Lists of the SNPs in ADARB2 and ADARB1 that reach statistical significance in the NECS and SICS and were attempted to be replicated in the JCS set. The first 7 columns report the details of the SNPs as described in the legend of Table 3. Columns 8–11 report the results for 14 of the 18 SNPs in Table 3 that were genotyped in 470 oldest old and 538 younger controls of the JCS. Only 3 SNPs reach statistical significance in this set (rows 1, 5 and 10) but one of them have opposite effects compared to the NECS and SICS (row 1). We did not attempt to merge the results from different populations because of the substantial differences in allele frequencies. doi:10.1371/journal.pone.0008210.t006

University, Tokyo, Japan (JCS). All data were analyzed anony- mously. Control data from the Illumina iControlDB database were anonymized.

showed that both the NECS and the Illumina controls are comprised of three major clusters that correspond to northwest, northeast and southwest Europeans, but in the Illumina controls sample there were also subjects with different levels of admixture between the three clusters Figure S3. We therefore randomly sampled 1,538 subjects from the three major clusters to create a control set that matched the genetic background of the NECS extreme old sample set as suggested in [49]. We use the same procedure to identify 81 female and 16 male subjects to be added to the set of SICS controls. The random-selection procedure was repeated twice and lead to the same results.

SNP Genotyping

For the NECS and SICS samples, 1 ug of genomic DNA was analyzed on the Illumina 370 CNV chip (Illumina, San Diego, CA) and only samples with at least a 93% call rate were used for the analysis. For the AJCS and JCS, genotyping was originally performed with the Affymetrix 6.0 chip and 5.0 chips, respectively with required call rates of 99% or greater. Affymetrix Birdseed algorithm and Illumina Beadstudio were used for genotype calling. Non overlapping SNPs that were not approximated by SNPs with substantial LD (D9.0.8) were genotyped with Sequenom (AJCS) and BigDye Terminator cycle sequencing kit and an ABI Prism 3730xl DNA analyzer (Applied Biosystems, CA, USA). The sequence data were analyzed with ABI PRISM SeqScape Software version 2.6 (Applied Biosystems).

Genetic Association Analysis (Pooled DNA Samples)

data is described in [27]. Briefly, the method uses Bayesian association tests to score the evidence for allelic associations between centenarians and controls. Prior distributions represent the prior knowledge about the expected number of genes that may be implicated with the trait and therefore correct for multiple comparisons. The analysis also uses linkage disequilibrium (LD) based filters to retain associations that are supported by clusters of

Creation of a Genetically Matched Control Set

A referent cohort sample for the NECS subjects was constructed utilizing genotype data from the Illumina iControlDB database and principal components analysis was used to match cases and controls by genetic background. To reduce chances of stratifica- tion, we identified 2,077 Caucasian referent subjects from the Illumina iControlDB, all genotype with Illumina arrays, with known age at enrollment between 30 and 75 years, and we used the principal component analysis implemented in the program EIGENSTRAT [48] to examine the structure of this referent group compared to the NECS and SICS subjects. The analysis

Genetic Association Analysis (Individual DNA Samples)

The genotype data of the 31 SNPs in the genes ADARB1 and 114 SNPs in ADARB2 were individually analyzed using Bayesian logistic regression [32] to fit dominant and recessive models of inheritance adjusted by gender. The marginal posterior distribu- tions of the ORs were estimated using the implementation of Gibbs sampling in WinBugs 1.4 [50], and the 2.5th and 97.5th

susrevksiR selellatnerefer)slortnoc643,dlotsedlo234(SCPJ2r/9DecnatsiDnoitisopPNSSCIS+SCENnoitisoprhCPNSwoR )A(pgisseyaBRO)A(pgisseyaBRO GGvGC/CC49.0/19.02240.0)70.1;43.0(16.03.0/36.0232502364319904082sr22.0/72.03000.0)95.1;51.1(43.18801431017417001sr2 TT/TCvCC60.0/90.02330.0)50.3;79.0(07.185.0/00.14222683163162430901sr01.0/21.01120.0)45.1;10.1(42.1016163101949488sr4 GG/GCvCC50.0/80.04320.0)85.3;10.1(48.122.0/19.06468274604125612271sr31.0/51.03430.0)84.1;89.0(22.16287931013567832sr6 GGvGC/CC69.0/19.01300.0)08.0;32.0(44.002.0/88.0627285135414093454sr86.0/47.07100.0)86.1;11.1(73.12340541015355082sr7 TTvTA/AA34.0/05.02020.0)28.1;30.1(63.130.0/80.057642541974549128156sr10.0/500.09030.0)40.1;81.0(84.007445454126188382sr81 TTvTC/CC39.0/98.09500.0)68.0;03.0(45.030.0/80.034352318974544288382sr .stesstcejbusSCISdnaSCENehtnisnoitaicossaetaroborrocdnatesSCJehtnidepytonegerewtahtsPNSlanoitiddA

percentile were used to estimate 95% credible intervals (CI) for the ORs. The 50th percentile was used to estimate the OR, and the frequency of OR.1 was used to estimate the posterior probability p(OR.1). The Gibbs sampler was run for at least 10,000 iterations and the last 10,000 simulated values were used to estimate these parameters. We used as prior distributions on the regression coefficients of the logit function normal distributions with mean 0 and standard deviation 3.2 that determine a normal prior distribution of the log(OR) with mean 0 (no association) and a variance that ranges between 10 with no genetic effect to 40 with a gene6gender effect. This set of prior distributions was determined to make the analysis robust to rare alleles (frequen- cy,0.10) and we searched for the largest variance that allowed successful execution of the Gibbs sampler. These prior distribu- tions bias the analysis toward the null hypothesis and reduce false positive associations.