Pairwise measures of linkage disequilibrium (LD) between the analyzed loci were calculated with the Haploview 4.2 [50]. The amount of LD was quantified by Lewontin’s coefficient (D’).

In order to model the effect of the haplotypes on the probability to attain longevity we used the haplo.stats package of R. It implements a haplotype-based association analysis within the generalized linear model (GLM) framework that allows for ambiguous haplotypes. The haplo.score function of this package has been used to obtain the GLM-based score statistics for testing global and individual haplotype effects on the probability to attain longevity [51]. Permutation-based p-values were used to evaluate the significance of the scores obtained (10000 permutations). In this model the effect of the different haplotypes was assessed assuming a dominant model after adjusting for sex and BMI.

By using the MB-MDR approach we did not find any significant interaction effect among the analyzed polymorphisms (data not shown).

Subsequently, using the aforementioned GLM-based algorithm, we evaluated the effect of the UCP haplotypes on the probability to attain longevity. LD analysis showed that among the four SNPs in the UCP2-UCP3 gene region rs660339, rs659366 and rs15763 were in moderate LD (see Fig. 1), while rs1800849 was in LD with rs15763 (D’ = 0.83), but it was virtually unlinked with the others (D’,0.30). Finally, we found a weak LD between rs9472817 and rs10498769 in the UCP4 gene (D’ = 0.47), and between rs2235800 and rs5975178 in the UCP5 gene (D’ =0.44). Based on these LD patterns, and using a D’ cutoff equal to 0.6, haplotypes were reconstructed at the UCP2-UCP3 and UCP3 loci.

Interaction analysis

In order to explore the interaction effects between the analyzed polymorphisms on the probability to be part of the very old age group, the Model-Based Multifactor Dimensionality Reduction (MB-MDR) method was applied [52]. In this model only all possible second order interactions, after adjusting for main effects sex and BMI, were analyzed.

As shown in Table 4, the analysis of the UCP2-UCP3 haplotypes revealed that the CAC haplotype, consisting of the minor allele for rs660339, major allele for rs659366, and minor allele rs15763, was associated with a decreased probability of attaining longevity (P= 0.003), while the TGT haplotype (opposite combination of alleles) acted in a reverse fashion by increasing this probability (p =0.046). However, the strength of these associations was influenced by the allelic status at rs659366 of the relevant haplotype. Indeed, the CGC and TAT haplotypes, when compared to the above, differ only for rs659366 and did not show any effect. For the UCP3 haplotypes involving rs15763 and rs1800849, we found that carriers of the CC haplotype have a decreased probability of achieving longevity (P,0.001). Conversely, as previously reported in the single-locus analysis, the presence of a T allele at either rs15763 or rs1800849 increased such probability, although this positive effect was more pronounced when the T allele at rs1800849 was involved (p =0.044 vs P= 0.006).

All statistical analyses were performed using genetics, RobustSNP, mbmdr and haplo.stats packages of R 2.10.1.

Results

Table 2 reports the socio-demographic characteristics of the sample analyzed according to age group. The observed genotype frequencies (Table S1) were in agreement with those expected at HWE (p.0.01). Table 3 reports the results of the association test for the three different genetic models (dominant, additive and recessive) obtained in the analyzed sample using the RobustSNP algorithm. Table 3 clearly shows that the variability of the UCP polymorphism was significantly associated with the analyzed phenotype. In particular, using the minor allele for each SNP as reference and after adjusting for BMI and sex, the dominant model for the rs660339 (UCP2) and the rs1800849 (UCP3) resulted to be significantly associated with the longevity phenotype (p = 0.001 in both cases), while the recessive model was the most likely for SNPs rs15763 (UCP3), rs9472817 (UCP4) and rs2235800 (UCP5) (p,0.05). However, after adjusting for multiple testing, all the previous associations remained statistically significant, except those for rs2235800 of the UCP5 gene (p= 0.058).

Discussion

Substantial evidence suggests that the ability of UCPs to reduce ROS and regulate energy utilization underpins the ability of UCPs to promote lifespan in various experimental models [27,28,29,30,35]. In the present study we found that variants in UCP2, UCP3, and UCP4 significantly affect an individual’s chances of becoming ultra-nonagenarians. The different localization of the proteins we found associated with longevity allows us to predict the areas where the uncoupling process may play an important role in survival at very old age.

Table 2. Socio-demographic characteristics of the analysed sample according to sex and age group.

Younger group Males Females Total N (%) 155 (41.3) 220 (58.7) 375 (100) Mean age (SD) 72.86 (6.027) 80.64 (9.127) 77.42 (8.858) BMI (SD) 27.06 (4.122) 26.38 (4.845) 26.66 (4.566) Older group Males Females Total N (%) 138 (61.9) 85 (38.1) 223 (100) Mean age (SD) 93.85 (3.771) 97.09 (3.235) 95.09 (3.903) BMI (SD) 24.42 (3.701) 22.50 (3.994) 23.69 (3.918)