heterozygosity of the X chromosome and gender recorded in the database.

Replication 1 (ELIX). This is comprised of 253 long lived individuals enrolled from ELIXIR Pharmaceutical (mean age 10163, median 100), and 341 healthy controls from the Illumina database (Illumina II). Controls were selected to match the genetic background of the 253 cases in this set.

Rules for SNP inclusion. SNPs were included in the final data set if all these conditions were satisfied:

CR.

% in each array type (

,

,

,

,

M) in both centenarians and controls of the discovery set, and overall CR.

% in all samples included in discovery and replication sets.

Cluster separation score .

Excess heterozygosity score between

and

Hardy Weinberg equilibrium x

statistics in controls ,

Minor allele frequency difference between any pair of array type ,

Replication 2 (NECS 2). 60 NECS individuals and 2863 healthy controls from the Illumina database (Ilumina III). In this set, no genetic matching was performed. The 60 centenarians include 39 subjects of European ancestry enrolled between June 2009 and September 2010 (age range 100–114, mean age 108) plus 21 centenarians also of European ancestry (age range 101– 115, mean age 107) that were not included in the discovery set during the genetic matching.

SNP genotyping

We analyzed 1 ug of genomic DNA for NECS and ELIXIR samples, using the Illumina 370 CNV chip, v.1, the Human610- Quad v1.0, and the Human 1 M v1.0 (Illumina, San Diego, CA). We used the Beadstudio software for genotype calling using the top-strand rule, so that SNPs alleles are coded using lexicograph- ical order (typically A/G and A/C). The data in the Illumina repository were generated with different SNP arrays (300 and 550) and we selected the SNPs that were in common to all platforms. SNPs with reverse alleles, and monomorphic in some of the arrays were detected by comparing allele frequencies in controls (300 vs 550, 370 vs 550), and in centenarians (370 vs 1 M, 370 vs 610). Table 2 summarizes the arrays used.

Assessment of between arrays bias and batch effects. The 610-Quad is part of the new line of Infinium high density whole-genome genotyping products, and had undergone substantial design changes compared to the Human CNV370, Human 1 M, HumanHap550-Duo and HumanHap300. We used data from 32 samples that had been genotyped with both the Human CNV370 and 610-Quad illumina arrays and that underwent the same QC procedure, to test for systematic bias between the two arrays. 345,219 SNPs were in common between the two arrays but only 294,153 SNPs had CR.0.97 (so at least 31 genotypes were called) in both arrays after reclustering. In this set, 915 SNPs had 2 or more different genotypes, and only 28 SNPs had allele frequencies that differed by more than 0.05. The plot of allele frequencies (Figure S20) suggests that there is no systematic bias between arrays but rather sporadic errors that can be identified by plotting allele frequencies.

Quality Control

clustered using standard Illumina cluster definitions in array- specific batches (all 370 samples together, all 1 M samples together, all 610 samples together). Specifically, we performed sample-based QC checks and produced QC statistics to compute sample call rates (CR). We eliminated all samples with CR,96.5% and remaining samples were reclustered. After re- clustering, we included the ‘‘excluded’’ samples using this new cluster file. If the previously excluded samples had a CR above 93% they were included in the final analysis.

We tested the agreement between allele coding in the other arrays by comparing the allele frequencies. See Figure S21. The plots rule out general bias between arrays and show that SNPs with reversed alleles were removed.

The additional sample of 60 centenarians included 39 subjects that were genotyped in September 2010, using the 610-Quad array. To be able to test for batch effects, we genotyped the 39 samples in a batch of 48 that included two replicated samples, and 7 samples that had been genotyped with the Human 1 M in the original analysis. The agreement between genotype calls in the 7 samples genotyped with the 610-Quad and the Human 1 M ranged between 99.2% and 99.7%.

We also used the genome-wide identity by descent analysis in PLINK [82], to discover unknown relatedness and to estimate error rate using the number of mismatch of replicated samples (2%). With this analysis we discovered one subject enrolled in both the NECS and ELIX studies, whom we removed from the ELIX set. We also removed samples with inconsistent gender between

Table 2. Breakdown of genotyped samples by Illumina SNP array type (columns 3—7), laboratory (column 8), and case/control/ study status (rows).

370 610 1 M 300 550 Lab Centenarians NECS 583 102 176 0 0 BU ELIXIR 209 44 0 0 0 BU Controls NECS 237 4 0 0 0 BU Illumina I 0 0 0 89 584 unknown Illumina II 0 0 0 62 279 unknown Illumina III 0 0 0 574 2289 unknown Coriell NINDs 867 0 0 0 0 CIDR

The columns of the table denote the Illumina array types. The column ‘‘Lab’’ denotes the laboratory that performed the genotyping: BU=Boston University; CIDR=Center for Inherited Disease Research. The row Illumina I denotes the control samples included in the discovery set; Illumina II denotes the control included in the first replication set, and Illumina III denotes the residual samples from the Illumina repository; Coriell NINDs denotes the neurologically normal doi:10.1371/journal.pone.0029848.t002

Single SNP Analysis

Genetic matching of controls