cambridge Uni- versity; Trinity College, Dublin), the Alzheimer’s Research Trust (ART) Collaboration (University of Nottingham; Uni- versity of Manchester; University of Southampton; University of Bristol; Queen’s University, Belfast; the Oxford Project to Investigate Memory and Ageing [OPTIMA], Oxford Univer- sity); Washington University, St. Louis, MO, USA; MRC PRION Unit, University College London, London; and the South East Region AD project (LASER-AD), University Col- lege, London; Competence Network of Dementia (CND) and Department of Psychiatry, University of Bonn, Germany; and

Number of Individuals200100566064687276 80 84 88Age at death (yrs)9296 100 104108

Fig. 1. Histogram plot representing the spread of age at death (AAD) of samples included in this study. The x- and y-axis represent AAD in years and number of individuals, respectively. This graph follows a normal distribution, with mean AAD  80.2 years (n  1385).

HAAHEEAEG, aMayo 8 CentresQuality Control i JSNP Genotyping Rate per SNP >0.95 Illumina 300 Illumina 610MAF > 0.01Westar Bley(600,489 SNPs,1385 subjects)Hardy-Weinberg Equilibrium p > 0.001 ——— SNP Genotyping Rate >0.99 if 0.05 > MAF > 0.01Subject Genotyping Rate > 0.95 SNPs common to both SNPs only present onplatforms Illumina 610Deeg PECsQuality QualityHapMapii2s i Control i ControlCEU (60) LD prune (r? < 0.2)LS Rn 287,913 SNPs, 240,517 SNPs,5 5 FZ $$ 1380 subjects, 950 subjects,JPT+CHB (90) BC-TORI0 | o}(-leie} GR=0.99 GR=0.99YRI(60)>Merged Data 348,935 ae Merged Data 2BESOIN) [ees (528,430SNPs and1364 subjects)            16 genetic outlierswere removed fromMerged Data 2    PrincipalComponentPINS

Quality ControlSNP Genotyping Rate per SNP >0.95TNO ne sgRYN etalon i) atl cies OR OR oe ek On OyeHardy-Weinberg Equilibrium p > 0.001Subject Genotyping Rate > 0.95 

 Quality QualityHapMap#23 i Control ControlCEU (60) LD prune (r? < 0.2)———— PAY ACR) Le 240,517 SNPs,A ec Een aacJPT+CHB (90) WEEEJ0RS0 |e} lei GR=0.99 GR=0.99>  Merged Data 3CEES Merged Data 2BESOIN) [ees (528,430 SNPs and1364 subjects)        16 genetic outlierswere removed fromMerged Data 2PrincipalComponentPINS

Fig. 2. Genome-wide association studies (GWAS) data quality control (QC) and data merging strategy. Flow diagram summarizing the processes undertaken for data preparation and QC prior to subsequent analyses. Each GWAS dataset is represented by numbered squares (top right corner). The data were then merged together under PLINK “Consensus call” mode (Merged Data 1). These data were split into 2 groups (Dataset “A” and “B”) according to genotyping rate (single nucleotide polymorphisms [SNPs] which had | > 95% genotyping rate for all samples [both chips] and the rest of the SNPs with genotyping rate eee2 > 6 95% for samples typed on the Illumina 610 chip). Both of these groups were subject to QC separately (see Quality Control panel at top left corner). The 2 datasets were then merged (Merged Data 2). Dataset “A” (which contained SNPs common to both platforms) was linkage disequilibrium (LD) pruned and merged with HapMap data (European [CEU], Asian [CHB/JPT], and Yoruba [YRI]) to 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 Uni- versity (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 which overlapped between GWAS datasets were re- moved. Each sample was checked individually for dis- crepancies between AAD and age at sampling (AAS). Samples with age at sampling greater than AAD were

polymorphisms (SNPs) present on the Illumina 300 chip, and enabled us to merge all the datasets.

AAD information were extracted from the Mayo dataset (Car- rasquillo 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 encom- passed centers in Nottingham, Bristol, Manchester, Bel-

CentreLondonOxfordNIMHWashUNottinghamMayoManchesterBristolBelfast Age at death (yrs)

removed from further analysis. Data merging was per- formed 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 2 GWAS datasets (“A” and “B”) using the “--geno 0.05” command in PLINK.

For both of the GWAS datasets, the following QC pro- cedures 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 short list, SNPs with a genotyping rate less than

(--geno

) were ex- cluded (--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 disequi- librium (LD) pruned subset of

,

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