Figure 2. Figure 2 shows the selected polymorphisms in the chromosome 7 region. Symbols are as in Figure 1. doi:10.1371/journal.pone.0045232.g002

variability in the three regions of interest. All polymorphisms with minor allele frequency (MAF) $5% in Caucasians from the International HapMap Project (version 28, August 2010; http:// www.hapmap.org) were included. Tagging SNPs were selected with the use of the Tagger program within Haploview (http:// www.broad.mit.edu/mpg/haploview/; http://www.broad.mit. edu/mpg/tagger/) [69,70], using pairwise tagging with a mini- mum r2 of 0.8. On chromosome 5 we considered a 1440 base-pair region (9628089–9629529) extending from rs41467 to rs2234233 in the TAS2R1 gene. For genes situated on chromosome 7 and 12 we selected tagging polymorphisms. For the TAS2R38 gene we selected rs713598, rs1726866 and rs10246939 as tagging SNPs since they are all non-synonymous and functional [51]. The final selection included 41 SNPs belonging to 20 genes. [51]. Figures 1, 2 and 3 show the selected polymorphisms in the chromosome 5, 7 and 12 regions respectively, as well as the distance and the LD between each SNP within the same chromosome. Table S1 shows the genes and SNPs selected in the study, the Hardy-Weinberg equilibrium (HWE) values observed for each SNP in the study, their position in the genome, in the gene, and the amino acidic change specified.

Kaspar (Kbioscience, Heddesdon, UK) or Taqman (Applied Biosystems, Foster City, CA, USA) assays. PCR plates were read on an ABI PRISM 7900HT instrument (Applied Biosystems).

Haplotype Reconstruction

this study using SNPtool (http://www.dkfz.de/de/ molgen_epidemiology/tools/SNPtool.html) [71] and the Haplo- view v4.2 software using a MAF of 0.05, an HWE p-value of 0.001 and a call rate of 75% as cut-off values. Individual haplotypes were then statistically inferred using the PHASE v.2.1.1 algorithm, based on a Bayesian approach (http://www.stat.washington.edu/ stephens/) [72].

Statistical Analysis

The frequency distribution of genotypes was examined for the cases and the controls. HWE was tested for each of the SNP by chi-square test. logistic regression for multivariate analyses to assess the main effects of the genetic polymorphism on longevity was used. In these models the genetic data was coded using a co- dominant and a dominant inheritance model using the most common genotype in the controls as the reference category. All analyses were adjusted for gender. For haplotype analysis unconditional logistic regression was used to estimate the ‘‘risk of longevity’’. The most frequent haplotype was set as reference, while haplotypes with a frequency below 1% were declared as rare and combined in a single category. In order to take into account the large number of tests performed in this study, we calculated for each gene the number of effective independent variables, M , by eff

DNA Extraction and Genotyping

DNA was extracted from blood samples with standard proteinase K digestion followed by phenol/chloroform extraction and ethanol precipitation. The order of DNAs of cases (age$85) and controls (age,85) was randomized on PCR plates in order to ensure that an equal number of cases and controls could be analyzed simultaneously. All genotyping was carried out using

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Figure 3. Figure 3 shows the selected polymorphisms in the chromosome 12 region. Symbols are as in Figure 1. doi:10.1371/journal.pone.0045232.g003

(higher or lower) using cross-validation and permutation testing. Detailed information is described elsewhere [74].

use of the SNP Spectral Decomposition approach [73]. We obtained a region-wide M value for each chromosomal region eff and also a study-wide M value, by adding up region M ’s.

All analysis were performed using STATGRAPHICSH Centu- rion XVI software ((cid:2) 2009 by StatPoint Technologies, Inc. www. STATGRAPHICS.com) and STATA software (StataCorp, Col- lege Station, TX).

Results

Genotyping Success Rates and Quality Control

The genotype distributions at all loci were in HWE with non- significant chi square values (p.0.05) (table S1). Random duplicate samples (8%) were also included and concordance of their genotypes was greater than 99%. The average call rate for the SNPs was 97.% (range 90%–100%).

Gene-gene Interactions

SNP-SNP interactions were tested using nonparametric Multi- factor Dimensionality Reduction (MDR, http://www.epistasis. org.) and verified with logistic regression. MDR is a data reduction approach for detecting interactions of multilocus genotypes and discrete environmental factors that are predictive of a discrete outcome. MDR defines a single variable that incorporates information from several loci and/or environmental factors and evaluates its ability to classify and predict outcome risk status

Main Effects of Genotyped SNPs

The distribution of the genotypes and their odds ratios (ORs) for association with longevity are shown in Table S2. We found that there were five significant associations between the SNPs in the chromosome 7 cluster and longevity at the conventional level of

Table 1. SNPs in chromosome 7 cluster associated with longevity.