Genotype data were used to confirm gender and family rela- tionships. Two RS GWAS control samples were excluded because of abnormalities in the sex chromosome (both samples had Triple X Syndrome). Latent clustering of genotypes because of population substructure was assessed by pairwise identity-by- state (IBS) distance using Graphical Relationship Representation (GRR) [http://bioinformatics.well.ox.ac.uk/GRR, (Abecasis et al., 2001)]. LLS GWAS cases showed no relationship errors. From the RS GWAS controls, 59 samples were excluded because of high IBS. In total, 403 LLS GWAS cases and 1670 RS GWAS con- trol samples with a sample call rate >0.95 were analyzed. Because cases and controls originate from different generations, we investigated whether substructure in these cohorts could have influenced the observed associations. IBS estimates for all pairs of subjects in the data set were computed on a randomly selected set of 10% of the SNPs that passed quality control thresholds, using the –genome, –cluster, and –mds-plot 4 com- mands in PLINK [http://pngu.mgh.harvard.edu/purcell/plink, (Purcell et al., 2007)]. The first two resulting principal compo- nents (C1 and C2) were plotted against each other, which gives a representation of the data in two dimensions. In the resulting scatter plot, each point represents an individual (green = LLS GWAS case and blue = RS GWAS control) (Fig. S5). If there had been substructure, one would see multiple clusters in one plot. However, because all our samples seem to be in one cluster, we concluded that there was no substructure to an extent that would affect the observations.

Measurement of serum parameters

All standard serum measurements were performed using fully automated equipment.

Glucose, total cholesterol, high-density lipoprotein choles- terol (HDL-C), and triglyceride levels were measured using the Hitachi Modular P 800 (Roche, Almere, the Netherlands) (Ro- zing et al., 2009). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula (Friedewald et al., 1972).

LDL and HDL particle sizes were measured using proton NMR spectroscopy (LipoScience Inc, Raleigh, NY, USA) (Heijmans et al., 2006).

Insulin-like growth factor-1 (IGF-1), insulin-like growth factor- binding protein 3 (IGFBP3), and insulin levels were measured using the Immulite 2500 (DPC, Los Angeles, CA, USA) (Rozing et al., 2009).

Free triiodothyronine (fT3) was measured using the Modular E170, and hsCRP was measured using Cobas Integra 800 (both from Roche) (Rozing et al., 2010b).

The level of ApoE was determined in serum samples using a human ApoE-specific sandwich ELISA (van Vlijmen et al., 1994; Mooijaart et al., 2006).

Statistical analysis

Replication studies

For the RS replication study, we used the existing GWAS data in the Rotterdam Study after the quality control screening described by Teichert et al. (Teichert et al., 2009). For the Leiden 85-plus and Danish replication studies, genotyping was per- formed using the Sequenom MassARRAY iPLEX Gold and Taq- Man SNP Genotyping assays. Of the 62 prioritized SNPs, 58 could be designed for replication studies using Sequenom, of which 56 were successfully genotyped in >95% of the samples displayed in Table 1. The average genotype call rate for SNPs genotyped with Sequenom was 98.40%, and the average con- cordance rate with GWAS data among the LLS GWAS cases was 99.97%. For 2 of the 6 SNPs that could not be genotyped with Sequenom, rs2075650 and rs642990, pre-designed TaqMan SNP genotyping assays (C___3084828_20 and C___2206314_20, respectively) were used for genotyping, fol- lowing the manufacturer’s instructions. The average genotype call rate for the SNPs genotyped with TaqMan was 99.04%, and the average concordance rate with GWAS data among the LLS GWAS cases was 100%.

GWAS and replication studies

For the association analysis of the GWAS data, we applied a Cochran-Armitage trend test (Cochran, 1954; Armitage, 1955). For X-linked SNPs, the genotypes of the men were considered as homozygous genotypes. SNPs with a P-value <1 · 10)4 (n = 62) were selected for replication. Odds ratios were estimated and the corresponding 95% confidence intervals were computed. For meta-analyses, a fixed effect approach was used. Scores and their variances were computed within each study and combined across the four studies to obtain a single meta-statistic. P-values below 5 · 10)8 were considered as genome-wide significant (Pe’er et al., 2008). The between-study variance was calculated to determine heterogeneity across the four studies. All these analysis were performed using Bioconductor R [http://www.bio- conductor.org, (Gentleman et al., 2004)].

The quantile–quantile plot (Fig. S6), constructed using Biocon- ductor R [http://www.bioconductor.org, (Gentleman et al., 2004)], showed that the P-value distribution of stage 1 con-

Serum parameter = b0 + b1*Age + b2*Sex + b3*(Age*Sex) + b4*Group + b5*rs2075650 + b6*rs429358 + b7*rs7412 + b8*(rs429358*rs7412) (Model 3)

formed to a null distribution at all but the extreme tail. The genomic inflation factor (k), which measures over-dispersion of test statistics from association tests indicating population stratifi- cation, was 1.027 and we therefore decided not to adjust for population stratification.

Age was coded in years. Sex was coded as 1 (male) or 2 (female), Group was coded as 0 (partner) or 1 (offspring). Robust standard errors were used to account for sibship relations. STA- TA⁄SE 11.1 (StataCorp LP) was used for these analyses.

Pairwise linkage disequilibrium (LD) between rs2075650 and the APOE e2⁄e3⁄e4 polymorphism determining SNPs rs7412 and rs429358 was calculated in 8946 individuals using the –ld com- mand in PLINK (http://pngu.mgh.harvard.edu/purcell/plink, (Pur- cell et al., 2007)).

Acknowledgments